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Add CI for docs
simplify/uniformize eigen_gen_docs
2026-04-10 11:34:33 +08:00 · 2025-10-17 21:34:55 -07:00 · 2025-10-16 22:01:28 -07:00 · 2013-10-18 13:19:14 +02:00 · 2013-10-11 20:43:29 +02:00 · 2013-10-11 20:42:41 +02:00
2184 changed files with 136857 additions and 353205 deletions
--- a/.clang-format
+++ b/.clang-format
@@ -1,19 +0,0 @@
 ---
 BasedOnStyle: Google
 ColumnLimit:  120
 ---
 Language:     Cpp
 BasedOnStyle: Google
 ColumnLimit:  120
 StatementMacros:
  - EIGEN_STATIC_ASSERT
  - EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
  - EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
 SortIncludes: false
 AttributeMacros:
 - EIGEN_STRONG_INLINE
 - EIGEN_ALWAYS_INLINE
 - EIGEN_DEVICE_FUNC
 - EIGEN_DONT_INLINE
 - EIGEN_DEPRECATED
 - EIGEN_UNUSED
--- a/.clang-tidy
+++ b/.clang-tidy
@@ -1,37 +0,0 @@
 ---
 # Conservative clang-tidy configuration for Eigen.
 #
 # Focuses on bug-finding checks with low false-positive rates.
 # Intentionally omits style-enforcement checks (modernize-*, google-*,
 # cppcoreguidelines-*) since Eigen has its own conventions and is a
 # heavily-templated math library where many "modern C++" idioms don't apply.
 Checks: >
  -*,
  bugprone-*,
  -bugprone-narrowing-conversions,
  -bugprone-easily-swappable-parameters,
  -bugprone-implicit-widening-of-multiplication-result,
  -bugprone-exception-escape,
  misc-redundant-expression,
  misc-unused-using-decls,
  misc-misleading-identifier,
  performance-for-range-copy,
  performance-implicit-conversion-in-loop,
  performance-unnecessary-copy-initialization,
  performance-unnecessary-value-param,
  readability-container-size-empty,
  readability-duplicate-include,
  readability-misleading-indentation,
  readability-redundant-control-flow,
  readability-redundant-smartptr-get,
 WarningsAsErrors: ''
 HeaderFilterRegex: 'Eigen/.*|test/.*|blas/.*|lapack/.*|unsupported/Eigen/.*'
 # Eigen uses its own assert macros.
 CheckOptions:
  - key: bugprone-assert-side-effect.AssertMacros
    value: 'eigen_assert,eigen_internal_assert,EIGEN_STATIC_ASSERT,VERIFY,VERIFY_IS_APPROX,VERIFY_IS_EQUAL,VERIFY_IS_MUCH_SMALLER_THAN,VERIFY_IS_NOT_APPROX,VERIFY_IS_NOT_EQUAL,VERIFY_IS_UNITARY,VERIFY_RAISES_ASSERT'
 ...
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -1,4 +0,0 @@
 # First major clang-format MR (https://gitlab.com/libeigen/eigen/-/merge_requests/1429).
 f38e16c193d489c278c189bc06b448a94adb45fb
 # Formatting of tests, examples, benchmarks, et cetera (https://gitlab.com/libeigen/eigen/-/merge_requests/1432).
 46e9cdb7fea25d7f7aef4332b9c3ead3857e213d
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,3 +0,0 @@
 *.sh                         eol=lf
 debug/msvc/*.dat             eol=crlf
 debug/msvc/*.natvis          eol=crlf
--- a/.gitignore
+++ b/.gitignore
@@ -39,4 +39,3 @@ Makefile
 !scripts/buildtests.in
 !Eigen/Core
 !Eigen/src/Core
 CLAUDE.md
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -7,27 +7,8 @@
 # Public License v. 2.0. If a copy of the MPL was not distributed
 # with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 default:
  interruptible: true
 # For MR pipelines, auto-cancel running jobs when new commits are pushed.
 # For scheduled (nightly) pipelines, never auto-cancel so all jobs run to
 # completion and all failures are visible for debugging.
 workflow:
  auto_cancel:
    on_new_commit: interruptible
    on_job_failure: none
  rules:
    - if: $CI_PIPELINE_SOURCE == "schedule"
      auto_cancel:
        on_new_commit: none
    - when: always
 stages:
  - checkformat
  - build
  - test
  - benchmark
  - deploy
 variables:
@@ -42,11 +23,6 @@ variables:
  EIGEN_CI_CTEST_ARGS: ""
 include:
  - "/ci/checkformat.gitlab-ci.yml"
  - "/ci/common.gitlab-ci.yml"
  - "/ci/build.linux.gitlab-ci.yml"
  - "/ci/build.windows.gitlab-ci.yml"
  - "/ci/test.linux.gitlab-ci.yml"
  - "/ci/test.windows.gitlab-ci.yml"
  - "/ci/benchmark.gitlab-ci.yml"
  - "/ci/deploy.gitlab-ci.yml"
--- a/.gitlab/issue_templates/Bug
+++ b/.gitlab/issue_templates/Bug
@@ -1,59 +0,0 @@
 <!--
 Thank you for submitting an issue!
 Before opening a new issue, please search for keywords in the existing [list of issues](https://gitlab.com/libeigen/eigen/-/issues?state=opened) to verify it isn't a duplicate.
 -->
 ### Summary
 <!-- Summarize the bug encountered concisely. -->
 ### Environment
 <!-- Please provide your development environment. -->
 - **Operating System** : Windows/Linux
 - **Architecture** : x64/Arm64/PowerPC ...
 - **Eigen Version** : 5.0.0
 - **Compiler Version** : gcc-12.0
 - **Compile Flags** : -O3 -march=native
 - **Vector Extension** : SSE/AVX/NEON ...
 ### Minimal Example
 <!--
 Please create a minimal reproducing example here that exhibits the problematic behavior.
 The example should be complete, in that it can fully build and run.  See the [the guidelines on stackoverflow](https://stackoverflow.com/help/minimal-reproducible-example) for how to create a good minimal example.
 You can also link to [godbolt](https://godbolt.org). Note that you need to click 
 the "Share" button in the top right-hand corner of the godbolt page to get the share link
 instead of the URL in your browser address bar. 
 -->
 ```cpp
 // Insert  your code here.
 ```
 ### Steps to reproduce the issue
 <!-- Describe the necessary steps to reproduce the issue. -->
 1. first step
 2. second step
 3. ... 
 ### What is the current *bug* behavior?
 <!-- Describe what actually happens. -->
 ### What is the expected *correct* behavior?
 <!-- Describe what you should see instead. -->
 ### Relevant logs
 <!-- Add relevant build logs or program output within blocks marked by " ``` " -->
 ### [Optional] Benchmark scripts and results
 <!-- Please share any benchmark scripts - either standalone, or using [Google Benchmark](https://github.com/google/benchmark). -->
 ### Anything else that might help
 <!--
 It will be better to provide us more information to help narrow down the cause. 
 Including but not limited to the following: 
 - lines of code that might help us diagnose the problem. 
 - potential ways to address the issue.
 - last known working/first broken version (release number or commit hash).
 --> 
--- a/.gitlab/issue_templates/Feature
+++ b/.gitlab/issue_templates/Feature
@@ -1,14 +0,0 @@
 <!--
 Thank you for submitting a Feature Request!
 If you want to run ideas by the maintainers and the Eigen community first, 
 you can chat about them on the [Eigen Discord server](https://discord.gg/2SkEJGqZjR).
 -->
 ### Describe the feature you would like to be implemented.
 ### Why Would such a feature be useful for other users?
 ### Any hints on how to implement the requested feature?
 ### Additional resources
--- a/.gitlab/merge_request_templates/Default.md
+++ b/.gitlab/merge_request_templates/Default.md
@@ -1,30 +0,0 @@
 <!--
 Thanks for contributing a merge request!
 We recommend that first-time contributors read our [contribution guidelines](https://eigen.tuxfamily.org/index.php?title=Contributing_to_Eigen).
 Before submitting the MR, please complete the following checks:
 - Create one PR per feature or bugfix,
 - Run the test suite to verify your changes.
  See our [test guidelines](https://eigen.tuxfamily.org/index.php?title=Tests).
 - Add tests to cover the bug addressed or any new feature.
 - Document new features.  If it is a substantial change, add it to the [Changelog](https://gitlab.com/libeigen/eigen/-/blob/master/CHANGELOG.md).
 - Leave the following box checked when submitting: `Allow commits from members who can merge to the target branch`.
  This allows us to rebase and merge your change.
 Note that we are a team of volunteers; we appreciate your patience during the review process.
 -->
 ### Description
 <!--Please explain your changes.-->
 %{first_multiline_commit}
 ### Reference issue
 <!--
 You can link to a specific issue using the gitlab syntax #<issue number>.
 If the MR fixes an issue, write "Fixes #<issue number>" to have the issue automatically closed on merge.
 -->
 ### Additional information
 <!--Any additional information you think is important.-->
--- a/.hgeol
+++ b/.hgeol
@@ -0,0 +1,3 @@
 [patterns]
 **.* = native
 eigen_autoexp_part.dat = CRLF
--- a/.hgignore
+++ b/.hgignore
@@ -0,0 +1,32 @@
 syntax: glob
 qrc_*cxx
 *.orig
 *.pyc
 *.diff
 diff
 *.save
 save
 *.old
 *.gmo
 *.qm
 core
 core.*
 *.bak
 *~
 build*
 *.moc.*
 *.moc
 ui_*
 CMakeCache.txt
 tags
 .*.swp
 activity.png
 *.out
 *.php*
 *.log
 *.orig
 *.rej
 log
 patch
 a
 a.*
--- a/.krazy
+++ b/.krazy
@@ -0,0 +1,3 @@
 SKIP /disabled/
 SKIP /bench/
 SKIP /build/
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,335 +1,85 @@
-cmake_minimum_required(VERSION 3.10.0)
+project(Eigen)
-#==============================================================================
+cmake_minimum_required(VERSION 2.6.2)
 # CMake Policy issues.
 #==============================================================================
 # Allow overriding options in a parent project via `set` before including Eigen.
 if (POLICY CMP0077)
  cmake_policy (SET CMP0077 NEW)
 endif (POLICY CMP0077)
-# NOTE Remove setting the policy once the minimum required CMake version is
+# guard against in-source builds
 # increased to at least 3.15. Retain enabling the export to package registry.
 if (POLICY CMP0090)
  # The export command does not populate package registry by default
  cmake_policy (SET CMP0090 NEW)
  # Unless otherwise specified, always export to package registry to ensure
  # backwards compatibility.
  if (NOT DEFINED CMAKE_EXPORT_PACKAGE_REGISTRY)
    set (CMAKE_EXPORT_PACKAGE_REGISTRY ON)
  endif (NOT DEFINED CMAKE_EXPORT_PACKAGE_REGISTRY)
 endif (POLICY CMP0090)
 # Disable warning about find_package(CUDA).
 # CUDA language support is lacking for clang as the CUDA compiler
 # until at least cmake version 3.18.  Even then, there seems to be
 # issues on Windows+Ninja in passing build flags.  Continue using
 # the "old" way for now.
 if (POLICY CMP0146)
  cmake_policy(SET CMP0146 OLD)
 endif ()
 # Normalize DESTINATION paths
 if (POLICY CMP0177)
  cmake_policy(SET CMP0177 NEW)
 endif ()
 # Respect <PackageName>_ROOT variables.
 if (POLICY CMP0074)
  cmake_policy(SET CMP0074 NEW)
 endif ()
 #==============================================================================
 # CMake Project.
 #==============================================================================
 project(Eigen3)
 # Remove this block after bumping CMake to v3.21.0
 # PROJECT_IS_TOP_LEVEL is defined then by default
 if(CMAKE_VERSION VERSION_LESS 3.21.0)
  if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
    set(PROJECT_IS_TOP_LEVEL ON)
  else()
    set(PROJECT_IS_TOP_LEVEL OFF)
  endif()
 endif()
 #==============================================================================
 # Build ON/OFF Settings.
 #==============================================================================
 # Determine if we should build tests.
 include(CMakeDependentOption)
 cmake_dependent_option(BUILD_TESTING "Enable creation of tests." ON "PROJECT_IS_TOP_LEVEL" OFF)
 option(EIGEN_BUILD_TESTING "Enable creation of Eigen tests." ${BUILD_TESTING})
 option(EIGEN_LEAVE_TEST_IN_ALL_TARGET "Leaves tests in the all target, needed by ctest for automatic building." OFF)
 # Determine if we should build BLAS/LAPACK implementations.
 option(EIGEN_BUILD_BLAS "Toggles the building of the Eigen Blas library" ${PROJECT_IS_TOP_LEVEL})
 option(EIGEN_BUILD_LAPACK "Toggles the building of the included Eigen LAPACK library" ${PROJECT_IS_TOP_LEVEL})
 if (EIGEN_BUILD_BLAS OR EIGEN_BUILD_LAPACK)
  # Determine if we should build shared libraries for BLAS/LAPACK on this platform.
  if (NOT EIGEN_BUILD_SHARED_LIBS)
    get_cmake_property(EIGEN_BUILD_SHARED_LIBS TARGET_SUPPORTS_SHARED_LIBS)
  endif()
 endif()
 # Avoid building docs if included from another project.
 # Building documentation requires creating and running executables on the host
 # platform.  We shouldn't do this if cross-compiling.
 if (PROJECT_IS_TOP_LEVEL AND NOT CMAKE_CROSSCOMPILING)
  set(EIGEN_BUILD_DOC_DEFAULT ON)
 endif()
 option(EIGEN_BUILD_DOC "Enable creation of Eigen documentation" ${EIGEN_BUILD_DOC_DEFAULT})
 option(EIGEN_BUILD_DEMOS "Toggles the building of the Eigen demos" ${PROJECT_IS_TOP_LEVEL})
 # Disable pkgconfig only for native Windows builds
 if(NOT WIN32 OR NOT CMAKE_HOST_SYSTEM_NAME MATCHES Windows)
  option(EIGEN_BUILD_PKGCONFIG "Build pkg-config .pc file for Eigen" ${PROJECT_IS_TOP_LEVEL})
 endif()
 option(EIGEN_BUILD_CMAKE_PACKAGE "Enables the creation of EigenConfig.cmake and related files" ${PROJECT_IS_TOP_LEVEL})
 if (EIGEN_BUILD_TESTING OR EIGEN_BUILD_BLAS OR EIGEN_BUILD_LAPACK OR EIGEN_BUILD_DOC OR EIGEN_BUILD_DEMOS)
  set(EIGEN_IS_BUILDING_ ON)
 endif()
 #==============================================================================
 # Version Info.
 #==============================================================================
 # If version information is not provided, automatically parse the version number
 # from header files.
 file(READ "${PROJECT_SOURCE_DIR}/Eigen/Version" _eigen_version_header)
 if (NOT DEFINED EIGEN_WORLD_VERSION)
  string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen_world_version_match "${_eigen_version_header}")
  set(EIGEN_WORLD_VERSION "${CMAKE_MATCH_1}" CACHE STRING "")
 endif()
 if (NOT DEFINED EIGEN_MAJOR_VERSION)
  string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen_major_version_match "${_eigen_version_header}")
  set(EIGEN_MAJOR_VERSION "${CMAKE_MATCH_1}" CACHE STRING "")
 endif()
 if (NOT DEFINED EIGEN_MINOR_VERSION)
  string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen_minor_version_match "${_eigen_version_header}")
  set(EIGEN_MINOR_VERSION "${CMAKE_MATCH_1}" CACHE STRING "")
 endif()
 if (NOT DEFINED EIGEN_PATCH_VERSION)
  string(REGEX MATCH "define[ \t]+EIGEN_PATCH_VERSION[ \t]+([0-9]+)" _eigen_patch_version_match "${_eigen_version_header}")
  set(EIGEN_PATCH_VERSION "${CMAKE_MATCH_1}" CACHE STRING "")
 endif()
 if (NOT DEFINED EIGEN_PRERELEASE_VERSION)
  set(EIGEN_PRERELEASE_VERSION "dev")
 endif()
 # If we are in a git repo, extract a changeset.
 if(IS_DIRECTORY ${CMAKE_SOURCE_DIR}/.git)
  # if the git program is absent or this will leave the EIGEN_GIT_REVNUM string empty,
  # but won't stop CMake.
  execute_process(COMMAND git ls-remote -q ${CMAKE_SOURCE_DIR} HEAD OUTPUT_VARIABLE EIGEN_GIT_OUTPUT)
 endif()
 # extract the git rev number from the git output...
 if(EIGEN_GIT_OUTPUT)
  string(REGEX MATCH "^([0-9;a-f]+).*" EIGEN_GIT_CHANGESET_MATCH "${EIGEN_GIT_OUTPUT}")
  set(EIGEN_GIT_REVNUM "${CMAKE_MATCH_1}")
 endif()
 if (NOT DEFINED EIGEN_BUILD_VERSION AND DEFINED EIGEN_GIT_REVNUM)
  string(SUBSTRING "${EIGEN_GIT_REVNUM}" 0 8 EIGEN_BUILD_VERSION)
 else()
  set(EIGEN_BUILD_VERSION "" CACHE STRING "")
 endif()
 # The EIGEN_VERSION_NUMBER must be of the form <major.minor.patch>.
 # The EIGEN_VERSION_STRING can contain the preprelease/build strings.
 set(EIGEN_VERSION_NUMBER "${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION}.${EIGEN_PATCH_VERSION}" CACHE STRING "")
 set(EIGEN_VERSION_STRING "${EIGEN_VERSION_NUMBER}" CACHE STRING "")
 if (NOT "x${EIGEN_PRERELEASE_VERSION}" STREQUAL "x")
  set(EIGEN_VERSION_STRING "${EIGEN_VERSION_STRING}-${EIGEN_PRERELEASE_VERSION}" CACHE STRING "")
 endif()
 if (NOT "x${EIGEN_BUILD_VERSION}" STREQUAL "x")
  set(EIGEN_VERSION_STRING "${EIGEN_VERSION_STRING}+${EIGEN_BUILD_VERSION}" CACHE STRING "")
 endif()
 # Generate version file.
 configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/Version.in"
               "${CMAKE_CURRENT_BINARY_DIR}/include/Eigen/Version")
 #==============================================================================
 # Install Path Configuration.
 #==============================================================================
 # Unconditionally allow install of targets to support nested dependency
 # installations.
 #
 # Note: projects that depend on Eigen should _probably_ exclude installing
 # Eigen by default (e.g. by using EXCLUDE_FROM_ALL when using
 # FetchContent_Declare or add_subdirectory) to avoid overwriting a previous
 # installation.
 include(GNUInstallDirs)
 # Backward compatibility support for EIGEN_INCLUDE_INSTALL_DIR
 if(EIGEN_INCLUDE_INSTALL_DIR)
  message(WARNING "EIGEN_INCLUDE_INSTALL_DIR is deprecated. Use INCLUDE_INSTALL_DIR instead.")
 endif()
 if(EIGEN_INCLUDE_INSTALL_DIR AND NOT INCLUDE_INSTALL_DIR)
  set(INCLUDE_INSTALL_DIR ${EIGEN_INCLUDE_INSTALL_DIR}
      CACHE PATH "The directory relative to CMAKE_INSTALL_PREFIX where Eigen header files are installed")
 else()
  set(INCLUDE_INSTALL_DIR
      "${CMAKE_INSTALL_INCLUDEDIR}/eigen3"
      CACHE PATH "The directory relative to CMAKE_INSTALL_PREFIX where Eigen header files are installed"
      )
 endif()
 set(CMAKEPACKAGE_INSTALL_DIR
    "${CMAKE_INSTALL_DATADIR}/eigen3/cmake"
    CACHE PATH "The directory relative to CMAKE_INSTALL_PREFIX where Eigen3Config.cmake is installed"
    )
 set(PKGCONFIG_INSTALL_DIR
    "${CMAKE_INSTALL_DATADIR}/pkgconfig"
    CACHE PATH "The directory relative to CMAKE_INSTALL_PREFIX where eigen3.pc is installed"
    )
 foreach(var INCLUDE_INSTALL_DIR CMAKEPACKAGE_INSTALL_DIR PKGCONFIG_INSTALL_DIR)
  # If an absolute path is specified, make it relative to "{CMAKE_INSTALL_PREFIX}".
  if(IS_ABSOLUTE "${${var}}")
    file(RELATIVE_PATH "${var}" "${CMAKE_INSTALL_PREFIX}" "${${var}}")
  endif()
 endforeach()
 #==============================================================================
 # Eigen Library.
 #==============================================================================
 # Alias Eigen_*_DIR to Eigen3_*_DIR:
 set(Eigen_SOURCE_DIR ${Eigen3_SOURCE_DIR})
 set(Eigen_BINARY_DIR ${Eigen3_BINARY_DIR})
 # Imported target support
 add_library (eigen INTERFACE)
 add_library (Eigen3::Eigen ALIAS eigen)
 target_include_directories (eigen INTERFACE
  $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>
  $<INSTALL_INTERFACE:${INCLUDE_INSTALL_DIR}>
 )
 # Eigen requires at least C++14
 target_compile_features (eigen INTERFACE cxx_std_14)
 # Export as title case Eigen
 set_target_properties (eigen PROPERTIES EXPORT_NAME Eigen)
 #==============================================================================
 # Install Rule Configuration.
 #==============================================================================
 install(FILES
  signature_of_eigen3_matrix_library
  DESTINATION ${INCLUDE_INSTALL_DIR} COMPONENT Devel
  )
 if(EIGEN_BUILD_PKGCONFIG)
    configure_file(eigen3.pc.in eigen3.pc @ONLY)
    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
        DESTINATION ${PKGCONFIG_INSTALL_DIR})
 endif()
 install(DIRECTORY Eigen DESTINATION ${INCLUDE_INSTALL_DIR} COMPONENT Devel)
 # Replace the "Version" header file with the generated one.
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/include/Eigen/Version
    DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/ COMPONENT Devel)
 install(TARGETS eigen EXPORT Eigen3Targets)
 if(EIGEN_BUILD_CMAKE_PACKAGE)
  include (CMakePackageConfigHelpers)
  configure_package_config_file (
    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3Config.cmake.in
    ${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
    INSTALL_DESTINATION ${CMAKEPACKAGE_INSTALL_DIR}
    NO_SET_AND_CHECK_MACRO # Eigen does not provide legacy style defines
    NO_CHECK_REQUIRED_COMPONENTS_MACRO # Eigen does not provide components
  )
  set(CVF_VERSION "${EIGEN_VERSION_NUMBER}")
  configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/Eigen3ConfigVersion.cmake.in"
                 "Eigen3ConfigVersion.cmake"
                 @ONLY)
  # The Eigen target will be located in the Eigen3 namespace. Other CMake
  # targets can refer to it using Eigen3::Eigen.
  export (TARGETS eigen NAMESPACE Eigen3:: FILE Eigen3Targets.cmake)
  # Export Eigen3 package to CMake registry such that it can be easily found by
  # CMake even if it has not been installed to a standard directory.
  export (PACKAGE Eigen3)
  install (EXPORT Eigen3Targets NAMESPACE Eigen3:: DESTINATION ${CMAKEPACKAGE_INSTALL_DIR})
  install (FILES ${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
                ${CMAKE_CURRENT_BINARY_DIR}/Eigen3ConfigVersion.cmake
          DESTINATION ${CMAKEPACKAGE_INSTALL_DIR})
  # Add uninstall target
  if(NOT TARGET uninstall AND PROJECT_IS_TOP_LEVEL)
    add_custom_target ( uninstall
        COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EigenUninstall.cmake)
  endif()
 endif()
 #==============================================================================
 # General Build Configuration.
 #==============================================================================
 # Avoid setting the standard in a parent if unset.
 if(PROJECT_IS_TOP_LEVEL)
  set(CMAKE_CXX_STANDARD 14 CACHE STRING "Default C++ standard")
  set(CMAKE_CXX_STANDARD_REQUIRED ON CACHE BOOL "Require C++ standard")
  set(CMAKE_CXX_EXTENSIONS OFF CACHE BOOL "Allow C++ extensions")
 endif()
 # Guard against in-source builds
 if(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_BINARY_DIR})
  message(FATAL_ERROR "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there. You may need to remove CMakeCache.txt. ")
 endif()
-# Guard against bad build-type strings
+# guard against bad build-type strings
-if (PROJECT_IS_TOP_LEVEL AND NOT CMAKE_BUILD_TYPE)
+
 if (NOT CMAKE_BUILD_TYPE)
  set(CMAKE_BUILD_TYPE "Release")
 endif()
-# Only try to figure out how to link the math library if we are building something.
+string(TOLOWER "${CMAKE_BUILD_TYPE}" cmake_build_type_tolower)
-# Otherwise, let the parent project deal with dependencies.
+if(    NOT cmake_build_type_tolower STREQUAL "debug"
-if (EIGEN_IS_BUILDING_)
+   AND NOT cmake_build_type_tolower STREQUAL "release"
-  # Use Eigen's cmake files.
+   AND NOT cmake_build_type_tolower STREQUAL "relwithdebinfo")
  message(FATAL_ERROR "Unknown build type \"${CMAKE_BUILD_TYPE}\". Allowed values are Debug, Release, RelWithDebInfo (case-insensitive).")
 endif()
 #############################################################################
 # retrieve version infomation                                               #
 #############################################################################
 # automatically parse the version number
 file(READ "${PROJECT_SOURCE_DIR}/Eigen/src/Core/util/Macros.h" _eigen_version_header)
 string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen_world_version_match "${_eigen_version_header}")
 set(EIGEN_WORLD_VERSION "${CMAKE_MATCH_1}")
 string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen_major_version_match "${_eigen_version_header}")
 set(EIGEN_MAJOR_VERSION "${CMAKE_MATCH_1}")
 string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen_minor_version_match "${_eigen_version_header}")
 set(EIGEN_MINOR_VERSION "${CMAKE_MATCH_1}")
 set(EIGEN_VERSION_NUMBER ${EIGEN_WORLD_VERSION}.${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION})
 # if the mercurial program is absent, this will leave the EIGEN_HG_CHANGESET string empty,
 # but won't stop CMake.
 execute_process(COMMAND hg tip -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_HGTIP_OUTPUT)
 execute_process(COMMAND hg branch -R ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE EIGEN_BRANCH_OUTPUT)
 # if this is the default (aka development) branch, extract the mercurial changeset number from the hg tip output...
 if(EIGEN_BRANCH_OUTPUT MATCHES "default")
 string(REGEX MATCH "^changeset: *[0-9]*:([0-9;a-f]+).*" EIGEN_HG_CHANGESET_MATCH "${EIGEN_HGTIP_OUTPUT}")
 set(EIGEN_HG_CHANGESET "${CMAKE_MATCH_1}")
 endif(EIGEN_BRANCH_OUTPUT MATCHES "default")
 #...and show it next to the version number
 if(EIGEN_HG_CHANGESET)
  set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER} (mercurial changeset ${EIGEN_HG_CHANGESET})")
 else(EIGEN_HG_CHANGESET)
  set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER}")
 endif(EIGEN_HG_CHANGESET)
 include(CheckCXXCompilerFlag)
 set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
-  set(CMAKE_INCLUDE_CURRENT_DIR OFF)
+#############################################################################
 # find how to link to the standard libraries                                #
 #############################################################################
 find_package(StandardMathLibrary)
-  find_package(AOCL QUIET)
+
 set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "")
  if(AOCL_FOUND)
    list(APPEND EIGEN_STANDARD_LIBRARIES_TO_LINK_TO ${AOCL_LIBRARIES})
    if(AOCL_INCLUDE_DIRS)
      include_directories(${AOCL_INCLUDE_DIRS})
    endif()
  endif()
 if(NOT STANDARD_MATH_LIBRARY_FOUND)
  message(FATAL_ERROR
    "Can't link to the standard math library. Please report to the Eigen developers, telling them about your platform.")
 else()
  if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
    set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO} ${STANDARD_MATH_LIBRARY}")
  else()
    set(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO "${STANDARD_MATH_LIBRARY}")
  endif()
  # Clean up any leading/trailing whitespace in the variable to avoid CMP0004 errors
  string(STRIP "${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO}" EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
 endif()
 endif()
 if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
  message(STATUS "Standard libraries to link to explicitly: ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO}")
@@ -337,112 +87,38 @@ if (EIGEN_IS_BUILDING_)
  message(STATUS "Standard libraries to link to explicitly: none")
 endif()
-  # Default tests/examples/libraries to row-major.
+option(EIGEN_BUILD_BTL "Build benchmark suite" OFF)
 if(NOT WIN32)
  option(EIGEN_BUILD_PKGCONFIG "Build pkg-config .pc file for Eigen" ON)
 endif(NOT WIN32)
 set(CMAKE_INCLUDE_CURRENT_DIR ON)
 option(EIGEN_SPLIT_LARGE_TESTS "Split large tests into smaller executables" ON)
 option(EIGEN_DEFAULT_TO_ROW_MAJOR "Use row-major as default matrix storage order" OFF)
 if(EIGEN_DEFAULT_TO_ROW_MAJOR)
  add_definitions("-DEIGEN_DEFAULT_TO_ROW_MAJOR")
 endif()
 endif()
-#==============================================================================
+add_definitions("-DEIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS")
 # Test Configuration.
 #==============================================================================
 if (EIGEN_BUILD_TESTING)
  function(ei_maybe_separate_arguments variable mode args)
    # Use separate_arguments if the input is a single string containing a space.
    # Otherwise, if it is already a list or doesn't have a space, just propagate
    # the original value.  This is to better support multi-argument lists.
    list(LENGTH args list_length)
    if (${list_length} EQUAL 1)
      string(FIND "${args}" " " has_space)
      if (${has_space} GREATER -1)
        separate_arguments(args ${mode} "${args}")
      endif()
    endif()
    set(${variable} ${args} PARENT_SCOPE)
  endfunction(ei_maybe_separate_arguments)
  include(CheckCXXCompilerFlag)
  macro(ei_add_cxx_compiler_flag FLAG)
    string(REGEX REPLACE "-" "" SFLAG1 ${FLAG})
    string(REGEX REPLACE "\\+" "p" SFLAG ${SFLAG1})
    check_cxx_compiler_flag(${FLAG} COMPILER_SUPPORT_${SFLAG})
    if(COMPILER_SUPPORT_${SFLAG})
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAG}")
    endif()
  endmacro()
  set(EIGEN_TEST_CUSTOM_LINKER_FLAGS  "" CACHE STRING "Additional linker flags when linking unit tests.")
  set(EIGEN_TEST_CUSTOM_CXX_FLAGS     "" CACHE STRING "Additional compiler flags when compiling unit tests.")
  # Convert space-separated arguments into CMake lists for downstream consumption.
  ei_maybe_separate_arguments(EIGEN_TEST_CUSTOM_LINKER_FLAGS NATIVE_COMMAND "${EIGEN_TEST_CUSTOM_LINKER_FLAGS}")
  ei_maybe_separate_arguments(EIGEN_TEST_CUSTOM_CXX_FLAGS NATIVE_COMMAND "${EIGEN_TEST_CUSTOM_CXX_FLAGS}")
  option(EIGEN_SPLIT_LARGE_TESTS "Split large tests into smaller executables" ON)
  set(EIGEN_TEST_MAX_SIZE "320" CACHE STRING "Maximal matrix/vector size, default is 320")
  # Flags for tests.
  if(NOT MSVC)
    # We assume that other compilers are partly compatible with GNUCC
    # clang outputs some warnings for unknown flags that are not caught by check_cxx_compiler_flag
    # adding -Werror turns such warnings into errors
    check_cxx_compiler_flag("-Werror" COMPILER_SUPPORT_WERROR)
    if(COMPILER_SUPPORT_WERROR)
      set(CMAKE_REQUIRED_FLAGS "-Werror")
    endif()
    ei_add_cxx_compiler_flag("-pedantic")
    ei_add_cxx_compiler_flag("-Wall")
    ei_add_cxx_compiler_flag("-Wextra")
    # ei_add_cxx_compiler_flag("-Weverything") # clang
    ei_add_cxx_compiler_flag("-Wundef")
    ei_add_cxx_compiler_flag("-Wcast-align")
    ei_add_cxx_compiler_flag("-Wchar-subscripts")
    ei_add_cxx_compiler_flag("-Wnon-virtual-dtor")
    ei_add_cxx_compiler_flag("-Wunused-local-typedefs")
    ei_add_cxx_compiler_flag("-Wpointer-arith")
    ei_add_cxx_compiler_flag("-Wwrite-strings")
    ei_add_cxx_compiler_flag("-Wformat-security")
    ei_add_cxx_compiler_flag("-Wshorten-64-to-32")
    ei_add_cxx_compiler_flag("-Wlogical-op")
    ei_add_cxx_compiler_flag("-Wenum-conversion")
    ei_add_cxx_compiler_flag("-Wc++11-extensions")
    ei_add_cxx_compiler_flag("-Wdouble-promotion")
    # ei_add_cxx_compiler_flag("-Wconversion")
    ei_add_cxx_compiler_flag("-Wshadow")
    ei_add_cxx_compiler_flag("-Wno-psabi")
    ei_add_cxx_compiler_flag("-Wno-variadic-macros")
    ei_add_cxx_compiler_flag("-Wno-long-long")
    ei_add_cxx_compiler_flag("-Wno-pass-failed")          # disable clang's warning for unrolling when the loop count is dynamic.
    ei_add_cxx_compiler_flag("-fno-common")
    ei_add_cxx_compiler_flag("-fstrict-aliasing")
    ei_add_cxx_compiler_flag("-wd981")                    # disable ICC's "operands are evaluated in unspecified order" remark
    ei_add_cxx_compiler_flag("-wd2304")                   # disable ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
    # Clang emits warnings about unused flag.
    if (NOT CMAKE_CXX_COMPILER_ID MATCHES "Clang")
      ei_add_cxx_compiler_flag("-fno-check-new")
    endif()
    # GCC 12+ emits false-positive -Warray-bounds, -Wmaybe-uninitialized,
    # -Wstringop-overread, and -Wnonnull warnings at -O2/-O3 in heavily
    # templated code with mixed static/dynamic sizes.  These are well-known
    # compiler bugs (see GCC PR 109394, 106247, 105329, 98610, among others).
 if(CMAKE_COMPILER_IS_GNUCXX)
-      ei_add_cxx_compiler_flag("-Wno-array-bounds")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fexceptions -fno-check-new -fno-common -fstrict-aliasing")
-      ei_add_cxx_compiler_flag("-Wno-maybe-uninitialized")
+  set(CMAKE_CXX_FLAGS_DEBUG "-g3")
-      ei_add_cxx_compiler_flag("-Wno-stringop-overread")
+  set(CMAKE_CXX_FLAGS_RELEASE "-g0 -O2")
-      ei_add_cxx_compiler_flag("-Wno-nonnull")
+
  check_cxx_compiler_flag("-Wno-variadic-macros" COMPILER_SUPPORT_WNOVARIADICMACRO)
  if(COMPILER_SUPPORT_WNOVARIADICMACRO)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-variadic-macros")
  endif()
-
+  check_cxx_compiler_flag("-Wextra" COMPILER_SUPPORT_WEXTRA)
-    if(ANDROID_NDK)
+  if(COMPILER_SUPPORT_WEXTRA)
-      ei_add_cxx_compiler_flag("-pie")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wextra")
      ei_add_cxx_compiler_flag("-fPIE")
  endif()
-    set(CMAKE_REQUIRED_FLAGS "")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
  option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
  if(EIGEN_TEST_SSE2)
@@ -474,101 +150,18 @@ if (EIGEN_BUILD_TESTING)
    message(STATUS "Enabling SSE4.2 in tests/examples")
  endif()
    option(EIGEN_TEST_AVX "Enable/Disable AVX in tests/examples" OFF)
    if(EIGEN_TEST_AVX)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx")
      message(STATUS "Enabling AVX in tests/examples")
    endif()
    option(EIGEN_TEST_FMA "Enable/Disable FMA in tests/examples" OFF)
    if(EIGEN_TEST_FMA AND NOT EIGEN_TEST_NEON)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfma")
      message(STATUS "Enabling FMA in tests/examples")
    endif()
    option(EIGEN_TEST_AVX2 "Enable/Disable AVX2 in tests/examples" OFF)
    if(EIGEN_TEST_AVX2)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx2 -mfma")
      message(STATUS "Enabling AVX2 in tests/examples")
    endif()
    option(EIGEN_TEST_AVX512 "Enable/Disable AVX512 in tests/examples" OFF)
    if(EIGEN_TEST_AVX512)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx512f -mfma")
      message(STATUS "Enabling AVX512 in tests/examples")
    endif()
    option(EIGEN_TEST_AVX512DQ "Enable/Disable AVX512DQ in tests/examples" OFF)
    if(EIGEN_TEST_AVX512DQ)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx512dq -mfma")
      message(STATUS "Enabling AVX512DQ in tests/examples")
    endif()
    option(EIGEN_TEST_AVX512FP16 "Enable/Disable AVX512-FP16 in tests/examples" OFF)
    if(EIGEN_TEST_AVX512FP16)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx512f -mfma -mavx512vl -mavx512fp16")
    message(STATUS "Enabling AVX512-FP16 in tests/examples")
    endif()
    option(EIGEN_TEST_F16C "Enable/Disable F16C in tests/examples" OFF)
    if(EIGEN_TEST_F16C)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mf16c")
      message(STATUS "Enabling F16C in tests/examples")
    endif()
  option(EIGEN_TEST_ALTIVEC "Enable/Disable AltiVec in tests/examples" OFF)
  if(EIGEN_TEST_ALTIVEC)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
    message(STATUS "Enabling AltiVec in tests/examples")
  endif()
    option(EIGEN_TEST_VSX "Enable/Disable VSX in tests/examples" OFF)
    if(EIGEN_TEST_VSX)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64 -mvsx")
      message(STATUS "Enabling VSX in tests/examples")
    endif()
    option(EIGEN_TEST_MSA "Enable/Disable MSA in tests/examples" OFF)
    if(EIGEN_TEST_MSA)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mmsa")
      message(STATUS "Enabling MSA in tests/examples")
    endif()
   option(EIGEN_TEST_LSX "Enable/Disable LSX in tests/examples" OFF)
   if(EIGEN_TEST_LSX)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mlsx")
     message(STATUS "Enabling LSX in tests/examples")
   endif()
  option(EIGEN_TEST_NEON "Enable/Disable Neon in tests/examples" OFF)
  if(EIGEN_TEST_NEON)
-      if(EIGEN_TEST_FMA)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfloat-abi=softfp -mfpu=neon -mcpu=cortex-a8")
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon-vfpv4")
      else()
        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon")
      endif()
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfloat-abi=hard")
    message(STATUS "Enabling NEON in tests/examples")
  endif()
    option(EIGEN_TEST_NEON64 "Enable/Disable Neon in tests/examples" OFF)
    if(EIGEN_TEST_NEON64)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
      message(STATUS "Enabling NEON in tests/examples")
    endif()
    option(EIGEN_TEST_Z13 "Enable/Disable S390X(zEC13) ZVECTOR in tests/examples" OFF)
    if(EIGEN_TEST_Z13)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=z13 -mzvector")
      message(STATUS "Enabling S390X(zEC13) ZVECTOR in tests/examples")
    endif()
    option(EIGEN_TEST_Z14 "Enable/Disable S390X(zEC14) ZVECTOR in tests/examples" OFF)
    if(EIGEN_TEST_Z14)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=z14 -mzvector")
      message(STATUS "Enabling S390X(zEC13) ZVECTOR in tests/examples")
    endif()
  check_cxx_compiler_flag("-fopenmp" COMPILER_SUPPORT_OPENMP)
  if(COMPILER_SUPPORT_OPENMP)
    option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
@@ -578,14 +171,16 @@ if (EIGEN_BUILD_TESTING)
    endif()
  endif()
-  else()
+endif(CMAKE_COMPILER_IS_GNUCXX)
 if(MSVC)
  # C4127 - conditional expression is constant
  # C4714 - marked as __forceinline not inlined (I failed to deactivate it selectively)
  #         We can disable this warning in the unit tests since it is clear that it occurs
  #         because we are oftentimes returning objects that have a destructor or may
  #         throw exceptions - in particular in the unit tests we are throwing extra many
  #         exceptions to cover indexing errors.
-    # C4505 - unreferenced local function has been removed (impossible to deactivate selectively)
+  # C4505 - unreferenced local function has been removed (impossible to deactive selectively)
  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /wd4127 /wd4505 /wd4714")
  # replace all /Wx by /W4
@@ -605,31 +200,10 @@ if (EIGEN_BUILD_TESTING)
    if(NOT CMAKE_CL_64)
      # arch is not supported on 64 bit systems, SSE is enabled automatically.
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:SSE2")
-      endif()
+    endif(NOT CMAKE_CL_64)
    message(STATUS "Enabling SSE2 in tests/examples")
-    endif()
+  endif(EIGEN_TEST_SSE2)
-
+endif(MSVC)
    option(EIGEN_TEST_AVX "Enable/Disable AVX in tests/examples" OFF)
    if(EIGEN_TEST_AVX)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
      message(STATUS "Enabling AVX in tests/examples")
    endif()
    option(EIGEN_TEST_FMA "Enable/Disable FMA/AVX2 in tests/examples" OFF)
    option(EIGEN_TEST_AVX2 "Enable/Disable FMA/AVX2 in tests/examples" OFF)
    if((EIGEN_TEST_FMA AND NOT EIGEN_TEST_NEON) OR EIGEN_TEST_AVX2)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
      message(STATUS "Enabling FMA/AVX2 in tests/examples")
    endif()
    option(EIGEN_TEST_AVX512 "Enable/Disable AVX512 in tests/examples" OFF)
    option(EIGEN_TEST_AVX512DQ "Enable/Disable AVX512DQ in tests/examples" OFF)
    if(EIGEN_TEST_AVX512 OR EIGEN_TEST_AVX512DQ)
      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX512")
      message(STATUS "Enabling AVX512 in tests/examples")
    endif()
  endif(NOT MSVC)
 option(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION "Disable explicit vectorization in tests/examples" OFF)
 option(EIGEN_TEST_X87 "Force using X87 instructions. Implies no vectorization." OFF)
@@ -665,156 +239,167 @@ if (EIGEN_BUILD_TESTING)
  message(STATUS "Disabling alignment in tests/examples")
 endif()
-  option(EIGEN_TEST_NO_EXCEPTIONS "Disables C++ exceptions" OFF)
+option(EIGEN_TEST_C++0x "Enables all C++0x features." OFF)
  if(EIGEN_TEST_NO_EXCEPTIONS)
    ei_add_cxx_compiler_flag("-fno-exceptions")
    message(STATUS "Disabling exceptions in tests/examples")
  endif()
-  set(EIGEN_CUDA_CXX_FLAGS "" CACHE STRING "Additional flags to pass to the cuda compiler.")
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
  set(EIGEN_CUDA_COMPUTE_ARCH 30 CACHE STRING "The CUDA compute architecture(s) to target when compiling CUDA code")
-  option(EIGEN_TEST_SYCL "Add Sycl support." OFF)
+# the user modifiable install path for header files
-  if(EIGEN_TEST_SYCL)
+set(EIGEN_INCLUDE_INSTALL_DIR ${EIGEN_INCLUDE_INSTALL_DIR} CACHE PATH "The directory where we install the header files (optional)")
    option(EIGEN_SYCL_DPCPP "Use the DPCPP Sycl implementation (DPCPP is default SYCL-Compiler)." ON)
    option(EIGEN_SYCL_TRISYCL "Use the triSYCL Sycl implementation." OFF)
    option(EIGEN_SYCL_ComputeCpp "Use the ComputeCPP Sycl implementation." OFF)
-    # Building options
+# set the internal install path for header files which depends on wether the user modifiable
-    # https://developer.codeplay.com/products/computecpp/ce/2.11.0/guides/eigen-overview/options-for-building-eigen-sycl
+# EIGEN_INCLUDE_INSTALL_DIR has been set by the user or not.
-    option(EIGEN_SYCL_USE_DEFAULT_SELECTOR "Use sycl default selector to select the preferred device." OFF)
+if(EIGEN_INCLUDE_INSTALL_DIR)
-    option(EIGEN_SYCL_NO_LOCAL_MEM "Build for devices without dedicated shared memory." OFF)
+  set(INCLUDE_INSTALL_DIR
-    option(EIGEN_SYCL_LOCAL_MEM "Allow the use of local memory (enabled by default)." ON)
+    ${EIGEN_INCLUDE_INSTALL_DIR}
-    option(EIGEN_SYCL_LOCAL_THREAD_DIM0 "Set work group size for dimension 0." 16)
+    CACHE INTERNAL
-    option(EIGEN_SYCL_LOCAL_THREAD_DIM1 "Set work group size for dimension 1." 16)
+    "The directory where we install the header files (internal)"
-    option(EIGEN_SYCL_ASYNC_EXECUTION "Allow asynchronous execution (enabled by default)." ON)
+  )
-    option(EIGEN_SYCL_DISABLE_SKINNY "Disable optimization for tall/skinny matrices." OFF)
+else()
-    option(EIGEN_SYCL_DISABLE_DOUBLE_BUFFER "Disable double buffer." OFF)
+  set(INCLUDE_INSTALL_DIR
-    option(EIGEN_SYCL_DISABLE_SCALAR "Disable scalar contraction." OFF)
+    "${CMAKE_INSTALL_PREFIX}/include/eigen3"
-    option(EIGEN_SYCL_DISABLE_GEMV "Disable GEMV and create a single kernel to calculate contraction instead." OFF)
+    CACHE INTERNAL
-
+    "The directory where we install the header files (internal)"
    set(EIGEN_SYCL ON)
    set(CMAKE_CXX_STANDARD 17)
    set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations -Wno-shorten-64-to-32 -Wno-cast-align")
    set(CMAKE_CXX_FLAGS  "${CMAKE_CXX_FLAGS} -Wno-deprecated-copy-with-user-provided-copy -Wno-unused-variable")
    set (CMAKE_MODULE_PATH "${CMAKE_ROOT}/Modules" "cmake/Modules/" "${CMAKE_MODULE_PATH}")
    find_package(Threads REQUIRED)
    if(EIGEN_SYCL_TRISYCL)
      message(STATUS "Using triSYCL")
      include(FindTriSYCL)
    elseif(EIGEN_SYCL_ComputeCpp)
      message(STATUS "Using ComputeCPP SYCL")
      include(FindComputeCpp)
      set(COMPUTECPP_DRIVER_DEFAULT_VALUE OFF)
      if (NOT MSVC)
        set(COMPUTECPP_DRIVER_DEFAULT_VALUE ON)
      endif()
      option(COMPUTECPP_USE_COMPILER_DRIVER
              "Use ComputeCpp driver instead of a 2 steps compilation"
              ${COMPUTECPP_DRIVER_DEFAULT_VALUE}
  )
    else() #Default SYCL compiler is DPCPP (EIGEN_SYCL_DPCPP)
      set(DPCPP_SYCL_TARGET "spir64" CACHE STRING "Default target for Intel CPU/GPU")
      message(STATUS "Using DPCPP")
      find_package(DPCPP)
      add_definitions(-DSYCL_COMPILER_IS_DPCPP)
    endif(EIGEN_SYCL_TRISYCL)
    if(EIGEN_DONT_VECTORIZE_SYCL)
      message(STATUS "Disabling SYCL vectorization in tests/examples")
      # When disabling SYCL vectorization, also disable Eigen default vectorization
      add_definitions(-DEIGEN_DONT_VECTORIZE=1)
      add_definitions(-DEIGEN_DONT_VECTORIZE_SYCL=1)
    endif()
 endif()
-  include(EigenConfigureTesting)
+# similar to set_target_properties but append the property instead of overwriting it
 macro(ei_add_target_property target prop value)
  get_target_property(previous ${target} ${prop})
  # if the property wasn't previously set, ${previous} is now "previous-NOTFOUND" which cmake allows catching with plain if()
  if(NOT previous)
    set(previous "")
  endif(NOT previous)
  set_target_properties(${target} PROPERTIES ${prop} "${previous} ${value}")
 endmacro(ei_add_target_property)
 install(FILES
  signature_of_eigen3_matrix_library
  DESTINATION ${INCLUDE_INSTALL_DIR} COMPONENT Devel
  )
 if(EIGEN_BUILD_PKGCONFIG)
    SET(path_separator ":")
    STRING(REPLACE ${path_separator} ";" pkg_config_libdir_search "$ENV{PKG_CONFIG_LIBDIR}")
    message(STATUS "searching for 'pkgconfig' directory in PKG_CONFIG_LIBDIR ( $ENV{PKG_CONFIG_LIBDIR} ), ${CMAKE_INSTALL_PREFIX}/share, and ${CMAKE_INSTALL_PREFIX}/lib")
    FIND_PATH(pkg_config_libdir pkgconfig ${pkg_config_libdir_search} ${CMAKE_INSTALL_PREFIX}/share ${CMAKE_INSTALL_PREFIX}/lib ${pkg_config_libdir_search})
    if(pkg_config_libdir)
        SET(pkg_config_install_dir ${pkg_config_libdir})
        message(STATUS "found ${pkg_config_libdir}/pkgconfig" )
    else(pkg_config_libdir)
        SET(pkg_config_install_dir ${CMAKE_INSTALL_PREFIX}/share)
        message(STATUS "pkgconfig not found; installing in ${pkg_config_install_dir}" )
    endif(pkg_config_libdir)
    configure_file(eigen3.pc.in eigen3.pc)
    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
        DESTINATION ${pkg_config_install_dir}/pkgconfig
        )
 endif(EIGEN_BUILD_PKGCONFIG)
 add_subdirectory(Eigen)
 add_subdirectory(doc EXCLUDE_FROM_ALL)
 add_custom_target(buildtests)
 add_custom_target(check COMMAND "ctest")
 add_dependencies(check buildtests)
 # CMake/Ctest does not allow us to change the build command,
 # so we have to workaround by directly editing the generated DartConfiguration.tcl file
 # save CMAKE_MAKE_PROGRAM
 set(CMAKE_MAKE_PROGRAM_SAVE ${CMAKE_MAKE_PROGRAM})
 # and set a fake one
 set(CMAKE_MAKE_PROGRAM "@EIGEN_MAKECOMMAND_PLACEHOLDER@")
 include(CTest)
 enable_testing() # must be called from the root CMakeLists, see man page
 include(EigenTesting)
 ei_init_testing()
 # overwrite default DartConfiguration.tcl
 # The worarounds are different for each version of the MSVC IDE
 if(MSVC_IDE)
  if(MSVC_VERSION EQUAL 1600) # MSVC 2010
    set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} buildtests.vcxproj /p:Configuration=\${CTEST_CONFIGURATION_TYPE} \n # ")
  else() # MSVC 2008 (TODO check MSVC 2005)
    set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} /project buildtests")
  endif()
 else()
  # for make and nmake
  set(EIGEN_MAKECOMMAND_PLACEHOLDER "${CMAKE_MAKE_PROGRAM_SAVE} buildtests")
 endif()
 configure_file(${CMAKE_BINARY_DIR}/DartConfiguration.tcl ${CMAKE_BINARY_DIR}/DartConfiguration.tcl)
 # restore default CMAKE_MAKE_PROGRAM
 set(CMAKE_MAKE_PROGRAM ${CMAKE_MAKE_PROGRAM_SAVE})
 # un-set temporary variables so that it is like they never existed.
 # CMake 2.6.3 introduces the more logical unset() syntax for this.
 set(CMAKE_MAKE_PROGRAM_SAVE)
 set(EIGEN_MAKECOMMAND_PLACEHOLDER)
 configure_file(${CMAKE_SOURCE_DIR}/CTestCustom.cmake.in ${CMAKE_BINARY_DIR}/CTestCustom.cmake)
 if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
-    # CTest automatic test building relies on the "all" target.
+  add_subdirectory(test) # can't do EXCLUDE_FROM_ALL here, breaks CTest
    add_subdirectory(test)
    add_subdirectory(failtest)
 else()
  add_subdirectory(test EXCLUDE_FROM_ALL)
    add_subdirectory(failtest EXCLUDE_FROM_ALL)
 endif()
 if(NOT MSVC)
  if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
    add_subdirectory(blas)
    add_subdirectory(lapack)
  else()
    add_subdirectory(blas EXCLUDE_FROM_ALL)
    add_subdirectory(lapack EXCLUDE_FROM_ALL)
  endif()
 endif(NOT MSVC)
 add_subdirectory(unsupported)
 add_subdirectory(demos EXCLUDE_FROM_ALL)
 # must be after test and unsupported, for configuring buildtests.in
 add_subdirectory(scripts EXCLUDE_FROM_ALL)
 # TODO: consider also replacing EIGEN_BUILD_BTL by a custom target "make btl"?
 if(EIGEN_BUILD_BTL)
  add_subdirectory(bench/btl EXCLUDE_FROM_ALL)
 endif(EIGEN_BUILD_BTL)
 ei_testing_print_summary()
-  if (EIGEN_SPLIT_TESTSUITE)
+message(STATUS "")
-    ei_split_testsuite("${EIGEN_SPLIT_TESTSUITE}")
+message(STATUS "Configured Eigen ${EIGEN_VERSION_NUMBER}")
-  endif()
+message(STATUS "")
 endif(EIGEN_BUILD_TESTING)
-#==============================================================================
+option(EIGEN_FAILTEST "Enable failtests." OFF)
-# Other Build Configurations.
+if(EIGEN_FAILTEST)
-#==============================================================================
+  add_subdirectory(failtest)
 add_subdirectory(unsupported)
 if(EIGEN_BUILD_BLAS)
  add_subdirectory(blas)
 endif()
 if (EIGEN_BUILD_LAPACK)
  add_subdirectory(lapack)
 endif()
 if(EIGEN_BUILD_DOC)
  add_subdirectory(doc EXCLUDE_FROM_ALL)
 endif()
 if (EIGEN_BUILD_DEMOS)
  add_subdirectory(demos EXCLUDE_FROM_ALL)
 endif()
 if (PROJECT_IS_TOP_LEVEL)
  # must be after test and unsupported, for configuring buildtests.in
  add_subdirectory(scripts EXCLUDE_FROM_ALL)
  configure_file(scripts/cdashtesting.cmake.in cdashtesting.cmake @ONLY)
 endif()
 #==============================================================================
 # Summary.
 #==============================================================================
 if(PROJECT_IS_TOP_LEVEL)
 string(TOLOWER "${CMAKE_GENERATOR}" cmake_generator_tolower)
 if(cmake_generator_tolower MATCHES "makefile")
-    message(STATUS "Available targets (use: make TARGET):")
+  message(STATUS "Some things you can do now:")
-  else()
+  message(STATUS "--------------+--------------------------------------------------------------")
-    message(STATUS "Available targets (use: cmake --build . --target TARGET):")
+  message(STATUS "Command       |   Description")
-  endif()
+  message(STATUS "--------------+--------------------------------------------------------------")
-  message(STATUS "------------+--------------------------------------------------------------")
+  message(STATUS "make install  | Install to ${CMAKE_INSTALL_PREFIX}. To change that:")
-  message(STATUS "Target      |   Description")
+  message(STATUS "              |     cmake . -DCMAKE_INSTALL_PREFIX=yourpath")
-  message(STATUS "------------+--------------------------------------------------------------")
+  message(STATUS "              |   Eigen headers will then be installed to:")
-  message(STATUS "install     | Install Eigen. Headers will be installed to:")
+  message(STATUS "              |     ${INCLUDE_INSTALL_DIR}")
-  message(STATUS "            |     <CMAKE_INSTALL_PREFIX>/<INCLUDE_INSTALL_DIR>")
+  message(STATUS "              |   To install Eigen headers to a separate location, do:")
-  message(STATUS "            |   Using the following values:")
+  message(STATUS "              |     cmake . -DEIGEN_INCLUDE_INSTALL_DIR=yourpath")
-  message(STATUS "            |     CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
+  message(STATUS "make doc      | Generate the API documentation, requires Doxygen & LaTeX")
-  message(STATUS "            |     INCLUDE_INSTALL_DIR:  ${INCLUDE_INSTALL_DIR}")
+  message(STATUS "make check    | Build and run the unit-tests. Read this page:")
  message(STATUS "            |   Change the install location of Eigen headers using:")
  message(STATUS "            |     cmake . -DCMAKE_INSTALL_PREFIX=yourprefix")
  message(STATUS "            |   Or:")
  message(STATUS "            |     cmake . -DINCLUDE_INSTALL_DIR=yourdir")
  message(STATUS "uninstall   | Remove files installed by the install target")
  if (EIGEN_BUILD_DOC)
    message(STATUS "doc         | Generate the API documentation, requires Doxygen & LaTeX")
    message(STATUS "install-doc | Install the API documentation")
  endif()
  if(EIGEN_BUILD_TESTING)
    message(STATUS "check       | Build and run the unit-tests. Read this page:")
  message(STATUS "              |   http://eigen.tuxfamily.org/index.php?title=Tests")
-  endif()
+  message(STATUS "make blas     | Build BLAS library (not the same thing as Eigen)")
-  if (EIGEN_BUILD_BLAS)
+  message(STATUS "--------------+--------------------------------------------------------------")
-    message(STATUS "blas        | Build BLAS library (not the same thing as Eigen)")
+else()
-  endif()
+  message(STATUS "To build/run the unit tests, read this page:")
-  if (EIGEN_BUILD_LAPACK)
+  message(STATUS "  http://eigen.tuxfamily.org/index.php?title=Tests")
    message(STATUS "lapack      | Build LAPACK subset library (not the same thing as Eigen)")
  endif()
  message(STATUS "------------+--------------------------------------------------------------")
  message(STATUS "")
 endif()
 message(STATUS "")
 message(STATUS "Configured Eigen ${EIGEN_VERSION_STRING}")
 message(STATUS "")
--- a/COPYING.APACHE
+++ b/COPYING.APACHE
@@ -1,203 +0,0 @@
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--- a/COPYING.BSD
+++ b/COPYING.BSD
@@ -1,26 +0,0 @@
 /*
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--- a/COPYING.GPL
+++ b/COPYING.GPL
@@ -0,0 +1,674 @@
                    GNU GENERAL PUBLIC LICENSE
                       Version 3, 29 June 2007
 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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 and execute modified versions of a covered work in that User Product from
 a modified version of its Corresponding Source.  The information must
 suffice to ensure that the continued functioning of the modified object
 code is in no case prevented or interfered with solely because
 modification has been made.
  If you convey an object code work under this section in, or with, or
 specifically for use in, a User Product, and the conveying occurs as
 part of a transaction in which the right of possession and use of the
 User Product is transferred to the recipient in perpetuity or for a
 fixed term (regardless of how the transaction is characterized), the
 Corresponding Source conveyed under this section must be accompanied
 by the Installation Information.  But this requirement does not apply
 if neither you nor any third party retains the ability to install
 modified object code on the User Product (for example, the work has
 been installed in ROM).
  The requirement to provide Installation Information does not include a
 requirement to continue to provide support service, warranty, or updates
 for a work that has been modified or installed by the recipient, or for
 the User Product in which it has been modified or installed.  Access to a
 network may be denied when the modification itself materially and
 adversely affects the operation of the network or violates the rules and
 protocols for communication across the network.
  Corresponding Source conveyed, and Installation Information provided,
 in accord with this section must be in a format that is publicly
 documented (and with an implementation available to the public in
 source code form), and must require no special password or key for
 unpacking, reading or copying.
  7. Additional Terms.
  "Additional permissions" are terms that supplement the terms of this
 License by making exceptions from one or more of its conditions.
 Additional permissions that are applicable to the entire Program shall
 be treated as though they were included in this License, to the extent
 that they are valid under applicable law.  If additional permissions
 apply only to part of the Program, that part may be used separately
 under those permissions, but the entire Program remains governed by
 this License without regard to the additional permissions.
  When you convey a copy of a covered work, you may at your option
 remove any additional permissions from that copy, or from any part of
 it.  (Additional permissions may be written to require their own
 removal in certain cases when you modify the work.)  You may place
 additional permissions on material, added by you to a covered work,
 for which you have or can give appropriate copyright permission.
  Notwithstanding any other provision of this License, for material you
 add to a covered work, you may (if authorized by the copyright holders of
 that material) supplement the terms of this License with terms:
    a) Disclaiming warranty or limiting liability differently from the
    terms of sections 15 and 16 of this License; or
    b) Requiring preservation of specified reasonable legal notices or
    author attributions in that material or in the Appropriate Legal
    Notices displayed by works containing it; or
    c) Prohibiting misrepresentation of the origin of that material, or
    requiring that modified versions of such material be marked in
    reasonable ways as different from the original version; or
    d) Limiting the use for publicity purposes of names of licensors or
    authors of the material; or
    e) Declining to grant rights under trademark law for use of some
    trade names, trademarks, or service marks; or
    f) Requiring indemnification of licensors and authors of that
    material by anyone who conveys the material (or modified versions of
    it) with contractual assumptions of liability to the recipient, for
    any liability that these contractual assumptions directly impose on
    those licensors and authors.
  All other non-permissive additional terms are considered "further
 restrictions" within the meaning of section 10.  If the Program as you
 received it, or any part of it, contains a notice stating that it is
 governed by this License along with a term that is a further
 restriction, you may remove that term.  If a license document contains
 a further restriction but permits relicensing or conveying under this
 License, you may add to a covered work material governed by the terms
 of that license document, provided that the further restriction does
 not survive such relicensing or conveying.
  If you add terms to a covered work in accord with this section, you
 must place, in the relevant source files, a statement of the
 additional terms that apply to those files, or a notice indicating
 where to find the applicable terms.
  Additional terms, permissive or non-permissive, may be stated in the
 form of a separately written license, or stated as exceptions;
 the above requirements apply either way.
  8. Termination.
  You may not propagate or modify a covered work except as expressly
 provided under this License.  Any attempt otherwise to propagate or
 modify it is void, and will automatically terminate your rights under
 this License (including any patent licenses granted under the third
 paragraph of section 11).
  However, if you cease all violation of this License, then your
 license from a particular copyright holder is reinstated (a)
 provisionally, unless and until the copyright holder explicitly and
 finally terminates your license, and (b) permanently, if the copyright
 holder fails to notify you of the violation by some reasonable means
 prior to 60 days after the cessation.
  Moreover, your license from a particular copyright holder is
 reinstated permanently if the copyright holder notifies you of the
 violation by some reasonable means, this is the first time you have
 received notice of violation of this License (for any work) from that
 copyright holder, and you cure the violation prior to 30 days after
 your receipt of the notice.
  Termination of your rights under this section does not terminate the
 licenses of parties who have received copies or rights from you under
 this License.  If your rights have been terminated and not permanently
 reinstated, you do not qualify to receive new licenses for the same
 material under section 10.
  9. Acceptance Not Required for Having Copies.
  You are not required to accept this License in order to receive or
 run a copy of the Program.  Ancillary propagation of a covered work
 occurring solely as a consequence of using peer-to-peer transmission
 to receive a copy likewise does not require acceptance.  However,
 nothing other than this License grants you permission to propagate or
 modify any covered work.  These actions infringe copyright if you do
 not accept this License.  Therefore, by modifying or propagating a
 covered work, you indicate your acceptance of this License to do so.
  10. Automatic Licensing of Downstream Recipients.
  Each time you convey a covered work, the recipient automatically
 receives a license from the original licensors, to run, modify and
 propagate that work, subject to this License.  You are not responsible
 for enforcing compliance by third parties with this License.
  An "entity transaction" is a transaction transferring control of an
 organization, or substantially all assets of one, or subdividing an
 organization, or merging organizations.  If propagation of a covered
 work results from an entity transaction, each party to that
 transaction who receives a copy of the work also receives whatever
 licenses to the work the party's predecessor in interest had or could
 give under the previous paragraph, plus a right to possession of the
 Corresponding Source of the work from the predecessor in interest, if
 the predecessor has it or can get it with reasonable efforts.
  You may not impose any further restrictions on the exercise of the
 rights granted or affirmed under this License.  For example, you may
 not impose a license fee, royalty, or other charge for exercise of
 rights granted under this License, and you may not initiate litigation
 (including a cross-claim or counterclaim in a lawsuit) alleging that
 any patent claim is infringed by making, using, selling, offering for
 sale, or importing the Program or any portion of it.
  11. Patents.
  A "contributor" is a copyright holder who authorizes use under this
 License of the Program or a work on which the Program is based.  The
 work thus licensed is called the contributor's "contributor version".
  A contributor's "essential patent claims" are all patent claims
 owned or controlled by the contributor, whether already acquired or
 hereafter acquired, that would be infringed by some manner, permitted
 by this License, of making, using, or selling its contributor version,
 but do not include claims that would be infringed only as a
 consequence of further modification of the contributor version.  For
 purposes of this definition, "control" includes the right to grant
 patent sublicenses in a manner consistent with the requirements of
 this License.
  Each contributor grants you a non-exclusive, worldwide, royalty-free
 patent license under the contributor's essential patent claims, to
 make, use, sell, offer for sale, import and otherwise run, modify and
 propagate the contents of its contributor version.
  In the following three paragraphs, a "patent license" is any express
 agreement or commitment, however denominated, not to enforce a patent
 (such as an express permission to practice a patent or covenant not to
 sue for patent infringement).  To "grant" such a patent license to a
 party means to make such an agreement or commitment not to enforce a
 patent against the party.
  If you convey a covered work, knowingly relying on a patent license,
 and the Corresponding Source of the work is not available for anyone
 to copy, free of charge and under the terms of this License, through a
 publicly available network server or other readily accessible means,
 then you must either (1) cause the Corresponding Source to be so
 available, or (2) arrange to deprive yourself of the benefit of the
 patent license for this particular work, or (3) arrange, in a manner
 consistent with the requirements of this License, to extend the patent
 license to downstream recipients.  "Knowingly relying" means you have
 actual knowledge that, but for the patent license, your conveying the
 covered work in a country, or your recipient's use of the covered work
 in a country, would infringe one or more identifiable patents in that
 country that you have reason to believe are valid.
  If, pursuant to or in connection with a single transaction or
 arrangement, you convey, or propagate by procuring conveyance of, a
 covered work, and grant a patent license to some of the parties
 receiving the covered work authorizing them to use, propagate, modify
 or convey a specific copy of the covered work, then the patent license
 you grant is automatically extended to all recipients of the covered
 work and works based on it.
  A patent license is "discriminatory" if it does not include within
 the scope of its coverage, prohibits the exercise of, or is
 conditioned on the non-exercise of one or more of the rights that are
 specifically granted under this License.  You may not convey a covered
 work if you are a party to an arrangement with a third party that is
 in the business of distributing software, under which you make payment
 to the third party based on the extent of your activity of conveying
 the work, and under which the third party grants, to any of the
 parties who would receive the covered work from you, a discriminatory
 patent license (a) in connection with copies of the covered work
 conveyed by you (or copies made from those copies), or (b) primarily
 for and in connection with specific products or compilations that
 contain the covered work, unless you entered into that arrangement,
 or that patent license was granted, prior to 28 March 2007.
  Nothing in this License shall be construed as excluding or limiting
 any implied license or other defenses to infringement that may
 otherwise be available to you under applicable patent law.
  12. No Surrender of Others' Freedom.
  If conditions are imposed on you (whether by court order, agreement or
 otherwise) that contradict the conditions of this License, they do not
 excuse you from the conditions of this License.  If you cannot convey a
 covered work so as to satisfy simultaneously your obligations under this
 License and any other pertinent obligations, then as a consequence you may
 not convey it at all.  For example, if you agree to terms that obligate you
 to collect a royalty for further conveying from those to whom you convey
 the Program, the only way you could satisfy both those terms and this
 License would be to refrain entirely from conveying the Program.
  13. Use with the GNU Affero General Public License.
  Notwithstanding any other provision of this License, you have
 permission to link or combine any covered work with a work licensed
 under version 3 of the GNU Affero General Public License into a single
 combined work, and to convey the resulting work.  The terms of this
 License will continue to apply to the part which is the covered work,
 but the special requirements of the GNU Affero General Public License,
 section 13, concerning interaction through a network will apply to the
 combination as such.
  14. Revised Versions of this License.
  The Free Software Foundation may publish revised and/or new versions of
 the GNU General Public License from time to time.  Such new versions will
 be similar in spirit to the present version, but may differ in detail to
 address new problems or concerns.
  Each version is given a distinguishing version number.  If the
 Program specifies that a certain numbered version of the GNU General
 Public License "or any later version" applies to it, you have the
 option of following the terms and conditions either of that numbered
 version or of any later version published by the Free Software
 Foundation.  If the Program does not specify a version number of the
 GNU General Public License, you may choose any version ever published
 by the Free Software Foundation.
  If the Program specifies that a proxy can decide which future
 versions of the GNU General Public License can be used, that proxy's
 public statement of acceptance of a version permanently authorizes you
 to choose that version for the Program.
  Later license versions may give you additional or different
 permissions.  However, no additional obligations are imposed on any
 author or copyright holder as a result of your choosing to follow a
 later version.
  15. Disclaimer of Warranty.
  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
 APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
 HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
 OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
 THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
 IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
 ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
  16. Limitation of Liability.
  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
 WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
 THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
 GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
 USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
 DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
 PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
 EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
 SUCH DAMAGES.
  17. Interpretation of Sections 15 and 16.
  If the disclaimer of warranty and limitation of liability provided
 above cannot be given local legal effect according to their terms,
 reviewing courts shall apply local law that most closely approximates
 an absolute waiver of all civil liability in connection with the
 Program, unless a warranty or assumption of liability accompanies a
 copy of the Program in return for a fee.
                     END OF TERMS AND CONDITIONS
            How to Apply These Terms to Your New Programs
  If you develop a new program, and you want it to be of the greatest
 possible use to the public, the best way to achieve this is to make it
 free software which everyone can redistribute and change under these terms.
  To do so, attach the following notices to the program.  It is safest
 to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.
    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
 Also add information on how to contact you by electronic and paper mail.
  If the program does terminal interaction, make it output a short
 notice like this when it starts in an interactive mode:
    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.
 The hypothetical commands `show w' and `show c' should show the appropriate
 parts of the General Public License.  Of course, your program's commands
 might be different; for a GUI interface, you would use an "about box".
  You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU GPL, see
 <http://www.gnu.org/licenses/>.
  The GNU General Public License does not permit incorporating your program
 into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
 <http://www.gnu.org/philosophy/why-not-lgpl.html>.
--- a/COPYING.LGPL
+++ b/COPYING.LGPL
@@ -0,0 +1,165 @@
                  GNU LESSER GENERAL PUBLIC LICENSE
                       Version 3, 29 June 2007
 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
  This version of the GNU Lesser General Public License incorporates
 the terms and conditions of version 3 of the GNU General Public
 License, supplemented by the additional permissions listed below.
  0. Additional Definitions. 
  As used herein, "this License" refers to version 3 of the GNU Lesser
 General Public License, and the "GNU GPL" refers to version 3 of the GNU
 General Public License.
  "The Library" refers to a covered work governed by this License,
 other than an Application or a Combined Work as defined below.
  An "Application" is any work that makes use of an interface provided
 by the Library, but which is not otherwise based on the Library.
 Defining a subclass of a class defined by the Library is deemed a mode
 of using an interface provided by the Library.
  A "Combined Work" is a work produced by combining or linking an
 Application with the Library.  The particular version of the Library
 with which the Combined Work was made is also called the "Linked
 Version".
  The "Minimal Corresponding Source" for a Combined Work means the
 Corresponding Source for the Combined Work, excluding any source code
 for portions of the Combined Work that, considered in isolation, are
 based on the Application, and not on the Linked Version.
  The "Corresponding Application Code" for a Combined Work means the
 object code and/or source code for the Application, including any data
 and utility programs needed for reproducing the Combined Work from the
 Application, but excluding the System Libraries of the Combined Work.
  1. Exception to Section 3 of the GNU GPL.
  You may convey a covered work under sections 3 and 4 of this License
 without being bound by section 3 of the GNU GPL.
  2. Conveying Modified Versions.
  If you modify a copy of the Library, and, in your modifications, a
 facility refers to a function or data to be supplied by an Application
 that uses the facility (other than as an argument passed when the
 facility is invoked), then you may convey a copy of the modified
 version:
   a) under this License, provided that you make a good faith effort to
   ensure that, in the event an Application does not supply the
   function or data, the facility still operates, and performs
   whatever part of its purpose remains meaningful, or
   b) under the GNU GPL, with none of the additional permissions of
   this License applicable to that copy.
  3. Object Code Incorporating Material from Library Header Files.
  The object code form of an Application may incorporate material from
 a header file that is part of the Library.  You may convey such object
 code under terms of your choice, provided that, if the incorporated
 material is not limited to numerical parameters, data structure
 layouts and accessors, or small macros, inline functions and templates
 (ten or fewer lines in length), you do both of the following:
   a) Give prominent notice with each copy of the object code that the
   Library is used in it and that the Library and its use are
   covered by this License.
   b) Accompany the object code with a copy of the GNU GPL and this license
   document.
  4. Combined Works.
  You may convey a Combined Work under terms of your choice that,
 taken together, effectively do not restrict modification of the
 portions of the Library contained in the Combined Work and reverse
 engineering for debugging such modifications, if you also do each of
 the following:
   a) Give prominent notice with each copy of the Combined Work that
   the Library is used in it and that the Library and its use are
   covered by this License.
   b) Accompany the Combined Work with a copy of the GNU GPL and this license
   document.
   c) For a Combined Work that displays copyright notices during
   execution, include the copyright notice for the Library among
   these notices, as well as a reference directing the user to the
   copies of the GNU GPL and this license document.
   d) Do one of the following:
       0) Convey the Minimal Corresponding Source under the terms of this
       License, and the Corresponding Application Code in a form
       suitable for, and under terms that permit, the user to
       recombine or relink the Application with a modified version of
       the Linked Version to produce a modified Combined Work, in the
       manner specified by section 6 of the GNU GPL for conveying
       Corresponding Source.
       1) Use a suitable shared library mechanism for linking with the
       Library.  A suitable mechanism is one that (a) uses at run time
       a copy of the Library already present on the user's computer
       system, and (b) will operate properly with a modified version
       of the Library that is interface-compatible with the Linked
       Version. 
   e) Provide Installation Information, but only if you would otherwise
   be required to provide such information under section 6 of the
   GNU GPL, and only to the extent that such information is
   necessary to install and execute a modified version of the
   Combined Work produced by recombining or relinking the
   Application with a modified version of the Linked Version. (If
   you use option 4d0, the Installation Information must accompany
   the Minimal Corresponding Source and Corresponding Application
   Code. If you use option 4d1, you must provide the Installation
   Information in the manner specified by section 6 of the GNU GPL
   for conveying Corresponding Source.)
  5. Combined Libraries.
  You may place library facilities that are a work based on the
 Library side by side in a single library together with other library
 facilities that are not Applications and are not covered by this
 License, and convey such a combined library under terms of your
 choice, if you do both of the following:
   a) Accompany the combined library with a copy of the same work based
   on the Library, uncombined with any other library facilities,
   conveyed under the terms of this License.
   b) Give prominent notice with the combined library that part of it
   is a work based on the Library, and explaining where to find the
   accompanying uncombined form of the same work.
  6. Revised Versions of the GNU Lesser General Public License.
  The Free Software Foundation may publish revised and/or new versions
 of the GNU Lesser General Public License from time to time. Such new
 versions will be similar in spirit to the present version, but may
 differ in detail to address new problems or concerns.
  Each version is given a distinguishing version number. If the
 Library as you received it specifies that a certain numbered version
 of the GNU Lesser General Public License "or any later version"
 applies to it, you have the option of following the terms and
 conditions either of that published version or of any later version
 published by the Free Software Foundation. If the Library as you
 received it does not specify a version number of the GNU Lesser
 General Public License, you may choose any version of the GNU Lesser
 General Public License ever published by the Free Software Foundation.
  If the Library as you received it specifies that a proxy can decide
 whether future versions of the GNU Lesser General Public License shall
 apply, that proxy's public statement of acceptance of any version is
 permanent authorization for you to choose that version for the
 Library.
--- a/COPYING.MINPACK
+++ b/COPYING.MINPACK
@@ -1,51 +0,0 @@
 Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
 Redistribution and use in source and binary forms, with or
 without modification, are permitted provided that the
 following conditions are met:
 1. Redistributions of source code must retain the above
 copyright notice, this list of conditions and the following
 disclaimer.
 2. Redistributions in binary form must reproduce the above
 copyright notice, this list of conditions and the following
 disclaimer in the documentation and/or other materials
 provided with the distribution.
 3. The end-user documentation included with the
 redistribution, if any, must include the following
 acknowledgment:
   "This product includes software developed by the
   University of Chicago, as Operator of Argonne National
   Laboratory.
 Alternately, this acknowledgment may appear in the software
 itself, if and wherever such third-party acknowledgments
 normally appear.
 4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
 WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
 UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
 THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
 OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
 OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
 OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
 USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
 THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
 DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
 UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
 BE CORRECTED.
 5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
 HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
 ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
 INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
 ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
 PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
 SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
 (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
 EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
 POSSIBILITY OF SUCH LOSS OR DAMAGES.
--- a/COPYING.MPL2
+++ b/COPYING.MPL2
@@ -1,373 +0,0 @@
 Mozilla Public License Version 2.0
 ==================================
 1. Definitions
 --------------
 1.1. "Contributor"
    means each individual or legal entity that creates, contributes to
    the creation of, or owns Covered Software.
 1.2. "Contributor Version"
    means the combination of the Contributions of others (if any) used
    by a Contributor and that particular Contributor's Contribution.
 1.3. "Contribution"
    means Covered Software of a particular Contributor.
 1.4. "Covered Software"
    means Source Code Form to which the initial Contributor has attached
    the notice in Exhibit A, the Executable Form of such Source Code
    Form, and Modifications of such Source Code Form, in each case
    including portions thereof.
 1.5. "Incompatible With Secondary Licenses"
    means
    (a) that the initial Contributor has attached the notice described
        in Exhibit B to the Covered Software; or
    (b) that the Covered Software was made available under the terms of
        version 1.1 or earlier of the License, but not also under the
        terms of a Secondary License.
 1.6. "Executable Form"
    means any form of the work other than Source Code Form.
 1.7. "Larger Work"
    means a work that combines Covered Software with other material, in 
    a separate file or files, that is not Covered Software.
 1.8. "License"
    means this document.
 1.9. "Licensable"
    means having the right to grant, to the maximum extent possible,
    whether at the time of the initial grant or subsequently, any and
    all of the rights conveyed by this License.
 1.10. "Modifications"
    means any of the following:
    (a) any file in Source Code Form that results from an addition to,
        deletion from, or modification of the contents of Covered
        Software; or
    (b) any new file in Source Code Form that contains any Covered
        Software.
 1.11. "Patent Claims" of a Contributor
    means any patent claim(s), including without limitation, method,
    process, and apparatus claims, in any patent Licensable by such
    Contributor that would be infringed, but for the grant of the
    License, by the making, using, selling, offering for sale, having
    made, import, or transfer of either its Contributions or its
    Contributor Version.
 1.12. "Secondary License"
    means either the GNU General Public License, Version 2.0, the GNU
    Lesser General Public License, Version 2.1, the GNU Affero General
    Public License, Version 3.0, or any later versions of those
    licenses.
 1.13. "Source Code Form"
    means the form of the work preferred for making modifications.
 1.14. "You" (or "Your")
    means an individual or a legal entity exercising rights under this
    License. For legal entities, "You" includes any entity that
    controls, is controlled by, or is under common control with You. For
    purposes of this definition, "control" means (a) the power, direct
    or indirect, to cause the direction or management of such entity,
    whether by contract or otherwise, or (b) ownership of more than
    fifty percent (50%) of the outstanding shares or beneficial
    ownership of such entity.
 2. License Grants and Conditions
 --------------------------------
 2.1. Grants
 Each Contributor hereby grants You a world-wide, royalty-free,
 non-exclusive license:
 (a) under intellectual property rights (other than patent or trademark)
    Licensable by such Contributor to use, reproduce, make available,
    modify, display, perform, distribute, and otherwise exploit its
    Contributions, either on an unmodified basis, with Modifications, or
    as part of a Larger Work; and
 (b) under Patent Claims of such Contributor to make, use, sell, offer
    for sale, have made, import, and otherwise transfer either its
    Contributions or its Contributor Version.
 2.2. Effective Date
 The licenses granted in Section 2.1 with respect to any Contribution
 become effective for each Contribution on the date the Contributor first
 distributes such Contribution.
 2.3. Limitations on Grant Scope
 The licenses granted in this Section 2 are the only rights granted under
 this License. No additional rights or licenses will be implied from the
 distribution or licensing of Covered Software under this License.
 Notwithstanding Section 2.1(b) above, no patent license is granted by a
 Contributor:
 (a) for any code that a Contributor has removed from Covered Software;
    or
 (b) for infringements caused by: (i) Your and any other third party's
    modifications of Covered Software, or (ii) the combination of its
    Contributions with other software (except as part of its Contributor
    Version); or
 (c) under Patent Claims infringed by Covered Software in the absence of
    its Contributions.
 This License does not grant any rights in the trademarks, service marks,
 or logos of any Contributor (except as may be necessary to comply with
 the notice requirements in Section 3.4).
 2.4. Subsequent Licenses
 No Contributor makes additional grants as a result of Your choice to
 distribute the Covered Software under a subsequent version of this
 License (see Section 10.2) or under the terms of a Secondary License (if
 permitted under the terms of Section 3.3).
 2.5. Representation
 Each Contributor represents that the Contributor believes its
 Contributions are its original creation(s) or it has sufficient rights
 to grant the rights to its Contributions conveyed by this License.
 2.6. Fair Use
 This License is not intended to limit any rights You have under
 applicable copyright doctrines of fair use, fair dealing, or other
 equivalents.
 2.7. Conditions
 Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
 in Section 2.1.
 3. Responsibilities
 -------------------
 3.1. Distribution of Source Form
 All distribution of Covered Software in Source Code Form, including any
 Modifications that You create or to which You contribute, must be under
 the terms of this License. You must inform recipients that the Source
 Code Form of the Covered Software is governed by the terms of this
 License, and how they can obtain a copy of this License. You may not
 attempt to alter or restrict the recipients' rights in the Source Code
 Form.
 3.2. Distribution of Executable Form
 If You distribute Covered Software in Executable Form then:
 (a) such Covered Software must also be made available in Source Code
    Form, as described in Section 3.1, and You must inform recipients of
    the Executable Form how they can obtain a copy of such Source Code
    Form by reasonable means in a timely manner, at a charge no more
    than the cost of distribution to the recipient; and
 (b) You may distribute such Executable Form under the terms of this
    License, or sublicense it under different terms, provided that the
    license for the Executable Form does not attempt to limit or alter
    the recipients' rights in the Source Code Form under this License.
 3.3. Distribution of a Larger Work
 You may create and distribute a Larger Work under terms of Your choice,
 provided that You also comply with the requirements of this License for
 the Covered Software. If the Larger Work is a combination of Covered
 Software with a work governed by one or more Secondary Licenses, and the
 Covered Software is not Incompatible With Secondary Licenses, this
 License permits You to additionally distribute such Covered Software
 under the terms of such Secondary License(s), so that the recipient of
 the Larger Work may, at their option, further distribute the Covered
 Software under the terms of either this License or such Secondary
 License(s).
 3.4. Notices
 You may not remove or alter the substance of any license notices
 (including copyright notices, patent notices, disclaimers of warranty,
 or limitations of liability) contained within the Source Code Form of
 the Covered Software, except that You may alter any license notices to
 the extent required to remedy known factual inaccuracies.
 3.5. Application of Additional Terms
 You may choose to offer, and to charge a fee for, warranty, support,
 indemnity or liability obligations to one or more recipients of Covered
 Software. However, You may do so only on Your own behalf, and not on
 behalf of any Contributor. You must make it absolutely clear that any
 such warranty, support, indemnity, or liability obligation is offered by
 You alone, and You hereby agree to indemnify every Contributor for any
 liability incurred by such Contributor as a result of warranty, support,
 indemnity or liability terms You offer. You may include additional
 disclaimers of warranty and limitations of liability specific to any
 jurisdiction.
 4. Inability to Comply Due to Statute or Regulation
 ---------------------------------------------------
 If it is impossible for You to comply with any of the terms of this
 License with respect to some or all of the Covered Software due to
 statute, judicial order, or regulation then You must: (a) comply with
 the terms of this License to the maximum extent possible; and (b)
 describe the limitations and the code they affect. Such description must
 be placed in a text file included with all distributions of the Covered
 Software under this License. Except to the extent prohibited by statute
 or regulation, such description must be sufficiently detailed for a
 recipient of ordinary skill to be able to understand it.
 5. Termination
 --------------
 5.1. The rights granted under this License will terminate automatically
 if You fail to comply with any of its terms. However, if You become
 compliant, then the rights granted under this License from a particular
 Contributor are reinstated (a) provisionally, unless and until such
 Contributor explicitly and finally terminates Your grants, and (b) on an
 ongoing basis, if such Contributor fails to notify You of the
 non-compliance by some reasonable means prior to 60 days after You have
 come back into compliance. Moreover, Your grants from a particular
 Contributor are reinstated on an ongoing basis if such Contributor
 notifies You of the non-compliance by some reasonable means, this is the
 first time You have received notice of non-compliance with this License
 from such Contributor, and You become compliant prior to 30 days after
 Your receipt of the notice.
 5.2. If You initiate litigation against any entity by asserting a patent
 infringement claim (excluding declaratory judgment actions,
 counter-claims, and cross-claims) alleging that a Contributor Version
 directly or indirectly infringes any patent, then the rights granted to
 You by any and all Contributors for the Covered Software under Section
 2.1 of this License shall terminate.
 5.3. In the event of termination under Sections 5.1 or 5.2 above, all
 end user license agreements (excluding distributors and resellers) which
 have been validly granted by You or Your distributors under this License
 prior to termination shall survive termination.
 ************************************************************************
 *                                                                      *
 *  6. Disclaimer of Warranty                                           *
 *  -------------------------                                           *
 *                                                                      *
 *  Covered Software is provided under this License on an "as is"       *
 *  basis, without warranty of any kind, either expressed, implied, or  *
 *  statutory, including, without limitation, warranties that the       *
 *  Covered Software is free of defects, merchantable, fit for a        *
 *  particular purpose or non-infringing. The entire risk as to the     *
 *  quality and performance of the Covered Software is with You.        *
 *  Should any Covered Software prove defective in any respect, You     *
 *  (not any Contributor) assume the cost of any necessary servicing,   *
 *  repair, or correction. This disclaimer of warranty constitutes an   *
 *  essential part of this License. No use of any Covered Software is   *
 *  authorized under this License except under this disclaimer.         *
 *                                                                      *
 ************************************************************************
 ************************************************************************
 *                                                                      *
 *  7. Limitation of Liability                                          *
 *  --------------------------                                          *
 *                                                                      *
 *  Under no circumstances and under no legal theory, whether tort      *
 *  (including negligence), contract, or otherwise, shall any           *
 *  Contributor, or anyone who distributes Covered Software as          *
 *  permitted above, be liable to You for any direct, indirect,         *
 *  special, incidental, or consequential damages of any character      *
 *  including, without limitation, damages for lost profits, loss of    *
 *  goodwill, work stoppage, computer failure or malfunction, or any    *
 *  and all other commercial damages or losses, even if such party      *
 *  shall have been informed of the possibility of such damages. This   *
 *  limitation of liability shall not apply to liability for death or   *
 *  personal injury resulting from such party's negligence to the       *
 *  extent applicable law prohibits such limitation. Some               *
 *  jurisdictions do not allow the exclusion or limitation of           *
 *  incidental or consequential damages, so this exclusion and          *
 *  limitation may not apply to You.                                    *
 *                                                                      *
 ************************************************************************
 8. Litigation
 -------------
 Any litigation relating to this License may be brought only in the
 courts of a jurisdiction where the defendant maintains its principal
 place of business and such litigation shall be governed by laws of that
 jurisdiction, without reference to its conflict-of-law provisions.
 Nothing in this Section shall prevent a party's ability to bring
 cross-claims or counter-claims.
 9. Miscellaneous
 ----------------
 This License represents the complete agreement concerning the subject
 matter hereof. If any provision of this License is held to be
 unenforceable, such provision shall be reformed only to the extent
 necessary to make it enforceable. Any law or regulation which provides
 that the language of a contract shall be construed against the drafter
 shall not be used to construe this License against a Contributor.
 10. Versions of the License
 ---------------------------
 10.1. New Versions
 Mozilla Foundation is the license steward. Except as provided in Section
 10.3, no one other than the license steward has the right to modify or
 publish new versions of this License. Each version will be given a
 distinguishing version number.
 10.2. Effect of New Versions
 You may distribute the Covered Software under the terms of the version
 of the License under which You originally received the Covered Software,
 or under the terms of any subsequent version published by the license
 steward.
 10.3. Modified Versions
 If you create software not governed by this License, and you want to
 create a new license for such software, you may create and use a
 modified version of this License if you rename the license and remove
 any references to the name of the license steward (except to note that
 such modified license differs from this License).
 10.4. Distributing Source Code Form that is Incompatible With Secondary
 Licenses
 If You choose to distribute Source Code Form that is Incompatible With
 Secondary Licenses under the terms of this version of the License, the
 notice described in Exhibit B of this License must be attached.
 Exhibit A - Source Code Form License Notice
 -------------------------------------------
  This Source Code Form is subject to the terms of the Mozilla Public
  License, v. 2.0. If a copy of the MPL was not distributed with this
  file, You can obtain one at https://mozilla.org/MPL/2.0/.
 If it is not possible or desirable to put the notice in a particular
 file, then You may include the notice in a location (such as a LICENSE
 file in a relevant directory) where a recipient would be likely to look
 for such a notice.
 You may add additional accurate notices of copyright ownership.
 Exhibit B - "Incompatible With Secondary Licenses" Notice
 ---------------------------------------------------------
  This Source Code Form is "Incompatible With Secondary Licenses", as
  defined by the Mozilla Public License, v. 2.0.
--- a/COPYING.README
+++ b/COPYING.README
@@ -1,11 +0,0 @@
 Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
  http://www.mozilla.org/MPL/2.0/
  http://www.mozilla.org/MPL/2.0/FAQ.html
 Some files contain third-party code under BSD, LGPL, Apache, or other
 MPL2-compatible licenses, hence the other COPYING.* files here.
 Note that some optional external dependencies (e.g. FFTW, MPFR C++)
 are distributed under different licenses, including the GPL. Refer to
 the individual source files and their respective COPYING files for
 details.
--- a/CTestConfig.cmake
+++ b/CTestConfig.cmake
@@ -2,16 +2,12 @@
 ## Then modify the CMakeLists.txt file in the root directory of your
 ## project to incorporate the testing dashboard.
 ## # The following are required to uses Dart and the Cdash dashboard
-##   enable_testing()
+##   ENABLE_TESTING()
-##   include(CTest)
+##   INCLUDE(CTest)
 set(CTEST_PROJECT_NAME "Eigen")
 set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
 set(CTEST_DROP_METHOD "http")
-set(CTEST_DROP_SITE "my.cdash.org")
+set(CTEST_DROP_SITE "manao.inria.fr")
-set(CTEST_DROP_LOCATION "/submit.php?project=Eigen")
+set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
 set(CTEST_DROP_SITE_CDASH TRUE)
 #set(CTEST_PROJECT_SUBPROJECTS
 #Official
 #Unsupported
 #)
--- a/CTestCustom.cmake.in
+++ b/CTestCustom.cmake.in
@@ -1,4 +1,4 @@
-set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "2000")
+## A tribute to Dynamic!
-set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS   "2000")
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331")
-list(APPEND CTEST_CUSTOM_ERROR_EXCEPTION    @EIGEN_CTEST_ERROR_EXCEPTION@)
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331")
--- a/Eigen/AccelerateSupport
+++ b/Eigen/AccelerateSupport
@@ -1,52 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
 #define EIGEN_ACCELERATESUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 /** \ingroup Support_modules
 * \defgroup AccelerateSupport_Module AccelerateSupport module
 *
 * This module provides an interface to the Apple Accelerate library.
 * It provides the seven following main factorization classes:
 * - class AccelerateLLT: a Cholesky (LL^T) factorization.
 * - class AccelerateLDLT: the default LDL^T factorization.
 * - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
 * - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
 * - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
 * - class AccelerateQR: a QR factorization
 * - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
 *
 * \code
 * #include <Eigen/AccelerateSupport>
 * \endcode
 *
 * In order to use this module, the Accelerate headers must be accessible from
 * the include paths, and your binary must be linked to the Accelerate framework.
 * The Accelerate library is only available on Apple hardware.
 *
 * Note that many of the algorithms can be influenced by the UpLo template
 * argument. All matrices are assumed to be symmetric. For example, the following
 * creates an LDLT factorization where your matrix is symmetric (implicit) and
 * uses the lower triangle:
 *
 * \code
 * AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
 * \endcode
 */
 // IWYU pragma: begin_exports
 #include "src/AccelerateSupport/AccelerateSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_ACCELERATESUPPORT_MODULE_H
--- a/Eigen/Array
+++ b/Eigen/Array
@@ -0,0 +1,11 @@
 #ifndef EIGEN_ARRAY_MODULE_H
 #define EIGEN_ARRAY_MODULE_H
 // include Core first to handle Eigen2 support macros
 #include "Core"
 #ifndef EIGEN2_SUPPORT
  #error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core.
 #endif
 #endif // EIGEN_ARRAY_MODULE_H
--- a/Eigen/CMakeLists.txt
+++ b/Eigen/CMakeLists.txt
@@ -0,0 +1,19 @@
 include(RegexUtils)
 test_escape_string_as_regex()
 file(GLOB Eigen_directory_files "*")
 escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
 foreach(f ${Eigen_directory_files})
  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src")
    list(APPEND Eigen_directory_files_to_install ${f})
  endif()
 endforeach(f ${Eigen_directory_files})
 install(FILES
  ${Eigen_directory_files_to_install}
  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
  )
 add_subdirectory(src)
--- a/Eigen/Cholesky
+++ b/Eigen/Cholesky
@@ -1,41 +1,33 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_CHOLESKY_MODULE_H
 #define EIGEN_CHOLESKY_MODULE_H
 #include "Core"
 #include "Jacobi"
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup Cholesky_Module Cholesky module
  *
  *
  *
  * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
- * Those decompositions are also accessible via the following methods:
+  * Those decompositions are accessible via the following MatrixBase methods:
- *  - MatrixBase::llt()
+  *  - MatrixBase::llt(),
  *  - MatrixBase::ldlt()
 *  - SelfAdjointView::llt()
 *  - SelfAdjointView::ldlt()
  *
  * \code
  * #include <Eigen/Cholesky>
  * \endcode
  */
-// IWYU pragma: begin_exports
+#include "src/misc/Solve.h"
 #include "src/Cholesky/LLT.h"
 #include "src/Cholesky/LDLT.h"
-#ifdef EIGEN_USE_LAPACKE
+
-#include "src/misc/lapacke_helpers.h"
+} // namespace Eigen
 #include "src/Cholesky/LLT_LAPACKE.h"
 #endif
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_CHOLESKY_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/CholmodSupport
+++ b/Eigen/CholmodSupport
@@ -1,48 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
 #define EIGEN_CHOLMODSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include <cholmod.h>
 /** \ingroup Support_modules
 * \defgroup CholmodSupport_Module CholmodSupport module
 *
 * This module provides an interface to the Cholmod library which is part of the <a
 * href="http://www.suitesparse.com">suitesparse</a> package. It provides the two following main factorization classes:
 * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
 * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of
 * the underlying factorization method (supernodal or simplicial).
 *
 * For the sake of completeness, this module also propose the two following classes:
 * - class CholmodSimplicialLLT
 * - class CholmodSimplicialLDLT
 * Note that these classes do not bring any particular advantage compared to the built-in
 * SimplicialLLT and SimplicialLDLT factorization classes.
 *
 * \code
 * #include <Eigen/CholmodSupport>
 * \endcode
 *
 * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be
 * linked to the cholmod library and its dependencies. The dependencies depend on how cholmod has been compiled. For a
 * cmake based project, you can use our FindCholmod.cmake module to help you in this task.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/CholmodSupport/CholmodSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_CHOLMODSUPPORT_MODULE_H
--- a/Eigen/Core
+++ b/Eigen/Core
@@ -4,58 +4,127 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
-#ifndef EIGEN_CORE_MODULE_H
+#ifndef EIGEN_CORE_H
-#define EIGEN_CORE_MODULE_H
+#define EIGEN_CORE_H
-// Eigen version information.
+// first thing Eigen does: stop the compiler from committing suicide
 #include "Version"
 // first thing Eigen does: stop the compiler from reporting useless warnings.
 #include "src/Core/util/DisableStupidWarnings.h"
 // then include this file where all our macros are defined. It's really important to do it first because
-// it's where we do all the compiler/OS/arch detections and define most defaults.
+// it's where we do all the alignment settings (platform detection and honoring the user's will if he
 // defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
 #include "src/Core/util/Macros.h"
-// This detects SSE/AVX/NEON/etc. and configure alignment settings
+// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
-#include "src/Core/util/ConfigureVectorization.h"
+// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
-
+#if !EIGEN_ALIGN
-// We need cuda_runtime.h/hip_runtime.h to ensure that
+  #ifndef EIGEN_DONT_VECTORIZE
-// the EIGEN_USING_STD macro works properly on the device side
+    #define EIGEN_DONT_VECTORIZE
-#if defined(EIGEN_CUDACC)
+  #endif
 #include <cuda_runtime.h>
 #elif defined(EIGEN_HIPCC)
 #include <hip/hip_runtime.h>
 #endif
-#ifdef EIGEN_EXCEPTIONS
+#ifdef _MSC_VER
-#include <new>
+  #include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
  #if (_MSC_VER >= 1500) // 2008 or later
    // Remember that usage of defined() in a #define is undefined by the standard.
    // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
    #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
      #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
    #endif
  #endif
 #else
  // Remember that usage of defined() in a #define is undefined by the standard
  #if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
    #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
  #endif
 #endif
-// Prevent ICC from specializing std::complex operators that silently fail
+#ifndef EIGEN_DONT_VECTORIZE
-// on device. This allows us to use our own device-compatible specializations
+
-// instead.
+  #if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
-#if EIGEN_COMP_ICC && defined(EIGEN_GPU_COMPILE_PHASE) && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
+
-#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
+    // Defines symbols for compile-time detection of which instructions are
    // used.
    // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
    #define EIGEN_VECTORIZE
    #define EIGEN_VECTORIZE_SSE
    #define EIGEN_VECTORIZE_SSE2
    // Detect sse3/ssse3/sse4:
    // gcc and icc defines __SSE3__, ...
    // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
    // want to force the use of those instructions with msvc.
    #ifdef __SSE3__
      #define EIGEN_VECTORIZE_SSE3
    #endif
-#include <complex>
+    #ifdef __SSSE3__
-
+      #define EIGEN_VECTORIZE_SSSE3
-// this include file manages BLAS and MKL related macros
+    #endif
-// and inclusion of their respective header files
+    #ifdef __SSE4_1__
-#include "src/Core/util/MKL_support.h"
+      #define EIGEN_VECTORIZE_SSE4_1
-#include "src/Core/util/AOCL_Support.h"
+    #endif
-
+    #ifdef __SSE4_2__
-
+      #define EIGEN_VECTORIZE_SSE4_2
 #if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
 #define EIGEN_HAS_GPU_FP16
    #endif
-#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
+    // include files
-#define EIGEN_HAS_GPU_BF16
+
    // This extern "C" works around a MINGW-w64 compilation issue
    // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
    // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
    // However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
    // with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
    // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
    // notice that since these are C headers, the extern "C" is theoretically needed anyways.
    extern "C" {
      #include <emmintrin.h>
      #include <xmmintrin.h>
      #ifdef  EIGEN_VECTORIZE_SSE3
      #include <pmmintrin.h>
      #endif
      #ifdef EIGEN_VECTORIZE_SSSE3
      #include <tmmintrin.h>
      #endif
      #ifdef EIGEN_VECTORIZE_SSE4_1
      #include <smmintrin.h>
      #endif
      #ifdef EIGEN_VECTORIZE_SSE4_2
      #include <nmmintrin.h>
      #endif
    } // end extern "C"
  #elif defined __ALTIVEC__
    #define EIGEN_VECTORIZE
    #define EIGEN_VECTORIZE_ALTIVEC
    #include <altivec.h>
    // We need to #undef all these ugly tokens defined in <altivec.h>
    // => use __vector instead of vector
    #undef bool
    #undef vector
    #undef pixel
  #elif defined  __ARM_NEON__
    #define EIGEN_VECTORIZE
    #define EIGEN_VECTORIZE_NEON
    #include <arm_neon.h>
  #endif
 #endif
 #if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
@@ -63,11 +132,11 @@
 #endif
 #ifdef EIGEN_HAS_OPENMP
 #include <atomic>
 #include <omp.h>
 #endif
-#if !EIGEN_COMP_ARM
+// MSVC for windows mobile does not have the errno.h file
 #if !(defined(_MSC_VER) && defined(_WIN32_WCE)) && !defined(__ARMCC_VERSION)
 #define EIGEN_HAS_ERRNO
 #endif
@@ -77,11 +146,10 @@
 #include <cstddef>
 #include <cstdlib>
 #include <cmath>
 #include <complex>
 #include <cassert>
 #include <functional>
 #ifndef EIGEN_NO_IO
 #include <sstream>
 #include <iosfwd>
 #endif
 #include <cstring>
 #include <string>
 #include <limits>
@@ -89,71 +157,85 @@
 // for min/max:
 #include <algorithm>
 #include <array>
 #include <memory>
 #include <vector>
 // for std::is_nothrow_move_assignable
 #include <type_traits>
 // for std::this_thread::yield().
 #if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
 #include <thread>
 #endif
 // for __cpp_lib feature test macros
 #if defined(__has_include) && __has_include(<version>)
 #include <version>
 #endif
 // for std::bit_cast()
 #if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
 #include <bit>
 #endif
 // for outputting debug info
 #ifdef EIGEN_DEBUG_ASSIGN
 #include <iostream>
 #endif
 // required for __cpuid, needs to be included after cmath
-// also required for _BitScanReverse on Windows on ARM
+#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64))
 #if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64)
  #include <intrin.h>
 #endif
-// Required for querying cache sizes on Linux and macOS.
+#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
-#if EIGEN_OS_LINUX
+  #define EIGEN_EXCEPTIONS
 #include <unistd.h>
 #elif EIGEN_OS_MAC
 #include <sys/types.h>
 #include <sys/sysctl.h>
 #endif
-#if defined(EIGEN_USE_SYCL)
+#ifdef EIGEN_EXCEPTIONS
-#undef min
+  #include <new>
 #undef max
 #undef isnan
 #undef isinf
 #undef isfinite
 #include <CL/sycl.hpp>
 #include <map>
 #include <thread>
 #include <utility>
 #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
 #define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
 #endif
 #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
 #define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
 #endif
 #endif
 // defined in bits/termios.h
 #undef B0
 /** \brief Namespace containing all symbols from the %Eigen library. */
 namespace Eigen {
-using std::size_t;
+inline static const char *SimdInstructionSetsInUse(void) {
-using std::ptrdiff_t;
+#if defined(EIGEN_VECTORIZE_SSE4_2)
  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
 #elif defined(EIGEN_VECTORIZE_SSE4_1)
  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
 #elif defined(EIGEN_VECTORIZE_SSSE3)
  return "SSE, SSE2, SSE3, SSSE3";
 #elif defined(EIGEN_VECTORIZE_SSE3)
  return "SSE, SSE2, SSE3";
 #elif defined(EIGEN_VECTORIZE_SSE2)
  return "SSE, SSE2";
 #elif defined(EIGEN_VECTORIZE_ALTIVEC)
  return "AltiVec";
 #elif defined(EIGEN_VECTORIZE_NEON)
  return "ARM NEON";
 #else
  return "None";
 #endif
 }
-}  // namespace Eigen
+#define STAGE10_FULL_EIGEN2_API             10
 #define STAGE20_RESOLVE_API_CONFLICTS       20
 #define STAGE30_FULL_EIGEN3_API             30
 #define STAGE40_FULL_EIGEN3_STRICTNESS      40
 #define STAGE99_NO_EIGEN2_SUPPORT           99
 #if   defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
  #define EIGEN2_SUPPORT
  #define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
 #elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
  #define EIGEN2_SUPPORT
  #define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
 #elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
  #define EIGEN2_SUPPORT
  #define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
 #elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
  #define EIGEN2_SUPPORT
  #define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
 #elif defined EIGEN2_SUPPORT
  // default to stage 3, that's what it's always meant
  #define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
  #define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
 #else
  #define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
 #endif
 #ifdef EIGEN2_SUPPORT
 #undef minor
 #endif
 // we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
 // ensure QNX/QCC support
 using std::size_t;
 // gcc 4.6.0 wants std:: for ptrdiff_t 
 using std::ptrdiff_t;
 /** \defgroup Core_Module Core module
  * This is the main module of Eigen providing dense matrix and vector support
@@ -165,249 +247,86 @@ using std::ptrdiff_t;
  * \endcode
  */
 #ifdef EIGEN_USE_LAPACKE
 #ifdef EIGEN_USE_MKL
 #include "mkl_lapacke.h"
 #elif defined(EIGEN_LAPACKE_SYSTEM)
 #include <lapacke.h>
 #else
 #include "src/misc/lapacke.h"
 #endif
 #endif
 // IWYU pragma: begin_exports
 #include "src/Core/util/Constants.h"
 #include "src/Core/util/Meta.h"
 #include "src/Core/util/Assert.h"
 #include "src/Core/util/ForwardDeclarations.h"
-#include "src/Core/util/StaticAssert.h"
+#include "src/Core/util/Meta.h"
 #include "src/Core/util/XprHelper.h"
 #include "src/Core/util/StaticAssert.h"
 #include "src/Core/util/Memory.h"
 #include "src/Core/util/IntegralConstant.h"
 #include "src/Core/util/Serializer.h"
 #include "src/Core/util/SymbolicIndex.h"
 #include "src/Core/util/EmulateArray.h"
 #include "src/Core/util/MoreMeta.h"
 #include "src/Core/NumTraits.h"
 #include "src/Core/MathFunctions.h"
 #include "src/Core/RandomImpl.h"
 #include "src/Core/GenericPacketMath.h"
 #include "src/Core/MathFunctionsImpl.h"
 #include "src/Core/arch/Default/ConjHelper.h"
 // Generic half float support
 #include "src/Core/arch/Default/Half.h"
 #include "src/Core/arch/Default/BFloat16.h"
 #include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
-#if defined(EIGEN_VECTORIZE_GENERIC) && !defined(EIGEN_DONT_VECTORIZE)
+#if defined EIGEN_VECTORIZE_SSE
 #include "src/Core/arch/clang/PacketMath.h"
 #include "src/Core/arch/clang/TypeCasting.h"
 #include "src/Core/arch/clang/Complex.h"
 #include "src/Core/arch/clang/Reductions.h"
 #include "src/Core/arch/clang/MathFunctions.h"
 #else
 #if defined EIGEN_VECTORIZE_AVX512
  #include "src/Core/arch/SSE/PacketMath.h"
 #include "src/Core/arch/SSE/Reductions.h"
 #include "src/Core/arch/AVX/PacketMath.h"
 #include "src/Core/arch/AVX/Reductions.h"
 #include "src/Core/arch/AVX512/PacketMath.h"
 #include "src/Core/arch/AVX512/Reductions.h"
 #if defined EIGEN_VECTORIZE_AVX512FP16
 #include "src/Core/arch/AVX512/PacketMathFP16.h"
 #endif
 #include "src/Core/arch/SSE/TypeCasting.h"
 #include "src/Core/arch/AVX/TypeCasting.h"
 #include "src/Core/arch/AVX512/TypeCasting.h"
 #if defined EIGEN_VECTORIZE_AVX512FP16
 #include "src/Core/arch/AVX512/TypeCastingFP16.h"
 #endif
 #include "src/Core/arch/SSE/Complex.h"
 #include "src/Core/arch/AVX/Complex.h"
 #include "src/Core/arch/AVX512/Complex.h"
 #include "src/Core/arch/SSE/MathFunctions.h"
 #include "src/Core/arch/AVX/MathFunctions.h"
 #include "src/Core/arch/AVX512/MathFunctions.h"
 #if defined EIGEN_VECTORIZE_AVX512FP16
 #include "src/Core/arch/AVX512/MathFunctionsFP16.h"
 #endif
 #include "src/Core/arch/AVX512/TrsmKernel.h"
 #elif defined EIGEN_VECTORIZE_AVX
 // Use AVX for floats and doubles, SSE for integers
 #include "src/Core/arch/SSE/PacketMath.h"
 #include "src/Core/arch/SSE/Reductions.h"
 #include "src/Core/arch/SSE/TypeCasting.h"
 #include "src/Core/arch/SSE/Complex.h"
 #include "src/Core/arch/AVX/PacketMath.h"
 #include "src/Core/arch/AVX/Reductions.h"
 #include "src/Core/arch/AVX/TypeCasting.h"
 #include "src/Core/arch/AVX/Complex.h"
 #include "src/Core/arch/SSE/MathFunctions.h"
 #include "src/Core/arch/AVX/MathFunctions.h"
 #elif defined EIGEN_VECTORIZE_SSE
 #include "src/Core/arch/SSE/PacketMath.h"
 #include "src/Core/arch/SSE/Reductions.h"
 #include "src/Core/arch/SSE/TypeCasting.h"
  #include "src/Core/arch/SSE/MathFunctions.h"
  #include "src/Core/arch/SSE/Complex.h"
-#endif
+#elif defined EIGEN_VECTORIZE_ALTIVEC
 #if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
  #include "src/Core/arch/AltiVec/PacketMath.h"
 #include "src/Core/arch/AltiVec/TypeCasting.h"
 #include "src/Core/arch/AltiVec/MathFunctions.h"
  #include "src/Core/arch/AltiVec/Complex.h"
 #elif defined EIGEN_VECTORIZE_NEON
  #include "src/Core/arch/NEON/PacketMath.h"
 #include "src/Core/arch/NEON/TypeCasting.h"
 #include "src/Core/arch/NEON/MathFunctions.h"
  #include "src/Core/arch/NEON/Complex.h"
 #elif defined EIGEN_VECTORIZE_LSX
 #include "src/Core/arch/LSX/PacketMath.h"
 #include "src/Core/arch/LSX/TypeCasting.h"
 #include "src/Core/arch/LSX/MathFunctions.h"
 #include "src/Core/arch/LSX/Complex.h"
 #elif defined EIGEN_VECTORIZE_SVE
 #include "src/Core/arch/SVE/PacketMath.h"
 #include "src/Core/arch/SVE/TypeCasting.h"
 #include "src/Core/arch/SVE/MathFunctions.h"
 #elif defined EIGEN_VECTORIZE_RVV10
 #include "src/Core/arch/RVV10/PacketMath.h"
 #include "src/Core/arch/RVV10/PacketMath4.h"
 #include "src/Core/arch/RVV10/PacketMath2.h"
 #include "src/Core/arch/RVV10/TypeCasting.h"
 #include "src/Core/arch/RVV10/MathFunctions.h"
 #if defined EIGEN_VECTORIZE_RVV10FP16
 #include "src/Core/arch/RVV10/PacketMathFP16.h"
 #endif
 #if defined EIGEN_VECTORIZE_RVV10BF16
 #include "src/Core/arch/RVV10/PacketMathBF16.h"
 #endif
 #elif defined EIGEN_VECTORIZE_ZVECTOR
 #include "src/Core/arch/ZVector/PacketMath.h"
 #include "src/Core/arch/ZVector/MathFunctions.h"
 #include "src/Core/arch/ZVector/Complex.h"
 #elif defined EIGEN_VECTORIZE_MSA
 #include "src/Core/arch/MSA/PacketMath.h"
 #include "src/Core/arch/MSA/MathFunctions.h"
 #include "src/Core/arch/MSA/Complex.h"
 #elif defined EIGEN_VECTORIZE_HVX
 #include "src/Core/arch/HVX/PacketMath.h"
 #endif
 #if defined EIGEN_VECTORIZE_GPU
 #include "src/Core/arch/GPU/PacketMath.h"
 #include "src/Core/arch/GPU/MathFunctions.h"
 #include "src/Core/arch/GPU/TypeCasting.h"
 #endif
 #if defined(EIGEN_USE_SYCL)
 #include "src/Core/arch/SYCL/InteropHeaders.h"
 #if !defined(EIGEN_DONT_VECTORIZE_SYCL)
 #include "src/Core/arch/SYCL/PacketMath.h"
 #include "src/Core/arch/SYCL/MathFunctions.h"
 #include "src/Core/arch/SYCL/TypeCasting.h"
 #endif
 #endif
 #endif  // #ifndef EIGEN_VECTORIZE_GENERIC
 #include "src/Core/arch/Default/Settings.h"
 // This file provides generic implementations valid for scalar as well
 #include "src/Core/arch/Default/GenericPacketMathFunctions.h"
-#include "src/Core/functors/TernaryFunctors.h"
+#include "src/Core/Functors.h"
 #include "src/Core/functors/BinaryFunctors.h"
 #include "src/Core/functors/UnaryFunctors.h"
 #include "src/Core/functors/NullaryFunctors.h"
 #include "src/Core/functors/StlFunctors.h"
 #include "src/Core/functors/AssignmentFunctors.h"
 // Specialized functors for GPU.
 #ifdef EIGEN_GPUCC
 #include "src/Core/arch/GPU/Complex.h"
 #endif
 // Specializations of vectorized activation functions for NEON.
 #ifdef EIGEN_VECTORIZE_NEON
 #include "src/Core/arch/NEON/UnaryFunctors.h"
 #endif
 #include "src/Core/util/IndexedViewHelper.h"
 #include "src/Core/util/ReshapedHelper.h"
 #include "src/Core/ArithmeticSequence.h"
 #ifndef EIGEN_NO_IO
 #include "src/Core/IO.h"
 #endif
 #include "src/Core/DenseCoeffsBase.h"
 #include "src/Core/DenseBase.h"
 #include "src/Core/MatrixBase.h"
 #include "src/Core/EigenBase.h"
-#include "src/Core/Product.h"
+#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
-#include "src/Core/CoreEvaluators.h"
+                                // at least confirmed with Doxygen 1.5.5 and 1.5.6
 #include "src/Core/AssignEvaluator.h"
 #include "src/Core/RealView.h"
  #include "src/Core/Assign.h"
 #endif
 #include "src/Core/ArrayBase.h"
 #include "src/Core/util/BlasUtil.h"
 #include "src/Core/DenseStorage.h"
 #include "src/Core/NestByValue.h"
-
+#include "src/Core/ForceAlignedAccess.h"
 #include "src/Core/ReturnByValue.h"
 #include "src/Core/NoAlias.h"
 #include "src/Core/PlainObjectBase.h"
 #include "src/Core/Matrix.h"
 #include "src/Core/Array.h"
 #include "src/Core/Fill.h"
 #include "src/Core/CwiseTernaryOp.h"
 #include "src/Core/CwiseBinaryOp.h"
 #include "src/Core/CwiseUnaryOp.h"
 #include "src/Core/CwiseNullaryOp.h"
 #include "src/Core/CwiseUnaryView.h"
 #include "src/Core/SelfCwiseBinaryOp.h"
 #include "src/Core/InnerProduct.h"
 #include "src/Core/Dot.h"
 #include "src/Core/StableNorm.h"
 #include "src/Core/Stride.h"
 #include "src/Core/MapBase.h"
 #include "src/Core/Stride.h"
 #include "src/Core/Map.h"
 #include "src/Core/Ref.h"
 #include "src/Core/Block.h"
 #include "src/Core/VectorBlock.h"
 #include "src/Core/IndexedView.h"
 #include "src/Core/Reshaped.h"
 #include "src/Core/Transpose.h"
 #include "src/Core/DiagonalMatrix.h"
 #include "src/Core/Diagonal.h"
 #include "src/Core/DiagonalProduct.h"
 #include "src/Core/SkewSymmetricMatrix3.h"
 #include "src/Core/Redux.h"
 #include "src/Core/Visitor.h"
 #include "src/Core/FindCoeff.h"
 #include "src/Core/Fuzzy.h"
 #include "src/Core/Swap.h"
 #include "src/Core/CommaInitializer.h"
 #include "src/Core/GeneralProduct.h"
 #include "src/Core/Solve.h"
 #include "src/Core/Inverse.h"
 #include "src/Core/SolverBase.h"
 #include "src/Core/PermutationMatrix.h"
 #include "src/Core/Transpositions.h"
 #include "src/Core/Redux.h"
 #include "src/Core/Visitor.h"
 #include "src/Core/Fuzzy.h"
 #include "src/Core/IO.h"
 #include "src/Core/Swap.h"
 #include "src/Core/CommaInitializer.h"
 #include "src/Core/Flagged.h"
 #include "src/Core/ProductBase.h"
 #include "src/Core/Product.h"
 #include "src/Core/TriangularMatrix.h"
 #include "src/Core/SelfAdjointView.h"
-#include "src/Core/products/GeneralBlockPanelKernel.h"
+#include "src/Core/SolveTriangular.h"
 #include "src/Core/DeviceWrapper.h"
 #ifdef EIGEN_GEMM_THREADPOOL
 #include "ThreadPool"
 #endif
 #include "src/Core/products/Parallelizer.h"
-#include "src/Core/ProductEvaluators.h"
+#include "src/Core/products/CoeffBasedProduct.h"
 #include "src/Core/products/GeneralBlockPanelKernel.h"
 #include "src/Core/products/GeneralMatrixVector.h"
 #include "src/Core/products/GeneralMatrixMatrix.h"
 #include "src/Core/SolveTriangular.h"
 #include "src/Core/products/GeneralMatrixMatrixTriangular.h"
 #include "src/Core/products/SelfadjointMatrixVector.h"
 #include "src/Core/products/SelfadjointMatrixMatrix.h"
@@ -418,56 +337,24 @@ using std::ptrdiff_t;
 #include "src/Core/products/TriangularSolverMatrix.h"
 #include "src/Core/products/TriangularSolverVector.h"
 #include "src/Core/BandMatrix.h"
 #include "src/Core/CoreIterators.h"
 #include "src/Core/ConditionEstimator.h"
 #if !defined(EIGEN_VECTORIZE_GENERIC)
 #if defined(EIGEN_VECTORIZE_VSX)
 #include "src/Core/arch/AltiVec/MatrixProduct.h"
 #elif defined EIGEN_VECTORIZE_NEON
 #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
 #elif defined EIGEN_VECTORIZE_LSX
 #include "src/Core/arch/LSX/GeneralBlockPanelKernel.h"
 #elif defined EIGEN_VECTORIZE_RVV10
 #include "src/Core/arch/RVV10/GeneralBlockPanelKernel.h"
 #endif
 #if defined(EIGEN_VECTORIZE_AVX512)
 #include "src/Core/arch/AVX512/GemmKernel.h"
 #endif
 #endif
 #include "src/Core/BooleanRedux.h"
 #include "src/Core/Select.h"
 #include "src/Core/VectorwiseOp.h"
 #include "src/Core/PartialReduxEvaluator.h"
 #include "src/Core/Random.h"
 #include "src/Core/Replicate.h"
 #include "src/Core/Reverse.h"
 #include "src/Core/ArrayBase.h"
 #include "src/Core/ArrayWrapper.h"
 #include "src/Core/StlIterators.h"
-#ifdef EIGEN_USE_BLAS
+} // namespace Eigen
 #include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
 #include "src/Core/products/GeneralMatrixVector_BLAS.h"
 #include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
 #include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
 #include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
 #include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
 #include "src/Core/products/TriangularMatrixVector_BLAS.h"
 #include "src/Core/products/TriangularSolverMatrix_BLAS.h"
 #endif  // EIGEN_USE_BLAS
 #ifdef EIGEN_USE_MKL_VML
 #include "src/Core/Assign_MKL.h"
 #endif
 #ifdef EIGEN_USE_AOCL_VML
 #include "src/Core/Assign_AOCL.h"
 #endif
 #include "src/Core/GlobalFunctions.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
-#endif  // EIGEN_CORE_MODULE_H
+#ifdef EIGEN2_SUPPORT
 #include "Eigen2Support"
 #endif
 #endif // EIGEN_CORE_H
--- a/Eigen/Dense
+++ b/Eigen/Dense
@@ -1,13 +1,3 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_DENSE_MODULE_H
 #define EIGEN_DENSE_MODULE_H
 #include "Core"
 #include "LU"
 #include "Cholesky"
@@ -15,5 +5,3 @@
 #include "SVD"
 #include "Geometry"
 #include "Eigenvalues"
 #endif  // EIGEN_DENSE_MODULE_H
--- a/Eigen/Eigen
+++ b/Eigen/Eigen
@@ -1,14 +1,2 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_EIGEN_MODULE_H
 #define EIGEN_EIGEN_MODULE_H
 #include "Dense"
-#include "Sparse"
+//#include "Sparse"
 #endif  // EIGEN_EIGEN_MODULE_H
--- a/Eigen/Eigen2Support
+++ b/Eigen/Eigen2Support
@@ -0,0 +1,82 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
 // License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN2SUPPORT_H
 #define EIGEN2SUPPORT_H
 #if (!defined(EIGEN2_SUPPORT)) || (!defined(EIGEN_CORE_H))
 #error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
 #endif
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup Eigen2Support_Module Eigen2 support module
  * This module provides a couple of deprecated functions improving the compatibility with Eigen2.
  *
  * To use it, define EIGEN2_SUPPORT before including any Eigen header
  * \code
  * #define EIGEN2_SUPPORT
  * \endcode
  *
  */
 #include "src/Eigen2Support/Macros.h"
 #include "src/Eigen2Support/Memory.h"
 #include "src/Eigen2Support/Meta.h"
 #include "src/Eigen2Support/Lazy.h"
 #include "src/Eigen2Support/Cwise.h"
 #include "src/Eigen2Support/CwiseOperators.h"
 #include "src/Eigen2Support/TriangularSolver.h"
 #include "src/Eigen2Support/Block.h"
 #include "src/Eigen2Support/VectorBlock.h"
 #include "src/Eigen2Support/Minor.h"
 #include "src/Eigen2Support/MathFunctions.h"
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 // Eigen2 used to include iostream
 #include<iostream>
 #define USING_PART_OF_NAMESPACE_EIGEN \
 EIGEN_USING_MATRIX_TYPEDEFS \
 using Eigen::Matrix; \
 using Eigen::MatrixBase; \
 using Eigen::ei_random; \
 using Eigen::ei_real; \
 using Eigen::ei_imag; \
 using Eigen::ei_conj; \
 using Eigen::ei_abs; \
 using Eigen::ei_abs2; \
 using Eigen::ei_sqrt; \
 using Eigen::ei_exp; \
 using Eigen::ei_log; \
 using Eigen::ei_sin; \
 using Eigen::ei_cos;
 #endif // EIGEN2SUPPORT_H
--- a/Eigen/Eigenvalues
+++ b/Eigen/Eigenvalues
@@ -1,23 +1,20 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_EIGENVALUES_MODULE_H
 #define EIGEN_EIGENVALUES_MODULE_H
 #include "Core"
 #include "Cholesky"
 #include "LU"
 #include "Geometry"
 #include "Sparse"  // Needed by ComplexQZ.
 #include "src/Core/util/DisableStupidWarnings.h"
 #include "Cholesky"
 #include "Jacobi"
 #include "Householder"
 #include "LU"
 namespace Eigen {
 /** \defgroup Eigenvalues_Module Eigenvalues module
  *
  *
  *
  * This module mainly provides various eigenvalue solvers.
  * This module also provides some MatrixBase methods, including:
@@ -29,7 +26,6 @@
  * \endcode
  */
 // IWYU pragma: begin_exports
 #include "src/Eigenvalues/Tridiagonalization.h"
 #include "src/Eigenvalues/RealSchur.h"
 #include "src/Eigenvalues/EigenSolver.h"
@@ -38,24 +34,11 @@
 #include "src/Eigenvalues/HessenbergDecomposition.h"
 #include "src/Eigenvalues/ComplexSchur.h"
 #include "src/Eigenvalues/ComplexEigenSolver.h"
 #include "src/Eigenvalues/RealQZ.h"
 #include "src/Eigenvalues/ComplexQZ.h"
 #include "src/Eigenvalues/GeneralizedEigenSolver.h"
 #include "src/Eigenvalues/MatrixBaseEigenvalues.h"
-#ifdef EIGEN_USE_LAPACKE
+
-#ifdef EIGEN_USE_MKL
+} // namespace Eigen
 #include "mkl_lapacke.h"
 #elif defined(EIGEN_LAPACKE_SYSTEM)
 #include <lapacke.h>
 #else
 #include "src/misc/lapacke.h"
 #endif
 #include "src/Eigenvalues/RealSchur_LAPACKE.h"
 #include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
 #include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
 #endif
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_EIGENVALUES_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/Geometry
+++ b/Eigen/Geometry
@@ -1,39 +1,41 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_GEOMETRY_MODULE_H
 #define EIGEN_GEOMETRY_MODULE_H
 #include "Core"
 #include "SVD"
 #include "LU"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include "SVD"
 #include "LU"
 #include <limits>
 #ifndef M_PI
 #define M_PI 3.14159265358979323846
 #endif
 namespace Eigen {
 /** \defgroup Geometry_Module Geometry module
  *
  *
  *
  * This module provides support for:
  *  - fixed-size homogeneous transformations
  *  - translation, scaling, 2D and 3D rotations
- *  - \link Quaternion quaternions \endlink
+  *  - quaternions
- *  - cross products (\ref MatrixBase::cross(), \ref MatrixBase::cross3())
+  *  - \ref MatrixBase::cross() "cross product"
- *  - orthogonal vector generation (MatrixBase::unitOrthogonal)
+  *  - \ref MatrixBase::unitOrthogonal() "orthognal vector generation"
- *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
+  *  - some linear components: parametrized-lines and hyperplanes
- *  - \link AlignedBox axis aligned bounding boxes \endlink
+  *
 *  - \link umeyama() least-square transformation fitting \endlink
  * \code
  * #include <Eigen/Geometry>
  * \endcode
  */
 // IWYU pragma: begin_exports
 #include "src/Geometry/OrthoMethods.h"
 #include "src/Geometry/EulerAngles.h"
 #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
  #include "src/Geometry/Homogeneous.h"
  #include "src/Geometry/RotationBase.h"
  #include "src/Geometry/Rotation2D.h"
@@ -47,15 +49,19 @@
  #include "src/Geometry/AlignedBox.h"
  #include "src/Geometry/Umeyama.h"
-#ifndef EIGEN_VECTORIZE_GENERIC
+  #if defined EIGEN_VECTORIZE_SSE
-// TODO(rmlarsen): Make these work with generic vectorization if possible.
+    #include "src/Geometry/arch/Geometry_SSE.h"
 // Use the SSE optimized version whenever possible.
 #if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
 #include "src/Geometry/arch/Geometry_SIMD.h"
  #endif
 #endif
-// IWYU pragma: end_exports
+
 #ifdef EIGEN2_SUPPORT
 #include "src/Eigen2Support/Geometry/All.h"
 #endif
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_GEOMETRY_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/Householder
+++ b/Eigen/Householder
@@ -1,10 +1,3 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_HOUSEHOLDER_MODULE_H
 #define EIGEN_HOUSEHOLDER_MODULE_H
@@ -12,6 +5,8 @@
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup Householder_Module Householder module
  * This module provides Householder transformations.
  *
@@ -20,12 +15,13 @@
  * \endcode
  */
 // IWYU pragma: begin_exports
 #include "src/Householder/Householder.h"
 #include "src/Householder/BlockHouseholder.h"
 #include "src/Householder/HouseholderSequence.h"
-// IWYU pragma: end_exports
+#include "src/Householder/BlockHouseholder.h"
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_HOUSEHOLDER_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/IterativeLinearSolvers
+++ b/Eigen/IterativeLinearSolvers
@@ -1,52 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
 #define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
 #include "SparseCore"
 #include "OrderingMethods"
 #include "src/Core/util/DisableStupidWarnings.h"
 /**
  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
  *
  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a
  squared matrix, usually very large and sparse.
  * Those solvers are accessible via the following classes:
  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
  *  - BiCGSTAB for general square matrices.
  *
  * These iterative solvers are associated with some preconditioners:
  *  - IdentityPreconditioner - not really useful
  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
  *
  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport,
  UmfPackSupport, SuperLUSupport, AccelerateSupport.
  *
    \code
    #include <Eigen/IterativeLinearSolvers>
    \endcode
  */
 // IWYU pragma: begin_exports
 #include "src/IterativeLinearSolvers/SolveWithGuess.h"
 #include "src/IterativeLinearSolvers/IterativeSolverBase.h"
 #include "src/IterativeLinearSolvers/BasicPreconditioners.h"
 #include "src/IterativeLinearSolvers/ConjugateGradient.h"
 #include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
 #include "src/IterativeLinearSolvers/BiCGSTAB.h"
 #include "src/IterativeLinearSolvers/IncompleteLUT.h"
 #include "src/IterativeLinearSolvers/IncompleteCholesky.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
--- a/Eigen/Jacobi
+++ b/Eigen/Jacobi
@@ -1,10 +1,3 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_JACOBI_MODULE_H
 #define EIGEN_JACOBI_MODULE_H
@@ -12,6 +5,8 @@
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup Jacobi_Module Jacobi module
  * This module provides Jacobi and Givens rotations.
  *
@@ -24,10 +19,12 @@
  *  - MatrixBase::applyOnTheRight().
  */
 // IWYU pragma: begin_exports
 #include "src/Jacobi/Jacobi.h"
-// IWYU pragma: end_exports
+
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_JACOBI_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/KLUSupport
+++ b/Eigen/KLUSupport
@@ -1,43 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_KLUSUPPORT_MODULE_H
 #define EIGEN_KLUSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 extern "C" {
 #include <btf.h>
 #include <klu.h>
 }
 /** \ingroup Support_modules
 * \defgroup KLUSupport_Module KLUSupport module
 *
 * This module provides an interface to the KLU library which is part of the <a
 * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
 * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
 *
 * \code
 * #include <Eigen/KLUSupport>
 * \endcode
 *
 * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must
 * be linked to the klu library and its dependencies. The dependencies depend on how KLU has been compiled. For a
 * cmake based project, you can use our FindKLU.cmake module to help you in this task.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/KLUSupport/KLUSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_KLUSUPPORT_MODULE_H
--- a/Eigen/LU
+++ b/Eigen/LU
@@ -1,10 +1,3 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_LU_MODULE_H
 #define EIGEN_LU_MODULE_H
@@ -12,6 +5,8 @@
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup LU_Module LU module
  * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
  * This module defines the following MatrixBase methods:
@@ -23,27 +18,25 @@
  * \endcode
  */
-// IWYU pragma: begin_exports
+#include "src/misc/Solve.h"
 #include "src/misc/Kernel.h"
 #include "src/misc/Image.h"
 #include "src/misc/RankRevealingBase.h"
 #include "src/LU/FullPivLU.h"
 #include "src/LU/PartialPivLU.h"
 #ifdef EIGEN_USE_LAPACKE
 #include "src/misc/lapacke_helpers.h"
 #include "src/LU/PartialPivLU_LAPACKE.h"
 #endif
 #include "src/LU/Determinant.h"
-#include "src/LU/InverseImpl.h"
+#include "src/LU/Inverse.h"
-#ifndef EIGEN_VECTORIZE_GENERIC
+#if defined EIGEN_VECTORIZE_SSE
-// TODO(rmlarsen): Make these work with generic vectorization if possible.
+  #include "src/LU/arch/Inverse_SSE.h"
 #if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
 #include "src/LU/arch/InverseSize4.h"
 #endif
 #ifdef EIGEN2_SUPPORT
  #include "src/Eigen2Support/LU.h"
 #endif
-// IWYU pragma: end_exports
+
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_LU_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/LeastSquares
+++ b/Eigen/LeastSquares
@@ -0,0 +1,36 @@
 #ifndef EIGEN_REGRESSION_MODULE_H
 #define EIGEN_REGRESSION_MODULE_H
 #ifndef EIGEN2_SUPPORT
 #error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT)
 #endif
 // exclude from normal eigen3-only documentation
 #ifdef EIGEN2_SUPPORT
 #include "Core"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include "Eigenvalues"
 #include "Geometry"
 namespace Eigen {
 /** \defgroup LeastSquares_Module LeastSquares module
  * This module provides linear regression and related features.
  *
  * \code
  * #include <Eigen/LeastSquares>
  * \endcode
  */
 #include "src/Eigen2Support/LeastSquares.h"
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN2_SUPPORT
 #endif // EIGEN_REGRESSION_MODULE_H
--- a/Eigen/MetisSupport
+++ b/Eigen/MetisSupport
@@ -1,35 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_METISSUPPORT_MODULE_H
 #define EIGEN_METISSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 extern "C" {
 #include <metis.h>
 }
 /** \ingroup Support_modules
 * \defgroup MetisSupport_Module MetisSupport module
 *
 * \code
 * #include <Eigen/MetisSupport>
 * \endcode
 * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
 * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
 */
 // IWYU pragma: begin_exports
 #include "src/MetisSupport/MetisSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_METISSUPPORT_MODULE_H
--- a/Eigen/OrderingMethods
+++ b/Eigen/OrderingMethods
@@ -1,73 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_ORDERINGMETHODS_MODULE_H
 #define EIGEN_ORDERINGMETHODS_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 /**
 * \defgroup OrderingMethods_Module OrderingMethods module
 *
 * This module is currently for internal use only
 *
 * It defines various built-in and external ordering methods for sparse matrices.
 * They are typically used to reduce the number of elements during
 * the sparse matrix decomposition (LLT, LU, QR).
 * Precisely, in a preprocessing step, a permutation matrix P is computed using
 * those ordering methods and applied to the columns of the matrix.
 * Using for instance the sparse Cholesky decomposition, it is expected that
 * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
 *
 *
 * Usage :
 * \code
 * #include <Eigen/OrderingMethods>
 * \endcode
 *
 * A simple usage is as a template parameter in the sparse decomposition classes :
 *
 * \code
 * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
 * \endcode
 *
 * \code
 * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
 * \endcode
 *
 * It is possible as well to call directly a particular ordering method for your own purpose,
 * \code
 * AMDOrdering<int> ordering;
 * PermutationMatrix<Dynamic, Dynamic, int> perm;
 * SparseMatrix<double> A;
 * //Fill the matrix ...
 *
 * ordering(A, perm); // Call AMD
 * \endcode
 *
 * \note Some of these methods (like AMD or METIS), need the sparsity pattern
 * of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
 * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
 * If your matrix is already symmetric (at least in structure), you can avoid that
 * by calling the method with a SelfAdjointView type.
 *
 * \code
 *  // Call the ordering on the pattern of the lower triangular matrix A
 * ordering(A.selfadjointView<Lower>(), perm);
 * \endcode
 */
 // IWYU pragma: begin_exports
 #include "src/OrderingMethods/Amd.h"
 #include "src/OrderingMethods/Ordering.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_ORDERINGMETHODS_MODULE_H
--- a/Eigen/PaStiXSupport
+++ b/Eigen/PaStiXSupport
@@ -1,51 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_PASTIXSUPPORT_MODULE_H
 #define EIGEN_PASTIXSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 extern "C" {
 #include <pastix_nompi.h>
 #include <pastix.h>
 }
 #ifdef complex
 #undef complex
 #endif
 /** \ingroup Support_modules
 * \defgroup PaStiXSupport_Module PaStiXSupport module
 *
 * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
 * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
 * It provides the two following main factorization classes:
 * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
 * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
 * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
 *
 * \code
 * #include <Eigen/PaStiXSupport>
 * \endcode
 *
 * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be
 * linked to the PaSTiX library and its dependencies. This wrapper requires PaStiX version 5.x compiled without MPI
 * support. The dependencies depend on how PaSTiX has been compiled. For a cmake based project, you can use our
 * FindPaSTiX.cmake module to help you in this task.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/PaStiXSupport/PaStiXSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_PASTIXSUPPORT_MODULE_H
--- a/Eigen/PardisoSupport
+++ b/Eigen/PardisoSupport
@@ -1,38 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_PARDISOSUPPORT_MODULE_H
 #define EIGEN_PARDISOSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include <mkl_pardiso.h>
 /** \ingroup Support_modules
 * \defgroup PardisoSupport_Module PardisoSupport module
 *
 * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
 *
 * \code
 * #include <Eigen/PardisoSupport>
 * \endcode
 *
 * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be
 * linked to the MKL library and its dependencies. See this \ref TopicUsingIntelMKL "page" for more information on
 * MKL-Eigen integration.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/PardisoSupport/PardisoSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_PARDISOSUPPORT_MODULE_H
--- a/Eigen/QR
+++ b/Eigen/QR
@@ -1,47 +1,45 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_QR_MODULE_H
 #define EIGEN_QR_MODULE_H
 #include "Core"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include "Cholesky"
 #include "Jacobi"
 #include "Householder"
-#include "src/Core/util/DisableStupidWarnings.h"
+namespace Eigen {
 /** \defgroup QR_Module QR module
  *
  *
  *
  * This module provides various QR decompositions
  * This module also provides some MatrixBase methods, including:
- *  - MatrixBase::householderQr()
+  *  - MatrixBase::qr(),
 *  - MatrixBase::colPivHouseholderQr()
 *  - MatrixBase::fullPivHouseholderQr()
  *
  * \code
  * #include <Eigen/QR>
  * \endcode
  */
-#include "src/misc/RankRevealingBase.h"
+#include "src/misc/Solve.h"
 // IWYU pragma: begin_exports
 #include "src/QR/HouseholderQR.h"
 #include "src/QR/FullPivHouseholderQR.h"
 #include "src/QR/ColPivHouseholderQR.h"
-#include "src/QR/CompleteOrthogonalDecomposition.h"
+
-#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN2_SUPPORT
-#include "src/misc/lapacke_helpers.h"
+#include "src/Eigen2Support/QR.h"
 #include "src/QR/HouseholderQR_LAPACKE.h"
 #include "src/QR/ColPivHouseholderQR_LAPACKE.h"
 #endif
-// IWYU pragma: end_exports
+
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #ifdef EIGEN2_SUPPORT
 #include "Eigenvalues"
 #endif
 #endif // EIGEN_QR_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/QtAlignedMalloc
+++ b/Eigen/QtAlignedMalloc
@@ -1,9 +1,3 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_QTMALLOC_MODULE_H
 #define EIGEN_QTMALLOC_MODULE_H
@@ -14,13 +8,20 @@
 #include "src/Core/util/DisableStupidWarnings.h"
-inline void *qMalloc(std::size_t size) { return Eigen::internal::aligned_malloc(size); }
+void *qMalloc(size_t size)
 {
  return Eigen::internal::aligned_malloc(size);
 }
-inline void qFree(void *ptr) { Eigen::internal::aligned_free(ptr); }
+void qFree(void *ptr)
 {
  Eigen::internal::aligned_free(ptr);
 }
-inline void *qRealloc(void *ptr, std::size_t size) {
+void *qRealloc(void *ptr, size_t size)
 {
  void* newPtr = Eigen::internal::aligned_malloc(size);
-  std::memcpy(newPtr, ptr, size);
+  memcpy(newPtr, ptr, size);
  Eigen::internal::aligned_free(ptr);
  return newPtr;
 }
@@ -30,3 +31,4 @@ inline void *qRealloc(void *ptr, std::size_t size) {
 #endif
 #endif // EIGEN_QTMALLOC_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/SPQRSupport
+++ b/Eigen/SPQRSupport
@@ -1,41 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPQRSUPPORT_MODULE_H
 #define EIGEN_SPQRSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include "SuiteSparseQR.hpp"
 /** \ingroup Support_modules
 * \defgroup SPQRSupport_Module SuiteSparseQR module
 *
 * This module provides an interface to the SPQR library, which is part of the <a
 * href="http://www.suitesparse.com">suitesparse</a> package.
 *
 * \code
 * #include <Eigen/SPQRSupport>
 * \endcode
 *
 * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be
 * linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...). For a cmake based project, you can use
 * our FindSPQR.cmake and FindCholmod.Cmake modules
 *
 */
 #include "CholmodSupport"
 // IWYU pragma: begin_exports
 #include "src/SPQRSupport/SuiteSparseQRSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SPQRSUPPORT_MODULE_H
--- a/Eigen/SVD
+++ b/Eigen/SVD
@@ -1,53 +1,38 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SVD_MODULE_H
 #define EIGEN_SVD_MODULE_H
 #include "QR"
 #include "Householder"
 #include "Jacobi"
 #include "src/Core/util/DisableStupidWarnings.h"
 namespace Eigen {
 /** \defgroup SVD_Module SVD module
  *
  *
  *
  * This module provides SVD decomposition for matrices (both real and complex).
- * Two decomposition algorithms are provided:
+  * This decomposition is accessible via the following MatrixBase method:
 *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very
 * slow for larger ones.
 *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast
 * for large problems. These decompositions are accessible via the respective classes and following MatrixBase methods:
  *  - MatrixBase::jacobiSvd()
 *  - MatrixBase::bdcSvd()
  *
  * \code
  * #include <Eigen/SVD>
  * \endcode
  */
-// IWYU pragma: begin_exports
+#include "src/misc/Solve.h"
 #include "src/SVD/UpperBidiagonalization.h"
 #include "src/SVD/SVDBase.h"
 #include "src/SVD/JacobiSVD.h"
-#include "src/SVD/BDCSVD.h"
+#include "src/SVD/UpperBidiagonalization.h"
-#ifdef EIGEN_USE_LAPACKE
+
-#ifdef EIGEN_USE_MKL
+#ifdef EIGEN2_SUPPORT
-#include "mkl_lapacke.h"
+#include "src/Eigen2Support/SVD.h"
 #elif defined(EIGEN_LAPACKE_SYSTEM)
 #include <lapacke.h>
 #else
 #include "src/misc/lapacke.h"
 #endif
-#ifndef EIGEN_USE_LAPACKE_STRICT
+
-#include "src/SVD/JacobiSVD_LAPACKE.h"
+} // namespace Eigen
 #endif
 #include "src/SVD/BDCSVD_LAPACKE.h"
 #endif
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_SVD_MODULE_H
 /* vim: set filetype=cpp et sw=2 ts=2 ai: */
--- a/Eigen/Sparse
+++ b/Eigen/Sparse
@@ -1,33 +1,69 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPARSE_MODULE_H
 #define EIGEN_SPARSE_MODULE_H
-/** \defgroup Sparse_Module Sparse meta-module
+#include "Core"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include <vector>
 #include <map>
 #include <cstdlib>
 #include <cstring>
 #include <algorithm>
 #ifdef EIGEN2_SUPPORT
 #define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
 #endif
 #ifndef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
 #error The sparse module API is not stable yet. To use it anyway, please define the EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET preprocessor token.
 #endif
 namespace Eigen {
 /** \defgroup Sparse_Module Sparse module
  *
  * Meta-module including all related modules:
  * - \ref SparseCore_Module
  * - \ref OrderingMethods_Module
  * - \ref SparseCholesky_Module
  * - \ref SparseLU_Module
  * - \ref SparseQR_Module
  * - \ref IterativeLinearSolvers_Module
  *
-    \code
+  *
-    #include <Eigen/Sparse>
+  * See the \ref TutorialSparse "Sparse tutorial"
-    \endcode
+  *
  * \code
  * #include <Eigen/Sparse>
  * \endcode
  */
-#include "SparseCore"
+/** The type used to identify a general sparse storage. */
-#include "OrderingMethods"
+struct Sparse {};
-#include "SparseCholesky"
+
-#include "SparseLU"
+#include "src/Sparse/SparseUtil.h"
-#include "SparseQR"
+#include "src/Sparse/SparseMatrixBase.h"
-#include "IterativeLinearSolvers"
+#include "src/Sparse/CompressedStorage.h"
 #include "src/Sparse/AmbiVector.h"
 #include "src/Sparse/SparseMatrix.h"
 #include "src/Sparse/DynamicSparseMatrix.h"
 #include "src/Sparse/MappedSparseMatrix.h"
 #include "src/Sparse/SparseVector.h"
 #include "src/Sparse/CoreIterators.h"
 #include "src/Sparse/SparseBlock.h"
 #include "src/Sparse/SparseTranspose.h"
 #include "src/Sparse/SparseCwiseUnaryOp.h"
 #include "src/Sparse/SparseCwiseBinaryOp.h"
 #include "src/Sparse/SparseDot.h"
 #include "src/Sparse/SparseAssign.h"
 #include "src/Sparse/SparseRedux.h"
 #include "src/Sparse/SparseFuzzy.h"
 #include "src/Sparse/SparseProduct.h"
 #include "src/Sparse/SparseSparseProduct.h"
 #include "src/Sparse/SparseDenseProduct.h"
 #include "src/Sparse/SparseDiagonalProduct.h"
 #include "src/Sparse/SparseTriangularView.h"
 #include "src/Sparse/SparseSelfAdjointView.h"
 #include "src/Sparse/TriangularSolver.h"
 #include "src/Sparse/SparseView.h"
 } // namespace Eigen
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif // EIGEN_SPARSE_MODULE_H
--- a/Eigen/SparseCholesky
+++ b/Eigen/SparseCholesky
@@ -1,40 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPARSECHOLESKY_MODULE_H
 #define EIGEN_SPARSECHOLESKY_MODULE_H
 #include "SparseCore"
 #include "OrderingMethods"
 #include "src/Core/util/DisableStupidWarnings.h"
 /**
 * \defgroup SparseCholesky_Module SparseCholesky module
 *
 * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian)
 * matrices. Those decompositions are accessible via the following classes:
 *  - SimplicialLLt,
 *  - SimplicialLDLt
 *
 * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
 *
 * \code
 * #include <Eigen/SparseCholesky>
 * \endcode
 */
 // IWYU pragma: begin_exports
 #include "src/SparseCholesky/SimplicialCholesky.h"
 #include "src/SparseCholesky/SimplicialCholesky_impl.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SPARSECHOLESKY_MODULE_H
--- a/Eigen/SparseCore
+++ b/Eigen/SparseCore
@@ -1,66 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPARSECORE_MODULE_H
 #define EIGEN_SPARSECORE_MODULE_H
 #include "Core"
 #include "src/Core/util/DisableStupidWarnings.h"
 #include <map>
 #include <numeric>
 /**
 * \defgroup SparseCore_Module SparseCore module
 *
 * This module provides a sparse matrix representation, and basic associated matrix manipulations
 * and operations.
 *
 * See the \ref TutorialSparse "Sparse tutorial"
 *
 * \code
 * #include <Eigen/SparseCore>
 * \endcode
 *
 * This module depends on: Core.
 */
 // IWYU pragma: begin_exports
 #include "src/SparseCore/SparseUtil.h"
 #include "src/SparseCore/SparseMatrixBase.h"
 #include "src/SparseCore/SparseAssign.h"
 #include "src/SparseCore/CompressedStorage.h"
 #include "src/SparseCore/AmbiVector.h"
 #include "src/SparseCore/SparseCompressedBase.h"
 #include "src/SparseCore/SparseMatrix.h"
 #include "src/SparseCore/SparseMap.h"
 #include "src/SparseCore/SparseVector.h"
 #include "src/SparseCore/SparseRef.h"
 #include "src/SparseCore/SparseCwiseUnaryOp.h"
 #include "src/SparseCore/SparseCwiseBinaryOp.h"
 #include "src/SparseCore/SparseTranspose.h"
 #include "src/SparseCore/SparseBlock.h"
 #include "src/SparseCore/SparseDot.h"
 #include "src/SparseCore/SparseRedux.h"
 #include "src/SparseCore/SparseView.h"
 #include "src/SparseCore/SparseDiagonalProduct.h"
 #include "src/SparseCore/ConservativeSparseSparseProduct.h"
 #include "src/SparseCore/SparseSparseProductWithPruning.h"
 #include "src/SparseCore/SparseProduct.h"
 #include "src/SparseCore/SparseDenseProduct.h"
 #include "src/SparseCore/SparseSelfAdjointView.h"
 #include "src/SparseCore/SparseTriangularView.h"
 #include "src/SparseCore/TriangularSolver.h"
 #include "src/SparseCore/SparsePermutation.h"
 #include "src/SparseCore/SparseFuzzy.h"
 #include "src/SparseCore/SparseSolverBase.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SPARSECORE_MODULE_H
--- a/Eigen/SparseLU
+++ b/Eigen/SparseLU
@@ -1,50 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
 // Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPARSELU_MODULE_H
 #define EIGEN_SPARSELU_MODULE_H
 #include "SparseCore"
 /**
 * \defgroup SparseLU_Module SparseLU module
 * This module defines a supernodal factorization of general sparse matrices.
 * The code is fully optimized for supernode-panel updates with specialized kernels.
 * Please, see the documentation of the SparseLU class for more details.
 */
 // Ordering interface
 #include "OrderingMethods"
 #include "src/Core/util/DisableStupidWarnings.h"
 // IWYU pragma: begin_exports
 #include "src/SparseLU/SparseLU_Structs.h"
 #include "src/SparseLU/SparseLU_SupernodalMatrix.h"
 #include "src/SparseLU/SparseLUImpl.h"
 #include "src/SparseCore/SparseColEtree.h"
 #include "src/SparseLU/SparseLU_Memory.h"
 #include "src/SparseLU/SparseLU_heap_relax_snode.h"
 #include "src/SparseLU/SparseLU_relax_snode.h"
 #include "src/SparseLU/SparseLU_pivotL.h"
 #include "src/SparseLU/SparseLU_panel_dfs.h"
 #include "src/SparseLU/SparseLU_kernel_bmod.h"
 #include "src/SparseLU/SparseLU_panel_bmod.h"
 #include "src/SparseLU/SparseLU_column_dfs.h"
 #include "src/SparseLU/SparseLU_column_bmod.h"
 #include "src/SparseLU/SparseLU_copy_to_ucol.h"
 #include "src/SparseLU/SparseLU_pruneL.h"
 #include "src/SparseLU/SparseLU_Utils.h"
 #include "src/SparseLU/SparseLU.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SPARSELU_MODULE_H
--- a/Eigen/SparseQR
+++ b/Eigen/SparseQR
@@ -1,38 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SPARSEQR_MODULE_H
 #define EIGEN_SPARSEQR_MODULE_H
 #include "SparseCore"
 #include "OrderingMethods"
 #include "src/Core/util/DisableStupidWarnings.h"
 /** \defgroup SparseQR_Module SparseQR module
 * \brief Provides QR decomposition for sparse matrices
 *
 * This module provides a simplicial version of the left-looking Sparse QR decomposition.
 * The columns of the input matrix should be reordered to limit the fill-in during the
 * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
 * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
 * of built-in and external ordering methods.
 *
 * \code
 * #include <Eigen/SparseQR>
 * \endcode
 *
 *
 */
 // IWYU pragma: begin_exports
 #include "src/SparseCore/SparseColEtree.h"
 #include "src/SparseQR/SparseQR.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SPARSEQR_MODULE_H
--- a/Eigen/StdDeque
+++ b/Eigen/StdDeque
@@ -4,9 +4,24 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_STDDEQUE_MODULE_H
 #define EIGEN_STDDEQUE_MODULE_H
@@ -14,16 +29,13 @@
 #include "Core"
 #include <deque>
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
 #define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
 #else
 // IWYU pragma: begin_exports
 #include "src/StlSupport/StdDeque.h"
 // IWYU pragma: end_exports
 #endif
--- a/Eigen/StdList
+++ b/Eigen/StdList
@@ -3,9 +3,24 @@
 //
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_STDLIST_MODULE_H
 #define EIGEN_STDLIST_MODULE_H
@@ -13,16 +28,13 @@
 #include "Core"
 #include <list>
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */    
    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
 #define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
 #else
 // IWYU pragma: begin_exports
 #include "src/StlSupport/StdList.h"
 // IWYU pragma: end_exports
 #endif
--- a/Eigen/StdVector
+++ b/Eigen/StdVector
@@ -4,9 +4,24 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_STDVECTOR_MODULE_H
 #define EIGEN_STDVECTOR_MODULE_H
@@ -14,16 +29,13 @@
 #include "Core"
 #include <vector>
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
 #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
 #else
 // IWYU pragma: begin_exports
 #include "src/StlSupport/StdVector.h"
 // IWYU pragma: end_exports
 #endif
--- a/Eigen/SuperLUSupport
+++ b/Eigen/SuperLUSupport
@@ -1,71 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
 #define EIGEN_SUPERLUSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 #ifdef EMPTY
 #define EIGEN_EMPTY_WAS_ALREADY_DEFINED
 #endif
 // Required by SuperLU headers, which expect int_t to be defined as a global typedef.
 typedef int int_t;
 #include <slu_Cnames.h>
 #include <supermatrix.h>
 #include <slu_util.h>
 // slu_util.h defines a preprocessor token named EMPTY which is really polluting,
 // so we remove it in favor of a SUPERLU_EMPTY token.
 // If EMPTY was already defined then we don't undef it.
 #if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
 #undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
 #elif defined(EMPTY)
 #undef EMPTY
 #endif
 #define SUPERLU_EMPTY (-1)
 namespace Eigen {
 struct SluMatrix;
 }
 /** \ingroup Support_modules
 * \defgroup SuperLUSupport_Module SuperLUSupport module
 *
 * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
 * It provides the following factorization class:
 * - class SuperLU: a supernodal sequential LU factorization.
 * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative
 * methods).
 *
 * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
 *
 * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined
 * because it is too polluting.
 *
 * \code
 * #include <Eigen/SuperLUSupport>
 * \endcode
 *
 * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be
 * linked to the superlu library and its dependencies. The dependencies depend on how superlu has been compiled. For a
 * cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/SuperLUSupport/SuperLUSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_SUPERLUSUPPORT_MODULE_H
--- a/Eigen/ThreadPool
+++ b/Eigen/ThreadPool
@@ -1,80 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_THREADPOOL_MODULE_H
 #define EIGEN_THREADPOOL_MODULE_H
 #include "Core"
 #include "src/Core/util/DisableStupidWarnings.h"
 /** \defgroup ThreadPool_Module ThreadPool Module
 *
 * This module provides 2 threadpool implementations
 *  - a simple reference implementation
 *  - a faster non blocking implementation
 *
 * \code
 * #include <Eigen/ThreadPool>
 * \endcode
 */
 #include <cstddef>
 #include <cstring>
 #include <ctime>
 #include <vector>
 #include <atomic>
 #include <condition_variable>
 #include <deque>
 #include <mutex>
 #include <thread>
 #include <functional>
 #include <memory>
 #include <utility>
 // There are non-parenthesized calls to "max" in the  <unordered_map> header,
 // which trigger a check in test/main.h causing compilation to fail.
 // We work around the check here by removing the check for max in
 // the case where we have to emulate thread_local.
 #ifdef max
 #undef max
 #endif
 #include <unordered_map>
 #include "src/Core/util/Meta.h"
 #include "src/Core/util/MaxSizeVector.h"
 #ifndef EIGEN_MUTEX
 #define EIGEN_MUTEX std::mutex
 #endif
 #ifndef EIGEN_MUTEX_LOCK
 #define EIGEN_MUTEX_LOCK std::unique_lock<std::mutex>
 #endif
 #ifndef EIGEN_CONDVAR
 #define EIGEN_CONDVAR std::condition_variable
 #endif
 // IWYU pragma: begin_exports
 #include "src/ThreadPool/ThreadLocal.h"
 #include "src/ThreadPool/ThreadYield.h"
 #include "src/ThreadPool/ThreadCancel.h"
 #include "src/ThreadPool/EventCount.h"
 #include "src/ThreadPool/RunQueue.h"
 #include "src/ThreadPool/ThreadPoolInterface.h"
 #include "src/ThreadPool/ThreadEnvironment.h"
 #include "src/ThreadPool/Barrier.h"
 #include "src/ThreadPool/NonBlockingThreadPool.h"
 #include "src/ThreadPool/CoreThreadPoolDevice.h"
 #include "src/ThreadPool/ForkJoin.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_THREADPOOL_MODULE_H
--- a/Eigen/UmfPackSupport
+++ b/Eigen/UmfPackSupport
@@ -1,42 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
 #define EIGEN_UMFPACKSUPPORT_MODULE_H
 #include "SparseCore"
 #include "src/Core/util/DisableStupidWarnings.h"
 extern "C" {
 #include <umfpack.h>
 }
 /** \ingroup Support_modules
 * \defgroup UmfPackSupport_Module UmfPackSupport module
 *
 * This module provides an interface to the UmfPack library which is part of the <a
 * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
 * - class UmfPackLU: a multifrontal sequential LU factorization.
 *
 * \code
 * #include <Eigen/UmfPackSupport>
 * \endcode
 *
 * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be
 * linked to the umfpack library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
 * cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
 *
 */
 // IWYU pragma: begin_exports
 #include "src/UmfPackSupport/UmfPackSupport.h"
 // IWYU pragma: end_exports
 #include "src/Core/util/ReenableStupidWarnings.h"
 #endif  // EIGEN_UMFPACKSUPPORT_MODULE_H
--- a/Eigen/Version
+++ b/Eigen/Version
@@ -1,21 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_VERSION_H
 #define EIGEN_VERSION_H
 // The "WORLD" version will forever remain "3" for the "Eigen3" library.
 #define EIGEN_WORLD_VERSION 3
 // As of Eigen3 5.0.0, we have moved to Semantic Versioning (semver.org).
 #define EIGEN_MAJOR_VERSION 5
 #define EIGEN_MINOR_VERSION 0
 #define EIGEN_PATCH_VERSION 1
 #define EIGEN_PRERELEASE_VERSION "dev"
 #define EIGEN_BUILD_VERSION "master"
 #define EIGEN_VERSION_STRING "5.0.1-dev+master"
 #endif  // EIGEN_VERSION_H
--- a/Eigen/src/AccelerateSupport/AccelerateSupport.h
+++ b/Eigen/src/AccelerateSupport/AccelerateSupport.h
@@ -1,423 +0,0 @@
 #ifndef EIGEN_ACCELERATESUPPORT_H
 #define EIGEN_ACCELERATESUPPORT_H
 #include <Accelerate/Accelerate.h>
 #include <Eigen/Sparse>
 namespace Eigen {
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 class AccelerateImpl;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateLLT
 * \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
 */
 template <typename MatrixType, int UpLo = Lower>
 using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateLDLT
 * \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
 */
 template <typename MatrixType, int UpLo = Lower>
 using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateLDLTUnpivoted
 * \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
 */
 template <typename MatrixType, int UpLo = Lower>
 using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateLDLTSBK
 * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static
 * pivoting
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
 */
 template <typename MatrixType, int UpLo = Lower>
 using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateLDLTTPP
 * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
 */
 template <typename MatrixType, int UpLo = Lower>
 using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateQR
 * \brief A QR factorization and solver based on Accelerate
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateQR
 */
 template <typename MatrixType>
 using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
 /** \ingroup AccelerateSupport_Module
 * \typedef AccelerateCholeskyAtA
 * \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
 *
 * \warning Only single and double precision real scalar types are supported by Accelerate
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 *
 * \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
 */
 template <typename MatrixType>
 using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
 namespace internal {
 template <typename T>
 struct AccelFactorizationDeleter {
  void operator()(T* sym) const {
    if (sym) {
      SparseCleanup(*sym);
      delete sym;
      sym = nullptr;
    }
  }
 };
 template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
 struct SparseTypesTraitBase {
  typedef DenseVecT AccelDenseVector;
  typedef DenseMatT AccelDenseMatrix;
  typedef SparseMatT AccelSparseMatrix;
  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
  typedef NumFactT NumericFactorization;
  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
 };
 template <typename Scalar>
 struct SparseTypesTrait {};
 template <>
 struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
                                                       SparseOpaqueFactorization_Double> {};
 template <>
 struct SparseTypesTrait<float>
    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
 };
 }  // end namespace internal
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
 protected:
  using Base = SparseSolverBase<AccelerateImpl>;
  using Base::derived;
  using Base::m_isInitialized;
 public:
  using Base::_solve_impl;
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
  enum { UpLo = UpLo_ };
  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
  AccelerateImpl() {
    m_isInitialized = false;
    auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
    if (check_flag_set(UpLo_, Symmetric)) {
      m_sparseKind = SparseSymmetric;
      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
    } else if (check_flag_set(UpLo_, UnitLower)) {
      m_sparseKind = SparseUnitTriangular;
      m_triType = SparseLowerTriangle;
    } else if (check_flag_set(UpLo_, UnitUpper)) {
      m_sparseKind = SparseUnitTriangular;
      m_triType = SparseUpperTriangle;
    } else if (check_flag_set(UpLo_, StrictlyLower)) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseLowerTriangle;
    } else if (check_flag_set(UpLo_, StrictlyUpper)) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseUpperTriangle;
    } else if (check_flag_set(UpLo_, Lower)) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseLowerTriangle;
    } else if (check_flag_set(UpLo_, Upper)) {
      m_sparseKind = SparseTriangular;
      m_triType = SparseUpperTriangle;
    } else {
      m_sparseKind = SparseOrdinary;
      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
    }
    m_order = SparseOrderDefault;
  }
  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
  ~AccelerateImpl() {}
  inline Index cols() const { return m_nCols; }
  inline Index rows() const { return m_nRows; }
  ComputationInfo info() const {
    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
    return m_info;
  }
  void analyzePattern(const MatrixType& matrix);
  void factorize(const MatrixType& matrix);
  void compute(const MatrixType& matrix);
  template <typename Rhs, typename Dest>
  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
  /** Sets the ordering algorithm to use. */
  void setOrder(SparseOrder_t order) { m_order = order; }
 private:
  template <typename T>
  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
    const Index nColumnsStarts = a.cols() + 1;
    columnStarts.resize(nColumnsStarts);
    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
    SparseAttributes_t attributes{};
    attributes.transpose = false;
    attributes.triangle = m_triType;
    attributes.kind = m_sparseKind;
    SparseMatrixStructure structure{};
    structure.attributes = attributes;
    structure.rowCount = static_cast<int>(a.rows());
    structure.columnCount = static_cast<int>(a.cols());
    structure.blockSize = 1;
    structure.columnStarts = columnStarts.data();
    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
    A.structure = structure;
    A.data = const_cast<T*>(a.valuePtr());
  }
  void doAnalysis(AccelSparseMatrix& A) {
    m_numericFactorization.reset(nullptr);
    SparseSymbolicFactorOptions opts{};
    opts.control = SparseDefaultControl;
    opts.orderMethod = m_order;
    opts.order = nullptr;
    opts.ignoreRowsAndColumns = nullptr;
    opts.malloc = malloc;
    opts.free = free;
    opts.reportError = nullptr;
    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
    SparseStatus_t status = m_symbolicFactorization->status;
    updateInfoStatus(status);
    if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
  }
  void doFactorization(AccelSparseMatrix& A) {
    SparseStatus_t status = SparseStatusReleased;
    if (m_symbolicFactorization) {
      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
      status = m_numericFactorization->status;
      if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
    }
    updateInfoStatus(status);
  }
 protected:
  void updateInfoStatus(SparseStatus_t status) const {
    switch (status) {
      case SparseStatusOK:
        m_info = Success;
        break;
      case SparseFactorizationFailed:
      case SparseMatrixIsSingular:
        m_info = NumericalIssue;
        break;
      case SparseInternalError:
      case SparseParameterError:
      case SparseStatusReleased:
      default:
        m_info = InvalidInput;
        break;
    }
  }
  mutable ComputationInfo m_info;
  Index m_nRows, m_nCols;
  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
  SparseKind_t m_sparseKind;
  SparseTriangle_t m_triType;
  SparseOrder_t m_order;
 };
 /** Computes the symbolic and numeric decomposition of matrix \a a */
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
  m_nRows = a.rows();
  m_nCols = a.cols();
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
  buildAccelSparseMatrix(a, A, columnStarts);
  doAnalysis(A);
  if (m_symbolicFactorization) doFactorization(A);
  m_isInitialized = true;
 }
 /** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
 *
 * This function is particularly useful when solving for several problems having the same structure.
 *
 * \sa factorize()
 */
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
  m_nRows = a.rows();
  m_nCols = a.cols();
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
  buildAccelSparseMatrix(a, A, columnStarts);
  doAnalysis(A);
  m_isInitialized = true;
 }
 /** Performs a numeric decomposition of matrix \a a.
 *
 * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
 * performed.
 *
 * \sa analyzePattern()
 */
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
  AccelSparseMatrix A{};
  std::vector<long> columnStarts;
  buildAccelSparseMatrix(a, A, columnStarts);
  doFactorization(A);
 }
 template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
 template <typename Rhs, typename Dest>
 void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
                                                                              MatrixBase<Dest>& x) const {
  if (!m_numericFactorization) {
    m_info = InvalidInput;
    return;
  }
  eigen_assert(m_nRows == b.rows());
  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
  SparseStatus_t status = SparseStatusOK;
  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
  AccelDenseMatrix xmat{};
  xmat.attributes = SparseAttributes_t();
  xmat.columnCount = static_cast<int>(x.cols());
  xmat.rowCount = static_cast<int>(x.rows());
  xmat.columnStride = xmat.rowCount;
  xmat.data = x_ptr;
  AccelDenseMatrix bmat{};
  bmat.attributes = SparseAttributes_t();
  bmat.columnCount = static_cast<int>(b.cols());
  bmat.rowCount = static_cast<int>(b.rows());
  bmat.columnStride = bmat.rowCount;
  bmat.data = b_ptr;
  SparseSolve(*m_numericFactorization, bmat, xmat);
  updateInfoStatus(status);
 }
 }  // end namespace Eigen
 #endif  // EIGEN_ACCELERATESUPPORT_H
--- a/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
+++ b/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
@@ -1,3 +0,0 @@
 #ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
 #error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
 #endif
--- a/Eigen/src/CMakeLists.txt
+++ b/Eigen/src/CMakeLists.txt
@@ -0,0 +1,7 @@
 file(GLOB Eigen_src_subdirectories "*")
 escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
 foreach(f ${Eigen_src_subdirectories})
  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
    add_subdirectory(${f})
  endif()
 endforeach()
--- a/Eigen/src/Cholesky/CMakeLists.txt
+++ b/Eigen/src/Cholesky/CMakeLists.txt
@@ -0,0 +1,6 @@
 FILE(GLOB Eigen_Cholesky_SRCS "*.h")
 INSTALL(FILES
  ${Eigen_Cholesky_SRCS}
  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
  )
--- a/Eigen/src/Cholesky/InternalHeaderCheck.h
+++ b/Eigen/src/Cholesky/InternalHeaderCheck.h
@@ -1,3 +0,0 @@
 #ifndef EIGEN_CHOLESKY_MODULE_H
 #error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
 #endif
--- a/Eigen/src/Cholesky/LDLT.h
+++ b/Eigen/src/Cholesky/LDLT.h
@@ -1,78 +1,77 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_LDLT_H
 #define EIGEN_LDLT_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
-template <typename MatrixType_, int UpLo_>
+template<typename MatrixType, int UpLo> struct LDLT_Traits;
-struct traits<LDLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
+}
  typedef MatrixXpr XprKind;
  typedef SolverStorage StorageKind;
  typedef int StorageIndex;
  enum { Flags = 0 };
 };
-template <typename MatrixType, int UpLo>
+/** \ingroup cholesky_Module
 struct LDLT_Traits;
 // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
 enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
 }  // namespace internal
 /** \ingroup Cholesky_Module
  *
  * \class LDLT
  *
  * \brief Robust Cholesky decomposition of a matrix with pivoting
  *
- * \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
+  * \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
 * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
 *             The other triangular part won't be read.
  *
  * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
  * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
  * is lower triangular with a unit diagonal and D is a diagonal matrix.
  *
- * The decomposition uses pivoting to ensure stability, so that D will have
+  * The decomposition uses pivoting to ensure stability, so that L will have
  * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
  * on D also stabilizes the computation.
  *
  * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
 	* decomposition to determine whether a system of equations has a solution.
  *
- * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  * \sa MatrixBase::ldlt(), class LLT
 *
 * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
  */
-template <typename MatrixType_, int UpLo_>
+ /* THIS PART OF THE DOX IS CURRENTLY DISABLED BECAUSE INACCURATE BECAUSE OF BUG IN THE DECOMPOSITION CODE
-class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
+  * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
  * the strict lower part does not have to store correct values.
  */
 template<typename _MatrixType, int _UpLo> class LDLT
 {
  public:
-  typedef MatrixType_ MatrixType;
+    typedef _MatrixType MatrixType;
  typedef SolverBase<LDLT> Base;
  friend class SolverBase<LDLT>;
  EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
    enum {
      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
      Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    UpLo = UpLo_
+      UpLo = _UpLo
    };
-  typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
+    typedef typename MatrixType::Scalar Scalar;
    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
    typedef typename MatrixType::Index Index;
    typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
@@ -84,13 +83,7 @@ class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
      * The default constructor is useful in cases in which the user intends to
      * perform decompositions via LDLT::compute(const MatrixType&).
      */
-  LDLT()
+    LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
      : m_matrix(),
        m_l1_norm(0),
        m_transpositions(),
        m_sign(internal::ZeroSign),
        m_isInitialized(false),
        m_info(InvalidInput) {}
    /** \brief Default Constructor with memory preallocation
      *
@@ -98,95 +91,72 @@ class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
      * according to the specified problem \a size.
      * \sa LDLT()
      */
-  explicit LDLT(Index size)
+    LDLT(Index size)
      : m_matrix(size, size),
        m_l1_norm(0),
        m_transpositions(size),
        m_temporary(size),
-        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
-        m_isInitialized(false),
+    {}
        m_info(InvalidInput) {}
-  /** \brief Constructor with decomposition
+    LDLT(const MatrixType& matrix)
   *
   * This calculates the decomposition for the input \a matrix.
   *
   * \sa LDLT(Index size)
   */
  template <typename InputType>
  explicit LDLT(const EigenBase<InputType>& matrix)
      : m_matrix(matrix.rows(), matrix.cols()),
        m_l1_norm(0),
        m_transpositions(matrix.rows()),
        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
-        m_isInitialized(false),
+    {
-        m_info(InvalidInput) {
+      compute(matrix);
    compute(matrix.derived());
    }
  /** \brief Constructs a LDLT factorization from a given matrix
   *
   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
   * MatrixType is a Eigen::Ref.
   *
   * \sa LDLT(const EigenBase&)
   */
  template <typename InputType>
  explicit LDLT(EigenBase<InputType>& matrix)
      : m_matrix(matrix.derived()),
        m_l1_norm(0),
        m_transpositions(matrix.rows()),
        m_temporary(matrix.rows()),
        m_sign(internal::ZeroSign),
        m_isInitialized(false),
        m_info(InvalidInput) {
    compute(matrix.derived());
  }
  /** Clear any existing decomposition
   * \sa rankUpdate(w,sigma)
   */
  void setZero() { m_isInitialized = false; }
    /** \returns a view of the upper triangular matrix U */
-  inline typename Traits::MatrixU matrixU() const {
+    inline typename Traits::MatrixU matrixU() const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      return Traits::getU(m_matrix);
    }
    /** \returns a view of the lower triangular matrix L */
-  inline typename Traits::MatrixL matrixL() const {
+    inline typename Traits::MatrixL matrixL() const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      return Traits::getL(m_matrix);
    }
    /** \returns the permutation matrix P as a transposition sequence.
      */
-  inline const TranspositionType& transpositionsP() const {
+    inline const TranspositionType& transpositionsP() const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      return m_transpositions;
    }
    /** \returns the coefficients of the diagonal matrix D */
-  inline Diagonal<const MatrixType> vectorD() const {
+    inline Diagonal<const MatrixType> vectorD(void) const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      return m_matrix.diagonal();
    }
    /** \returns true if the matrix is positive (semidefinite) */
-  inline bool isPositive() const {
+    inline bool isPositive(void) const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
+      return m_sign == 1;
    }
    #ifdef EIGEN2_SUPPORT
    inline bool isPositiveDefinite() const
    {
      return isPositive();
    }
    #endif
    /** \returns true if the matrix is negative (semidefinite) */
-  inline bool isNegative(void) const {
+    inline bool isNegative(void) const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
+      return m_sign == -1;
    }
 #ifdef EIGEN_PARSED_BY_DOXYGEN
    /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
      *
      * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
@@ -198,73 +168,50 @@ class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
      * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
      * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
      * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
-   * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
+      * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
      *
-   * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
+      * \sa MatrixBase::ldlt()
      */
    template<typename Rhs>
-  inline Solve<LDLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+    inline const internal::solve_retval<LDLT, Rhs>
    solve(const MatrixBase<Rhs>& b) const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      eigen_assert(m_matrix.rows()==b.rows()
                && "LDLT::solve(): invalid number of rows of the right hand side matrix b");
      return internal::solve_retval<LDLT, Rhs>(*this, b.derived());
    }
    #ifdef EIGEN2_SUPPORT
    template<typename OtherDerived, typename ResultType>
    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
    {
      *result = this->solve(b);
      return true;
    }
    #endif
    template<typename Derived>
    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
-  template <typename InputType>
+    LDLT& compute(const MatrixType& matrix);
  LDLT& compute(const EigenBase<InputType>& matrix);
  /** \returns an estimate of the reciprocal condition number of the matrix of
   *  which \c *this is the LDLT decomposition.
   */
  RealScalar rcond() const {
    eigen_assert(m_isInitialized && "LDLT is not initialized.");
    return internal::rcond_estimate_helper(m_l1_norm, *this);
  }
  template <typename Derived>
  LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha = 1);
    /** \returns the internal LDLT decomposition matrix
      *
-   * TODO: document the storage layout.
+      * TODO: document the storage layout
      */
-  inline const MatrixType& matrixLDLT() const {
+    inline const MatrixType& matrixLDLT() const
    {
      eigen_assert(m_isInitialized && "LDLT is not initialized.");
      return m_matrix;
    }
    MatrixType reconstructedMatrix() const;
-  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+    inline Index rows() const { return m_matrix.rows(); }
-   * is self-adjoint.
+    inline Index cols() const { return m_matrix.cols(); }
   *
   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
   * \code x = decomposition.adjoint().solve(b) \endcode
   */
  const LDLT& adjoint() const { return *this; }
  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
  /** \brief Reports whether previous computation was successful.
   *
   * \returns \c Success if computation was successful,
   *          \c NumericalIssue if the factorization failed because of a zero pivot.
   */
  ComputationInfo info() const {
    eigen_assert(m_isInitialized && "LDLT is not initialized.");
    return m_info;
  }
 #ifndef EIGEN_PARSED_BY_DOXYGEN
  template <typename RhsType, typename DstType>
  void _solve_impl(const RhsType& rhs, DstType& dst) const;
  template <bool Conjugate, typename RhsType, typename DstType>
  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
 #endif
  protected:
  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
    /** \internal
      * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
@@ -273,66 +220,79 @@ class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
      * is not stored), and the diagonal entries correspond to D.
      */
    MatrixType m_matrix;
  RealScalar m_l1_norm;
    TranspositionType m_transpositions;
    TmpMatrixType m_temporary;
-  internal::SignMatrix m_sign;
+    int m_sign;
    bool m_isInitialized;
  ComputationInfo m_info;
 };
 namespace internal {
-template <int UpLo>
+template<int UpLo> struct ldlt_inplace;
 struct ldlt_inplace;
-template <>
+template<> struct ldlt_inplace<Lower>
-struct ldlt_inplace<Lower> {
+{
  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) {
+  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
-    using std::abs;
+  {
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename TranspositionType::StorageIndex IndexType;
+    typedef typename MatrixType::Index Index;
    eigen_assert(mat.rows()==mat.cols());
    const Index size = mat.rows();
    bool found_zero_pivot = false;
    bool ret = true;
-    if (size <= 1) {
+    if (size <= 1)
    {
      transpositions.setIdentity();
-      if (size == 0)
+      if(sign)
-        sign = ZeroSign;
+        *sign = real(mat.coeff(0,0))>0 ? 1:-1;
      else if (numext::real(mat.coeff(0, 0)) > static_cast<RealScalar>(0))
        sign = PositiveSemiDef;
      else if (numext::real(mat.coeff(0, 0)) < static_cast<RealScalar>(0))
        sign = NegativeSemiDef;
      else
        sign = ZeroSign;
      return true;
    }
-    for (Index k = 0; k < size; ++k) {
+    RealScalar cutoff = 0, biggest_in_corner;
    for (Index k = 0; k < size; ++k)
    {
      // Find largest diagonal element
      Index index_of_biggest_in_corner;
-      mat.diagonal().tail(size - k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
+      biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
      index_of_biggest_in_corner += k;
-      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
+      if(k == 0)
-      if (k != index_of_biggest_in_corner) {
+      {
        // The biggest overall is the point of reference to which further diagonals
        // are compared; if any diagonal is negligible compared
        // to the largest overall, the algorithm bails.
        cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
        if(sign)
          *sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
      }
      // Finish early if the matrix is not full rank.
      if(biggest_in_corner < cutoff)
      {
        for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
        break;
      }
      transpositions.coeffRef(k) = index_of_biggest_in_corner;
      if(k != index_of_biggest_in_corner)
      {
        // apply the transposition while taking care to consider only
        // the lower triangular part
        Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
        std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
-        for (Index i = k + 1; i < index_of_biggest_in_corner; ++i) {
+        for(int i=k+1;i<index_of_biggest_in_corner;++i)
        {
          Scalar tmp = mat.coeffRef(i,k);
-          mat.coeffRef(i, k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner, i));
+          mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
-          mat.coeffRef(index_of_biggest_in_corner, i) = numext::conj(tmp);
+          mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
        }
        if(NumTraits<Scalar>::IsComplex)
-          mat.coeffRef(index_of_biggest_in_corner, k) = numext::conj(mat.coeff(index_of_biggest_in_corner, k));
+          mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
      }
      // partition the matrix:
@@ -344,250 +304,111 @@ struct ldlt_inplace<Lower> {
      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-      if (k > 0) {
+      if(k>0)
-        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
+      {
        temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint();
        mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
-        if (rs > 0) A21.noalias() -= A20 * temp.head(k);
+        if(rs>0)
          A21.noalias() -= A20 * temp.head(k);
      }
      if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
        A21 /= mat.coeffRef(k,k);
    }
      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
      // we should only make sure that we do not introduce INF or NaN values.
      // Remark that LAPACK also uses 0 as the cutoff value.
      RealScalar realAkk = numext::real(mat.coeffRef(k, k));
      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
      if (k == 0 && !pivot_is_valid) {
        // The entire diagonal is zero, there is nothing more to do
        // except filling the transpositions, and checking whether the matrix is zero.
        sign = ZeroSign;
        for (Index j = 0; j < size; ++j) {
          transpositions.coeffRef(j) = IndexType(j);
          ret = ret && (mat.col(j).tail(size - j - 1).array() == Scalar(0)).all();
        }
        return ret;
      }
      if ((rs > 0) && pivot_is_valid)
        A21 /= realAkk;
      else if (rs > 0)
        ret = ret && (A21.array() == Scalar(0)).all();
      if (found_zero_pivot && pivot_is_valid)
        ret = false;  // factorization failed
      else if (!pivot_is_valid)
        found_zero_pivot = true;
      if (sign == PositiveSemiDef) {
        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
      } else if (sign == NegativeSemiDef) {
        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
      } else if (sign == ZeroSign) {
        if (realAkk > static_cast<RealScalar>(0))
          sign = PositiveSemiDef;
        else if (realAkk < static_cast<RealScalar>(0))
          sign = NegativeSemiDef;
      }
    }
    return ret;
  }
  // Reference for the algorithm: Davis and Hager, "Multiple Rank
  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
  // Trivial rearrangements of their computations (Timothy E. Holy)
  // allow their algorithm to work for rank-1 updates even if the
  // original matrix is not of full rank.
  // Here only rank-1 updates are implemented, to reduce the
  // requirement for intermediate storage and improve accuracy
  template <typename MatrixType, typename WDerived>
  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w,
                            const typename MatrixType::RealScalar& sigma = 1) {
    using numext::isfinite;
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
    const Index size = mat.rows();
    eigen_assert(mat.cols() == size && w.size() == size);
    RealScalar alpha = 1;
    // Apply the update
    for (Index j = 0; j < size; j++) {
      // Check for termination due to an original decomposition of low-rank
      if (!(isfinite)(alpha)) break;
      // Update the diagonal terms
      RealScalar dj = numext::real(mat.coeff(j, j));
      Scalar wj = w.coeff(j);
      RealScalar swj2 = sigma * numext::abs2(wj);
      RealScalar gamma = dj * alpha + swj2;
      mat.coeffRef(j, j) += swj2 / alpha;
      alpha += swj2 / dj;
      // Update the terms of L
      Index rs = size - j - 1;
      w.tail(rs) -= wj * mat.col(j).tail(rs);
      if (!numext::is_exactly_zero(gamma)) mat.col(j).tail(rs) += (sigma * numext::conj(wj) / gamma) * w.tail(rs);
    }
    return true;
  }
  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w,
                     const typename MatrixType::RealScalar& sigma = 1) {
    // Apply the permutation to the input w
    tmp = transpositions * w;
    return ldlt_inplace<Lower>::updateInPlace(mat, tmp, sigma);
  }
 };
-template <>
+template<> struct ldlt_inplace<Upper>
-struct ldlt_inplace<Upper> {
+{
  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp,
+  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
-                                            SignMatrix& sign) {
+  {
    Transpose<MatrixType> matt(mat);
    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
  }
  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w,
                                         const typename MatrixType::RealScalar& sigma = 1) {
    Transpose<MatrixType> matt(mat);
    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
  }
 };
-template <typename MatrixType>
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
-struct LDLT_Traits<MatrixType, Lower> {
+{
  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  inline static MatrixL getL(const MatrixType& m) { return m; }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+  inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
 };
-template <typename MatrixType>
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
-struct LDLT_Traits<MatrixType, Upper> {
+{
  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+  inline static MatrixU getU(const MatrixType& m) { return m; }
 };
 } // end namespace internal
 /** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
  */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType, int _UpLo>
-template <typename InputType>
+LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
-LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+{
  eigen_assert(a.rows()==a.cols());
  const Index size = a.rows();
-  m_matrix = a.derived();
+  m_matrix = a;
  // Compute matrix L1 norm = max abs column sum.
  m_l1_norm = RealScalar(0);
  // TODO: move this code to SelfAdjointView
  for (Index col = 0; col < size; ++col) {
    RealScalar abs_col_sum;
    if (UpLo_ == Lower)
      abs_col_sum =
          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
    else
      abs_col_sum =
          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
  }
  m_transpositions.resize(size);
  m_isInitialized = false;
  m_temporary.resize(size);
  m_sign = internal::ZeroSign;
-  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success
+  internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign);
                                                                                                    : NumericalIssue;
  m_isInitialized = true;
  return *this;
 }
-/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
+namespace internal {
- * \param w a vector to be incorporated into the decomposition.
+template<typename _MatrixType, int _UpLo, typename Rhs>
- * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column
+struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
- * vectors. Optional; default value is +1. \sa setZero()
+  : solve_retval_base<LDLT<_MatrixType,_UpLo>, Rhs>
- */
+{
-template <typename MatrixType, int UpLo_>
+  typedef LDLT<_MatrixType,_UpLo> LDLTType;
-template <typename Derived>
+  EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs)
 LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::rankUpdate(
    const MatrixBase<Derived>& w, const typename LDLT<MatrixType, UpLo_>::RealScalar& sigma) {
  typedef typename TranspositionType::StorageIndex IndexType;
  const Index size = w.rows();
  if (m_isInitialized) {
    eigen_assert(m_matrix.rows() == size);
  } else {
    m_matrix.resize(size, size);
    m_matrix.setZero();
    m_transpositions.resize(size);
    for (Index i = 0; i < size; i++) m_transpositions.coeffRef(i) = IndexType(i);
    m_temporary.resize(size);
    m_sign = sigma >= 0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
    m_isInitialized = true;
  }
-  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
+  template<typename Dest> void evalTo(Dest& dst) const
-
+  {
-  return *this;
+    eigen_assert(rhs().rows() == dec().matrixLDLT().rows());
 }
 #ifndef EIGEN_PARSED_BY_DOXYGEN
 template <typename MatrixType_, int UpLo_>
 template <typename RhsType, typename DstType>
 void LDLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
  _solve_impl_transposed<true>(rhs, dst);
 }
 template <typename MatrixType_, int UpLo_>
 template <bool Conjugate, typename RhsType, typename DstType>
 void LDLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
    // dst = P b
-  dst = m_transpositions * rhs;
+    dst = dec().transpositionsP() * rhs();
    // dst = L^-1 (P b)
-  // dst = L^-*T (P b)
+    dec().matrixL().solveInPlace(dst);
  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-  // dst = D^-* (L^-1 P b)
+    // dst = D^-1 (L^-1 P b)
  // dst = D^-1 (L^-*T P b)
    // more precisely, use pseudo-inverse of D (see bug 241)
    using std::abs;
-  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
+    using std::max;
-  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
+    typedef typename LDLTType::MatrixType MatrixType;
-  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
+    typedef typename LDLTType::Scalar Scalar;
-  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1)
+    typedef typename LDLTType::RealScalar RealScalar;
-  // / NumTraits<RealScalar>::highest()); However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the
+    const Diagonal<const MatrixType> vectorD = dec().vectorD();
-  // highest diagonal element is not well justified and leads to numerical issues in some cases. Moreover, Lapack's
+    RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
-  // xSYTRS routines use 0 for the tolerance. Using numeric_limits::min() gives us more robustness to denormals.
+				 RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
-  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
+    for (Index i = 0; i < vectorD.size(); ++i) {
-  for (Index i = 0; i < vecD.size(); ++i) {
+      if(abs(vectorD(i)) > tolerance)
-    if (abs(vecD(i)) > tolerance)
+	dst.row(i) /= vectorD(i);
      dst.row(i) /= vecD(i);
      else
 	dst.row(i).setZero();
    }
-  // dst = L^-* (D^-* L^-1 P b)
+    // dst = L^-T (D^-1 L^-1 P b)
-  // dst = L^-T (D^-1 L^-*T P b)
+    dec().matrixU().solveInPlace(dst);
  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
-  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
+    // dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
-  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
+    dst = dec().transpositionsP().transpose() * dst;
-  dst = m_transpositions.transpose() * dst;
+  }
 };
 }
 #endif
 /** \internal use x = ldlt_object.solve(x);
  *
@@ -602,11 +423,13 @@ void LDLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstTyp
  *
  * \sa LDLT::solve(), MatrixBase::ldlt()
  */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType,int _UpLo>
 template<typename Derived>
-bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
+bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
 {
  eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  eigen_assert(m_matrix.rows() == bAndX.rows());
+  const Index size = m_matrix.rows();
  eigen_assert(size == bAndX.rows());
  bAndX = this->solve(bAndX);
@@ -616,8 +439,9 @@ bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
 /** \returns the matrix represented by the decomposition,
 * i.e., it returns the product: P^T L D L^* P.
 * This function is provided for debug purpose. */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType, int _UpLo>
-MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
 {
  eigen_assert(m_isInitialized && "LDLT is not initialized.");
  const Index size = m_matrix.rows();
  MatrixType res(size,size);
@@ -628,7 +452,7 @@ MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
  // L^* P
  res = matrixU() * res;
  // D(L^*P)
-  res = vectorD().real().asDiagonal() * res;
+  res = vectorD().asDiagonal() * res;
  // L(DL^*P)
  res = matrixL() * res;
  // P^T (LDL^*P)
@@ -639,23 +463,22 @@ MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
 /** \cholesky_module
  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
 * \sa MatrixBase::ldlt()
  */
 template<typename MatrixType, unsigned int UpLo>
-inline LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::ldlt()
+inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-    const {
+SelfAdjointView<MatrixType, UpLo>::ldlt() const
 {
  return LDLT<PlainObject,UpLo>(m_matrix);
 }
 /** \cholesky_module
  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
 * \sa SelfAdjointView::ldlt()
  */
 template<typename Derived>
-inline LDLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::ldlt() const {
+inline const LDLT<typename MatrixBase<Derived>::PlainObject>
 MatrixBase<Derived>::ldlt() const
 {
  return LDLT<PlainObject>(derived());
 }
 }  // end namespace Eigen
 #endif // EIGEN_LDLT_H
--- a/Eigen/src/Cholesky/LLT.h
+++ b/Eigen/src/Cholesky/LLT.h
@@ -3,41 +3,39 @@
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_LLT_H
 #define EIGEN_LLT_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal{
 template<typename MatrixType, int UpLo> struct LLT_Traits;
 }
-template <typename MatrixType_, int UpLo_>
+/** \ingroup cholesky_Module
 struct traits<LLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
  typedef MatrixXpr XprKind;
  typedef SolverStorage StorageKind;
  typedef int StorageIndex;
  enum { Flags = 0 };
 };
 template <typename MatrixType, int UpLo>
 struct LLT_Traits;
 }  // namespace internal
 /** \ingroup Cholesky_Module
  *
  * \class LLT
  *
  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
  *
- * \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
+  * \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
 * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
 *               The other triangular part won't be read.
  *
  * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
@@ -47,36 +45,35 @@ struct LLT_Traits;
  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
  * situations like generalised eigen problems with hermitian matrices.
  *
- * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive
+  * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
- * definite matrices, use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine
+  * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
- * whether a system of equations has a solution.
+  * has a solution.
  *
- * Example: \include LLT_example.cpp
+  * \sa MatrixBase::llt(), class LDLT
 * Output: \verbinclude LLT_example.out
 *
 * \b Performance: for best performance, it is recommended to use a column-major storage format
 * with the Lower triangular part (the default), or, equivalently, a row-major storage format
 * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
 * step, and rank-updates can be up to 3 times slower.
 *
 * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
 *
 * Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is
 * considered. Therefore, the strict lower part does not have to store correct values.
 *
 * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
  */
-template <typename MatrixType_, int UpLo_>
+ /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
-class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
+  * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
  * the strict lower part does not have to store correct values.
  */
 template<typename _MatrixType, int _UpLo> class LLT
 {
  public:
-  typedef MatrixType_ MatrixType;
+    typedef _MatrixType MatrixType;
-  typedef SolverBase<LLT> Base;
+    enum {
-  friend class SolverBase<LLT>;
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
      Options = MatrixType::Options,
      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
    };
    typedef typename MatrixType::Scalar Scalar;
    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
    typedef typename MatrixType::Index Index;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
+    enum {
-  enum { MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+      PacketSize = internal::packet_traits<Scalar>::size,
-
+      AlignmentMask = int(PacketSize)-1,
-  enum { PacketSize = internal::packet_traits<Scalar>::size, AlignmentMask = int(PacketSize) - 1, UpLo = UpLo_ };
+      UpLo = _UpLo
    };
    typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
@@ -86,7 +83,7 @@ class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
      * The default constructor is useful in cases in which the user intends to
      * perform decompositions via LLT::compute(const MatrixType&).
      */
-  LLT() : m_matrix(), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+    LLT() : m_matrix(), m_isInitialized(false) {}
    /** \brief Default Constructor with memory preallocation
      *
@@ -94,40 +91,30 @@ class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
      * according to the specified problem \a size.
      * \sa LLT()
      */
-  explicit LLT(Index size) : m_matrix(size, size), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+    LLT(Index size) : m_matrix(size, size),
                    m_isInitialized(false) {}
-  template <typename InputType>
+    LLT(const MatrixType& matrix)
-  explicit LLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
-      : m_matrix(matrix.rows(), matrix.cols()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
+        m_isInitialized(false)
-    compute(matrix.derived());
+    {
-  }
+      compute(matrix);
  /** \brief Constructs a LLT factorization from a given matrix
   *
   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
   * \c MatrixType is a Eigen::Ref.
   *
   * \sa LLT(const EigenBase&)
   */
  template <typename InputType>
  explicit LLT(EigenBase<InputType>& matrix)
      : m_matrix(matrix.derived()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
    compute(matrix.derived());
    }
    /** \returns a view of the upper triangular matrix U */
-  inline typename Traits::MatrixU matrixU() const {
+    inline typename Traits::MatrixU matrixU() const
    {
      eigen_assert(m_isInitialized && "LLT is not initialized.");
      return Traits::getU(m_matrix);
    }
    /** \returns a view of the lower triangular matrix L */
-  inline typename Traits::MatrixL matrixL() const {
+    inline typename Traits::MatrixL matrixL() const
    {
      eigen_assert(m_isInitialized && "LLT is not initialized.");
      return Traits::getL(m_matrix);
    }
 #ifdef EIGEN_PARSED_BY_DOXYGEN
    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
      *
      * Since this LLT class assumes anyway that the matrix A is invertible, the solution
@@ -136,188 +123,120 @@ class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
      * Example: \include LLT_solve.cpp
      * Output: \verbinclude LLT_solve.out
      *
-   * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
+      * \sa solveInPlace(), MatrixBase::llt()
      */
    template<typename Rhs>
-  inline Solve<LLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+    inline const internal::solve_retval<LLT, Rhs>
    solve(const MatrixBase<Rhs>& b) const
    {
      eigen_assert(m_isInitialized && "LLT is not initialized.");
      eigen_assert(m_matrix.rows()==b.rows()
                && "LLT::solve(): invalid number of rows of the right hand side matrix b");
      return internal::solve_retval<LLT, Rhs>(*this, b.derived());
    }
    #ifdef EIGEN2_SUPPORT
    template<typename OtherDerived, typename ResultType>
    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
    {
      *result = this->solve(b);
      return true;
    }
    bool isPositiveDefinite() const { return true; }
    #endif
    template<typename Derived>
-  void solveInPlace(const MatrixBase<Derived>& bAndX) const;
+    void solveInPlace(MatrixBase<Derived> &bAndX) const;
-  template <typename InputType>
+    LLT& compute(const MatrixType& matrix);
  LLT& compute(const EigenBase<InputType>& matrix);
  /** \returns an estimate of the reciprocal condition number of the matrix of
   *  which \c *this is the Cholesky decomposition.
   */
  RealScalar rcond() const {
    eigen_assert(m_isInitialized && "LLT is not initialized.");
    eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
    return internal::rcond_estimate_helper(m_l1_norm, *this);
  }
    /** \returns the LLT decomposition matrix
      *
      * TODO: document the storage layout
      */
-  inline const MatrixType& matrixLLT() const {
+    inline const MatrixType& matrixLLT() const
    {
      eigen_assert(m_isInitialized && "LLT is not initialized.");
      return m_matrix;
    }
    MatrixType reconstructedMatrix() const;
    /** \brief Reports whether previous computation was successful.
      *
-   * \returns \c Success if computation was successful,
+      * \returns \c Success if computation was succesful,
-   *          \c NumericalIssue if the matrix.appears not to be positive definite.
+      *          \c NumericalIssue if the matrix.appears to be negative.
      */
-  ComputationInfo info() const {
+    ComputationInfo info() const
    {
      eigen_assert(m_isInitialized && "LLT is not initialized.");
      return m_info;
    }
-  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+    inline Index rows() const { return m_matrix.rows(); }
-   * is self-adjoint.
+    inline Index cols() const { return m_matrix.cols(); }
   *
   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
   * \code x = decomposition.adjoint().solve(b) \endcode
   */
  const LLT& adjoint() const noexcept { return *this; }
  constexpr Index rows() const noexcept { return m_matrix.rows(); }
  constexpr Index cols() const noexcept { return m_matrix.cols(); }
  template <typename VectorType>
  LLT& rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
 #ifndef EIGEN_PARSED_BY_DOXYGEN
  template <typename RhsType, typename DstType>
  void _solve_impl(const RhsType& rhs, DstType& dst) const;
  template <bool Conjugate, typename RhsType, typename DstType>
  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
 #endif
  protected:
  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
    /** \internal
      * Used to compute and store L
      * The strict upper part is not used and even not initialized.
      */
    MatrixType m_matrix;
  RealScalar m_l1_norm;
    bool m_isInitialized;
    ComputationInfo m_info;
 };
 namespace internal {
-template <typename Scalar, int UpLo>
+template<int UpLo> struct llt_inplace;
 struct llt_inplace;
-template <typename MatrixType, typename VectorType>
+template<> struct llt_inplace<Lower>
-static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec,
+{
-                                   const typename MatrixType::RealScalar& sigma) {
+  template<typename MatrixType>
-  using std::sqrt;
+  static typename MatrixType::Index unblocked(MatrixType& mat)
  {
    typedef typename MatrixType::Index Index;
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
  typedef typename MatrixType::ColXpr ColXpr;
  typedef internal::remove_all_t<ColXpr> ColXprCleaned;
  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
  typedef Matrix<Scalar, Dynamic, 1> TempVectorType;
  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
  Index n = mat.cols();
  eigen_assert(mat.rows() == n && vec.size() == n);
  TempVectorType temp;
  if (sigma > 0) {
    // This version is based on Givens rotations.
    // It is faster than the other one below, but only works for updates,
    // i.e., for sigma > 0
    temp = sqrt(sigma) * vec;
    for (Index i = 0; i < n; ++i) {
      JacobiRotation<Scalar> g;
      g.makeGivens(mat(i, i), -temp(i), &mat(i, i));
      Index rs = n - i - 1;
      if (rs > 0) {
        ColXprSegment x(mat.col(i).tail(rs));
        TempVecSegment y(temp.tail(rs));
        apply_rotation_in_the_plane(x, y, g);
      }
    }
  } else {
    temp = vec;
    RealScalar beta = 1;
    for (Index j = 0; j < n; ++j) {
      RealScalar Ljj = numext::real(mat.coeff(j, j));
      RealScalar dj = numext::abs2(Ljj);
      Scalar wj = temp.coeff(j);
      RealScalar swj2 = sigma * numext::abs2(wj);
      RealScalar gamma = dj * beta + swj2;
      RealScalar x = dj + swj2 / beta;
      if (x <= RealScalar(0)) return j;
      RealScalar nLjj = sqrt(x);
      mat.coeffRef(j, j) = nLjj;
      beta += swj2 / dj;
      // Update the terms of L
      Index rs = n - j - 1;
      if (rs) {
        temp.tail(rs) -= (wj / Ljj) * mat.col(j).tail(rs);
        if (!numext::is_exactly_zero(gamma))
          mat.col(j).tail(rs) =
              (nLjj / Ljj) * mat.col(j).tail(rs) + (nLjj * sigma * numext::conj(wj) / gamma) * temp.tail(rs);
      }
    }
  }
  return -1;
 }
 template <typename Scalar>
 struct llt_inplace<Scalar, Lower> {
  typedef typename NumTraits<Scalar>::Real RealScalar;
  template <typename MatrixType>
  static Index unblocked(MatrixType& mat) {
    using std::sqrt;
    eigen_assert(mat.rows()==mat.cols());
    const Index size = mat.rows();
-    for (Index k = 0; k < size; ++k) {
+    for(Index k = 0; k < size; ++k)
    {
      Index rs = size-k-1; // remaining size
      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-      RealScalar x = numext::real(mat.coeff(k, k));
+      RealScalar x = real(mat.coeff(k,k));
      if (k>0) x -= A10.squaredNorm();
-      if (x <= RealScalar(0)) return k;
+      if (x<=RealScalar(0))
        return k;
      mat.coeffRef(k,k) = x = sqrt(x);
      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
-      if (rs > 0) A21 /= x;
+      if (rs>0) A21 *= RealScalar(1)/x;
    }
    return -1;
  }
  template<typename MatrixType>
-  static Index blocked(MatrixType& m) {
+  static typename MatrixType::Index blocked(MatrixType& m)
  {
    typedef typename MatrixType::Index Index;
    eigen_assert(m.rows()==m.cols());
    Index size = m.rows();
-    if (size < 32) return unblocked(m);
+    if(size<32)
      return unblocked(m);
    Index blockSize = size/8;
    blockSize = (blockSize/16)*16;
    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
-    for (Index k = 0; k < size; k += blockSize) {
+    for (Index k=0; k<size; k+=blockSize)
    {
      // partition the matrix:
      //       A00 |  -  |  -
      // lu  = A10 | A11 |  -
@@ -331,92 +250,62 @@ struct llt_inplace<Scalar, Lower> {
      Index ret;
      if((ret=unblocked(A11))>=0) return k+ret;
      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
-      if (rs > 0)
+      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck
        A22.template selfadjointView<Lower>().rankUpdate(A21,
                                                         typename NumTraits<RealScalar>::Literal(-1));  // bottleneck
    }
    return -1;
  }
 };
-  template <typename MatrixType, typename VectorType>
+template<> struct llt_inplace<Upper>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
+{
-    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  template<typename MatrixType>
  static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat)
  {
    Transpose<MatrixType> matt(mat);
    return llt_inplace<Lower>::unblocked(matt);
  }
  template<typename MatrixType>
  static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat)
  {
    Transpose<MatrixType> matt(mat);
    return llt_inplace<Lower>::blocked(matt);
  }
 };
-template <typename Scalar>
+template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
-struct llt_inplace<Scalar, Upper> {
+{
  typedef typename NumTraits<Scalar>::Real RealScalar;
  template <typename MatrixType>
  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat) {
    Transpose<MatrixType> matt(mat);
    return llt_inplace<Scalar, Lower>::unblocked(matt);
  }
  template <typename MatrixType>
  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat) {
    Transpose<MatrixType> matt(mat);
    return llt_inplace<Scalar, Lower>::blocked(matt);
  }
  template <typename MatrixType, typename VectorType>
  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
    Transpose<MatrixType> matt(mat);
    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
  }
 };
 template <typename MatrixType>
 struct LLT_Traits<MatrixType, Lower> {
  typedef const TriangularView<const MatrixType, Lower> MatrixL;
  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  inline static MatrixL getL(const MatrixType& m) { return m; }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+  inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
-  static bool inplace_decomposition(MatrixType& m) {
+  static bool inplace_decomposition(MatrixType& m)
-    return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m) == -1;
+  { return llt_inplace<Lower>::blocked(m)==-1; }
  }
 };
-template <typename MatrixType>
+template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
-struct LLT_Traits<MatrixType, Upper> {
+{
  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
  typedef const TriangularView<const MatrixType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+  inline static MatrixU getU(const MatrixType& m) { return m; }
-  static bool inplace_decomposition(MatrixType& m) {
+  static bool inplace_decomposition(MatrixType& m)
-    return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m) == -1;
+  { return llt_inplace<Upper>::blocked(m)==-1; }
  }
 };
 } // end namespace internal
 /** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
  *
 * \returns a reference to *this
  *
- * Example: \include TutorialLinAlgComputeTwice.cpp
+  * \returns a reference to *this
 * Output: \verbinclude TutorialLinAlgComputeTwice.out
  */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType, int _UpLo>
-template <typename InputType>
+LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
-LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+{
-  eigen_assert(a.rows() == a.cols());
+  assert(a.rows()==a.cols());
  const Index size = a.rows();
  m_matrix.resize(size, size);
-  if (!internal::is_same_dense(m_matrix, a.derived())) m_matrix = a.derived();
+  m_matrix = a;
  // Compute matrix L1 norm = max abs column sum.
  m_l1_norm = RealScalar(0);
  // TODO: move this code to SelfAdjointView
  for (Index col = 0; col < size; ++col) {
    RealScalar abs_col_sum;
    if (UpLo_ == Lower)
      abs_col_sum =
          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
    else
      abs_col_sum =
          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
  }
  m_isInitialized = true;
  bool ok = Traits::inplace_decomposition(m_matrix);
@@ -425,58 +314,39 @@ LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputTyp
  return *this;
 }
-/** Performs a rank one update (or dowdate) of the current decomposition.
+namespace internal {
- * If A = LL^* before the rank one update,
+template<typename _MatrixType, int UpLo, typename Rhs>
- * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
+struct solve_retval<LLT<_MatrixType, UpLo>, Rhs>
- * of same dimension.
+  : solve_retval_base<LLT<_MatrixType, UpLo>, Rhs>
- */
+{
-template <typename MatrixType_, int UpLo_>
+  typedef LLT<_MatrixType,UpLo> LLTType;
-template <typename VectorType>
+  EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
 LLT<MatrixType_, UpLo_>& LLT<MatrixType_, UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma) {
  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
  eigen_assert(v.size() == m_matrix.cols());
  eigen_assert(m_isInitialized);
  if (internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix, v, sigma) >= 0)
    m_info = NumericalIssue;
  else
    m_info = Success;
-  return *this;
+  template<typename Dest> void evalTo(Dest& dst) const
  {
    dst = rhs();
    dec().solveInPlace(dst);
  }
-
+};
 #ifndef EIGEN_PARSED_BY_DOXYGEN
 template <typename MatrixType_, int UpLo_>
 template <typename RhsType, typename DstType>
 void LLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
  _solve_impl_transposed<true>(rhs, dst);
 }
 template <typename MatrixType_, int UpLo_>
 template <bool Conjugate, typename RhsType, typename DstType>
 void LLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
  dst = rhs;
  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
  matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
 }
 #endif
 /** \internal use x = llt_object.solve(x);
  * 
  * This is the \em in-place version of solve().
  *
  * \param bAndX represents both the right-hand side matrix b and result x.
  *
- * This version avoids a copy when the right hand side matrix b is not needed anymore.
+  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
  *
- * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+  * This version avoids a copy when the right hand side matrix b is not
- * This function will const_cast it, so constness isn't honored here.
+  * needed anymore.
  *
  * \sa LLT::solve(), MatrixBase::llt()
  */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType, int _UpLo>
 template<typename Derived>
-void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) const {
+void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
 {
  eigen_assert(m_isInitialized && "LLT is not initialized.");
  eigen_assert(m_matrix.rows()==bAndX.rows());
  matrixL().solveInPlace(bAndX);
@@ -486,31 +356,31 @@ void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) cons
 /** \returns the matrix represented by the decomposition,
 * i.e., it returns the product: L L^*.
 * This function is provided for debug purpose. */
-template <typename MatrixType, int UpLo_>
+template<typename MatrixType, int _UpLo>
-MatrixType LLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
 {
  eigen_assert(m_isInitialized && "LLT is not initialized.");
  return matrixL() * matrixL().adjoint().toDenseMatrix();
 }
 /** \cholesky_module
  * \returns the LLT decomposition of \c *this
 * \sa SelfAdjointView::llt()
  */
 template<typename Derived>
-inline LLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::llt() const {
+inline const LLT<typename MatrixBase<Derived>::PlainObject>
 MatrixBase<Derived>::llt() const
 {
  return LLT<PlainObject>(derived());
 }
 /** \cholesky_module
  * \returns the LLT decomposition of \c *this
 * \sa SelfAdjointView::llt()
  */
 template<typename MatrixType, unsigned int UpLo>
-inline LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::llt()
+inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-    const {
+SelfAdjointView<MatrixType, UpLo>::llt() const
 {
  return LLT<PlainObject,UpLo>(m_matrix);
 }
 }  // end namespace Eigen
 #endif // EIGEN_LLT_H
--- a/Eigen/src/Cholesky/LLT_LAPACKE.h
+++ b/Eigen/src/Cholesky/LLT_LAPACKE.h
@@ -1,124 +0,0 @@
 /*
 Copyright (c) 2011, Intel Corporation. All rights reserved.
 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:
 * Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.
 * Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.
 * Neither the name of Intel Corporation nor the names of its contributors may
   be used to endorse or promote products derived from this software without
   specific prior written permission.
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 ********************************************************************************
 *   Content : Eigen bindings to LAPACKe
 *     LLt decomposition based on LAPACKE_?potrf function.
 ********************************************************************************
 */
 #ifndef EIGEN_LLT_LAPACKE_H
 #define EIGEN_LLT_LAPACKE_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 namespace lapacke_helpers {
 // -------------------------------------------------------------------------------------------------------------------
 //        Dispatch for rank update handling upper and lower parts
 // -------------------------------------------------------------------------------------------------------------------
 template <UpLoType Mode>
 struct rank_update {};
 template <>
 struct rank_update<Lower> {
  template <typename MatrixType, typename VectorType>
  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
  }
 };
 template <>
 struct rank_update<Upper> {
  template <typename MatrixType, typename VectorType>
  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
    Transpose<MatrixType> matt(mat);
    return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
  }
 };
 // -------------------------------------------------------------------------------------------------------------------
 //        Generic lapacke llt implementation that hands of to the dispatches
 // -------------------------------------------------------------------------------------------------------------------
 template <typename Scalar, UpLoType Mode>
 struct lapacke_llt {
  EIGEN_STATIC_ASSERT(((Mode == Lower) || (Mode == Upper)), MODE_MUST_BE_UPPER_OR_LOWER)
  template <typename MatrixType>
  static Index blocked(MatrixType &m) {
    eigen_assert(m.rows() == m.cols());
    if (m.rows() == 0) {
      return -1;
    }
    /* Set up parameters for ?potrf */
    lapack_int size = to_lapack(m.rows());
    lapack_int matrix_order = lapack_storage_of(m);
    constexpr char uplo = Mode == Upper ? 'U' : 'L';
    Scalar *a = &(m.coeffRef(0, 0));
    lapack_int lda = to_lapack(m.outerStride());
    lapack_int info = potrf(matrix_order, uplo, size, to_lapack(a), lda);
    info = (info == 0) ? -1 : info > 0 ? info - 1 : size;
    return info;
  }
  template <typename MatrixType, typename VectorType>
  static Index rankUpdate(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
    return rank_update<Mode>::run(mat, vec, sigma);
  }
 };
 }  // namespace lapacke_helpers
 // end namespace lapacke_helpers
 /*
 * Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
 * type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
 * in LLT.h for double, float and complex double, complex float types.
 */
 #define EIGEN_LAPACKE_LLT(EIGTYPE)                                                             \
  template <>                                                                                  \
  struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
  template <>                                                                                  \
  struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
 EIGEN_LAPACKE_LLT(double)
 EIGEN_LAPACKE_LLT(float)
 EIGEN_LAPACKE_LLT(std::complex<double>)
 EIGEN_LAPACKE_LLT(std::complex<float>)
 #undef EIGEN_LAPACKE_LLT
 }  // end namespace internal
 }  // end namespace Eigen
 #endif  // EIGEN_LLT_LAPACKE_H
--- a/Eigen/src/CholmodSupport/CholmodSupport.h
+++ b/Eigen/src/CholmodSupport/CholmodSupport.h
@@ -1,738 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_CHOLMODSUPPORT_H
 #define EIGEN_CHOLMODSUPPORT_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename Scalar>
 struct cholmod_configure_matrix;
 template <>
 struct cholmod_configure_matrix<double> {
  template <typename CholmodType>
  static void run(CholmodType& mat) {
    mat.xtype = CHOLMOD_REAL;
    mat.dtype = CHOLMOD_DOUBLE;
  }
 };
 template <>
 struct cholmod_configure_matrix<std::complex<double> > {
  template <typename CholmodType>
  static void run(CholmodType& mat) {
    mat.xtype = CHOLMOD_COMPLEX;
    mat.dtype = CHOLMOD_DOUBLE;
  }
 };
 // Other scalar types are not yet supported by Cholmod
 // template<> struct cholmod_configure_matrix<float> {
 //   template<typename CholmodType>
 //   static void run(CholmodType& mat) {
 //     mat.xtype = CHOLMOD_REAL;
 //     mat.dtype = CHOLMOD_SINGLE;
 //   }
 // };
 //
 // template<> struct cholmod_configure_matrix<std::complex<float> > {
 //   template<typename CholmodType>
 //   static void run(CholmodType& mat) {
 //     mat.xtype = CHOLMOD_COMPLEX;
 //     mat.dtype = CHOLMOD_SINGLE;
 //   }
 // };
 }  // namespace internal
 /** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
 * Note that the data are shared.
 */
 template <typename Scalar_, int Options_, typename StorageIndex_>
 cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, StorageIndex_> > mat) {
  cholmod_sparse res;
  res.nzmax = mat.nonZeros();
  res.nrow = mat.rows();
  res.ncol = mat.cols();
  res.p = mat.outerIndexPtr();
  res.i = mat.innerIndexPtr();
  res.x = mat.valuePtr();
  res.z = 0;
  res.sorted = 1;
  if (mat.isCompressed()) {
    res.packed = 1;
    res.nz = 0;
  } else {
    res.packed = 0;
    res.nz = mat.innerNonZeroPtr();
  }
  res.dtype = 0;
  res.stype = -1;
  if (internal::is_same<StorageIndex_, int>::value) {
    res.itype = CHOLMOD_INT;
  } else if (internal::is_same<StorageIndex_, SuiteSparse_long>::value) {
    res.itype = CHOLMOD_LONG;
  } else {
    eigen_assert(false && "Index type not supported yet");
  }
  // setup res.xtype
  internal::cholmod_configure_matrix<Scalar_>::run(res);
  res.stype = 0;
  return res;
 }
 template <typename Scalar_, int Options_, typename Index_>
 const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_, Options_, Index_>& mat) {
  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
  return res;
 }
 template <typename Scalar_, int Options_, typename Index_>
 const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_, Options_, Index_>& mat) {
  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
  return res;
 }
 /** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
 * The data are not copied but shared. */
 template <typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
 cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_, Options_, Index_>, UpLo>& mat) {
  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.matrix().const_cast_derived()));
  if (UpLo == Upper) res.stype = 1;
  if (UpLo == Lower) res.stype = -1;
  // swap stype for rowmajor matrices (only works for real matrices)
  EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0,
                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
  if (Options_ & RowMajorBit) res.stype *= -1;
  return res;
 }
 /** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
 * The data are not copied but shared. */
 template <typename Derived>
 cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat) {
  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags & RowMajorBit) == 0,
                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
  typedef typename Derived::Scalar Scalar;
  cholmod_dense res;
  res.nrow = mat.rows();
  res.ncol = mat.cols();
  res.nzmax = res.nrow * res.ncol;
  res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
  res.x = (void*)(mat.derived().data());
  res.z = 0;
  internal::cholmod_configure_matrix<Scalar>::run(res);
  return res;
 }
 /** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
 * The data are not copied but shared. */
 template <typename Scalar, typename StorageIndex>
 Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_sparse& cm) {
  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
      cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol], static_cast<StorageIndex*>(cm.p),
      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
 }
 /** Returns a view of the Cholmod sparse matrix factor \a cm as an Eigen sparse matrix.
 * The data are not copied but shared. */
 template <typename Scalar, typename StorageIndex>
 Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_factor& cm) {
  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
      cm.n, cm.n, static_cast<StorageIndex*>(cm.p)[cm.n], static_cast<StorageIndex*>(cm.p),
      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
 }
 namespace internal {
 // template specializations for int and long that call the correct cholmod method
 #define EIGEN_CHOLMOD_SPECIALIZE0(ret, name)                        \
  template <typename StorageIndex_>                                 \
  inline ret cm_##name(cholmod_common& Common) {                    \
    return cholmod_##name(&Common);                                 \
  }                                                                 \
  template <>                                                       \
  inline ret cm_##name<SuiteSparse_long>(cholmod_common & Common) { \
    return cholmod_l_##name(&Common);                               \
  }
 #define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1)                         \
  template <typename StorageIndex_>                                          \
  inline ret cm_##name(t1& a1, cholmod_common& Common) {                     \
    return cholmod_##name(&a1, &Common);                                     \
  }                                                                          \
  template <>                                                                \
  inline ret cm_##name<SuiteSparse_long>(t1 & a1, cholmod_common & Common) { \
    return cholmod_l_##name(&a1, &Common);                                   \
  }
 EIGEN_CHOLMOD_SPECIALIZE0(int, start)
 EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
 EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
 EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X)
 EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
 EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
 EIGEN_CHOLMOD_SPECIALIZE1(cholmod_sparse*, factor_to_sparse, cholmod_factor, L)
 template <typename StorageIndex_>
 inline cholmod_dense* cm_solve(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
  return cholmod_solve(sys, &L, &B, &Common);
 }
 template <>
 inline cholmod_dense* cm_solve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
  return cholmod_l_solve(sys, &L, &B, &Common);
 }
 template <typename StorageIndex_>
 inline cholmod_sparse* cm_spsolve(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common) {
  return cholmod_spsolve(sys, &L, &B, &Common);
 }
 template <>
 inline cholmod_sparse* cm_spsolve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_sparse& B,
                                                    cholmod_common& Common) {
  return cholmod_l_spsolve(sys, &L, &B, &Common);
 }
 template <typename StorageIndex_>
 inline int cm_factorize_p(cholmod_sparse* A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L,
                          cholmod_common& Common) {
  return cholmod_factorize_p(A, beta, fset, fsize, L, &Common);
 }
 template <>
 inline int cm_factorize_p<SuiteSparse_long>(cholmod_sparse* A, double beta[2], SuiteSparse_long* fset,
                                            std::size_t fsize, cholmod_factor* L, cholmod_common& Common) {
  return cholmod_l_factorize_p(A, beta, fset, fsize, L, &Common);
 }
 #undef EIGEN_CHOLMOD_SPECIALIZE0
 #undef EIGEN_CHOLMOD_SPECIALIZE1
 }  // namespace internal
 enum CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt };
 /** \ingroup CholmodSupport_Module
 * \class CholmodBase
 * \brief The base class for the direct Cholesky factorization of Cholmod
 * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
 */
 template <typename MatrixType_, int UpLo_, typename Derived>
 class CholmodBase : public SparseSolverBase<Derived> {
 protected:
  typedef SparseSolverBase<Derived> Base;
  using Base::derived;
  using Base::m_isInitialized;
 public:
  typedef MatrixType_ MatrixType;
  enum { UpLo = UpLo_ };
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef MatrixType CholMatrixType;
  typedef typename MatrixType::StorageIndex StorageIndex;
  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
 public:
  CholmodBase() : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
    internal::cm_start<StorageIndex>(m_cholmod);
  }
  explicit CholmodBase(const MatrixType& matrix)
      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
    internal::cm_start<StorageIndex>(m_cholmod);
    compute(matrix);
  }
  ~CholmodBase() {
    if (m_cholmodFactor) internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
    internal::cm_finish<StorageIndex>(m_cholmod);
  }
  inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
  inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
  /** \brief Reports whether previous computation was successful.
   *
   * \returns \c Success if computation was successful,
   *          \c NumericalIssue if the matrix.appears to be negative.
   */
  ComputationInfo info() const {
    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
    return m_info;
  }
  /** Computes the sparse Cholesky decomposition of \a matrix */
  Derived& compute(const MatrixType& matrix) {
    analyzePattern(matrix);
    factorize(matrix);
    return derived();
  }
  /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
   *
   * This function is particularly useful when solving for several problems having the same structure.
   *
   * \sa factorize()
   */
  void analyzePattern(const MatrixType& matrix) {
    if (m_cholmodFactor) {
      internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
      m_cholmodFactor = 0;
    }
    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
    m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
    this->m_isInitialized = true;
    this->m_info = Success;
    m_analysisIsOk = true;
    m_factorizationIsOk = false;
  }
  /** Performs a numeric decomposition of \a matrix
   *
   * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
   * performed.
   *
   * \sa analyzePattern()
   */
  void factorize(const MatrixType& matrix) {
    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
    internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
    // If the factorization failed, either the input matrix was zero (so m_cholmodFactor == nullptr), or minor is the
    // column at which it failed. On success minor == n.
    this->m_info =
        (m_cholmodFactor != nullptr && m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
    m_factorizationIsOk = true;
  }
  /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
   *  See the Cholmod user guide for details. */
  cholmod_common& cholmod() { return m_cholmod; }
 #ifndef EIGEN_PARSED_BY_DOXYGEN
  /** \internal */
  template <typename Rhs, typename Dest>
  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
    eigen_assert(m_factorizationIsOk &&
                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
                 "symbolic()/numeric()");
    const Index size = m_cholmodFactor->n;
    EIGEN_UNUSED_VARIABLE(size);
    eigen_assert(size == b.rows());
    // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
    Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b.derived());
    cholmod_dense b_cd = viewAsCholmod(b_ref);
    cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
    if (!x_cd) {
      this->m_info = NumericalIssue;
      return;
    }
    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
    // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
    dest = Matrix<Scalar, Dest::RowsAtCompileTime, Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),
                                                                                 b.rows(), b.cols());
    internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
  }
  /** \internal */
  template <typename RhsDerived, typename DestDerived>
  void _solve_impl(const SparseMatrixBase<RhsDerived>& b, SparseMatrixBase<DestDerived>& dest) const {
    eigen_assert(m_factorizationIsOk &&
                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
                 "symbolic()/numeric()");
    const Index size = m_cholmodFactor->n;
    EIGEN_UNUSED_VARIABLE(size);
    eigen_assert(size == b.rows());
    // note: cs stands for Cholmod Sparse
    Ref<SparseMatrix<typename RhsDerived::Scalar, ColMajor, typename RhsDerived::StorageIndex> > b_ref(
        b.const_cast_derived());
    cholmod_sparse b_cs = viewAsCholmod(b_ref);
    cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
    if (!x_cs) {
      this->m_info = NumericalIssue;
      return;
    }
    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
    // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's
    // sparse solver)
    dest.derived() = viewAsEigen<typename DestDerived::Scalar, typename DestDerived::StorageIndex>(*x_cs);
    internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
  }
 #endif  // EIGEN_PARSED_BY_DOXYGEN
  /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
   *
   * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
   * \c d_ii = \a offset + \c d_ii
   *
   * The default is \a offset=0.
   *
   * \returns a reference to \c *this.
   */
  Derived& setShift(const RealScalar& offset) {
    m_shiftOffset[0] = double(offset);
    return derived();
  }
  /** \returns the determinant of the underlying matrix from the current factorization */
  Scalar determinant() const {
    using std::exp;
    return exp(logDeterminant());
  }
  /** \returns the log determinant of the underlying matrix from the current factorization */
  Scalar logDeterminant() const {
    using numext::real;
    using std::log;
    eigen_assert(m_factorizationIsOk &&
                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
                 "symbolic()/numeric()");
    RealScalar logDet = 0;
    Scalar* x = static_cast<Scalar*>(m_cholmodFactor->x);
    if (m_cholmodFactor->is_super) {
      // Supernodal factorization stored as a packed list of dense column-major blocks,
      // as described by the following structure:
      // super[k] == index of the first column of the j-th super node
      StorageIndex* super = static_cast<StorageIndex*>(m_cholmodFactor->super);
      // pi[k] == offset to the description of row indices
      StorageIndex* pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
      // px[k] == offset to the respective dense block
      StorageIndex* px = static_cast<StorageIndex*>(m_cholmodFactor->px);
      Index nb_super_nodes = m_cholmodFactor->nsuper;
      for (Index k = 0; k < nb_super_nodes; ++k) {
        StorageIndex ncols = super[k + 1] - super[k];
        StorageIndex nrows = pi[k + 1] - pi[k];
        Map<const Array<Scalar, 1, Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows + 1));
        logDet += sk.real().log().sum();
      }
    } else {
      // Simplicial factorization stored as standard CSC matrix.
      StorageIndex* p = static_cast<StorageIndex*>(m_cholmodFactor->p);
      Index size = m_cholmodFactor->n;
      for (Index k = 0; k < size; ++k) logDet += log(real(x[p[k]]));
    }
    if (m_cholmodFactor->is_ll) logDet *= 2.0;
    return logDet;
  }
  template <typename Stream>
  void dumpMemory(Stream& /*s*/) {}
 protected:
  mutable cholmod_common m_cholmod;
  cholmod_factor* m_cholmodFactor;
  double m_shiftOffset[2];
  mutable ComputationInfo m_info;
  int m_factorizationIsOk;
  int m_analysisIsOk;
 };
 /** \ingroup CholmodSupport_Module
 * \class CholmodSimplicialLLT
 * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
 *
 * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
 * using the Cholmod library.
 * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical
 * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
 * either dense or sparse.
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
 *               or Upper. Default is Lower.
 *
 * \implsparsesolverconcept
 *
 * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
 * compressed.
 *
 * \warning Only double precision real and complex scalar types are supported by Cholmod.
 *
 * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
 */
 template <typename MatrixType_, int UpLo_ = Lower>
 class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> > {
  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
  using Base::m_cholmod;
 public:
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  typedef TriangularView<const MatrixType, Eigen::Lower> MatrixL;
  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::Upper> MatrixU;
  CholmodSimplicialLLT() : Base() { init(); }
  CholmodSimplicialLLT(const MatrixType& matrix) : Base() {
    init();
    this->compute(matrix);
  }
  ~CholmodSimplicialLLT() {}
  /** \returns an expression of the factor L */
  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
  /** \returns an expression of the factor U (= L^*) */
  inline MatrixU matrixU() const { return matrixL().adjoint(); }
 protected:
  void init() {
    m_cholmod.final_asis = 0;
    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
    m_cholmod.final_ll = 1;
  }
 };
 /** \ingroup CholmodSupport_Module
 * \class CholmodSimplicialLDLT
 * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
 *
 * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
 * using the Cholmod library.
 * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical
 * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
 * either dense or sparse.
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
 *               or Upper. Default is Lower.
 *
 * \implsparsesolverconcept
 *
 * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
 * compressed.
 *
 * \warning Only double precision real and complex scalar types are supported by Cholmod.
 *
 * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
 */
 template <typename MatrixType_, int UpLo_ = Lower>
 class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> > {
  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
  using Base::m_cholmod;
 public:
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  typedef Matrix<Scalar, Dynamic, 1> VectorType;
  typedef TriangularView<const MatrixType, Eigen::UnitLower> MatrixL;
  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
  CholmodSimplicialLDLT() : Base() { init(); }
  CholmodSimplicialLDLT(const MatrixType& matrix) : Base() {
    init();
    this->compute(matrix);
  }
  ~CholmodSimplicialLDLT() {}
  /** \returns a vector expression of the diagonal D */
  inline VectorType vectorD() const {
    auto cholmodL = viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor);
    VectorType D{cholmodL.rows()};
    for (Index k = 0; k < cholmodL.outerSize(); ++k) {
      typename decltype(cholmodL)::InnerIterator it{cholmodL, k};
      D(k) = it.value();
    }
    return D;
  }
  /** \returns an expression of the factor L */
  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
  /** \returns an expression of the factor U (= L^*) */
  inline MatrixU matrixU() const { return matrixL().adjoint(); }
 protected:
  void init() {
    m_cholmod.final_asis = 1;
    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
  }
 };
 /** \ingroup CholmodSupport_Module
 * \class CholmodSupernodalLLT
 * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
 *
 * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
 * using the Cholmod library.
 * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
 * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
 * X and B can be either dense or sparse.
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
 *               or Upper. Default is Lower.
 *
 * \implsparsesolverconcept
 *
 * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
 * compressed.
 *
 * \warning Only double precision real and complex scalar types are supported by Cholmod.
 *
 * \sa \ref TutorialSparseSolverConcept
 */
 template <typename MatrixType_, int UpLo_ = Lower>
 class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> > {
  typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
  using Base::m_cholmod;
 public:
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  CholmodSupernodalLLT() : Base() { init(); }
  CholmodSupernodalLLT(const MatrixType& matrix) : Base() {
    init();
    this->compute(matrix);
  }
  ~CholmodSupernodalLLT() {}
  /** \returns an expression of the factor L */
  inline MatrixType matrixL() const {
    // Convert Cholmod factor's supernodal storage format to Eigen's CSC storage format
    cholmod_sparse* cholmodL = internal::cm_factor_to_sparse(*Base::m_cholmodFactor, m_cholmod);
    MatrixType L = viewAsEigen<Scalar, StorageIndex>(*cholmodL);
    internal::cm_free_sparse<StorageIndex>(cholmodL, m_cholmod);
    return L;
  }
  /** \returns an expression of the factor U (= L^*) */
  inline MatrixType matrixU() const { return matrixL().adjoint(); }
 protected:
  void init() {
    m_cholmod.final_asis = 1;
    m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
  }
 };
 /** \ingroup CholmodSupport_Module
 * \class CholmodDecomposition
 * \brief A general Cholesky factorization and solver based on Cholmod
 *
 * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
 * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
 * X and B can be either dense or sparse.
 *
 * This variant permits to change the underlying Cholesky method at runtime.
 * On the other hand, it does not provide access to the result of the factorization.
 * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
 *
 * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
 * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
 *               or Upper. Default is Lower.
 *
 * \implsparsesolverconcept
 *
 * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
 * compressed.
 *
 * \warning Only double precision real and complex scalar types are supported by Cholmod.
 *
 * \sa \ref TutorialSparseSolverConcept
 */
 template <typename MatrixType_, int UpLo_ = Lower>
 class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> > {
  typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
  using Base::m_cholmod;
 public:
  typedef MatrixType_ MatrixType;
  CholmodDecomposition() : Base() { init(); }
  CholmodDecomposition(const MatrixType& matrix) : Base() {
    init();
    this->compute(matrix);
  }
  ~CholmodDecomposition() {}
  void setMode(CholmodMode mode) {
    switch (mode) {
      case CholmodAuto:
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_AUTO;
        break;
      case CholmodSimplicialLLt:
        m_cholmod.final_asis = 0;
        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
        m_cholmod.final_ll = 1;
        break;
      case CholmodSupernodalLLt:
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
        break;
      case CholmodLDLt:
        m_cholmod.final_asis = 1;
        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
        break;
      default:
        break;
    }
  }
 protected:
  void init() {
    m_cholmod.final_asis = 1;
    m_cholmod.supernodal = CHOLMOD_AUTO;
  }
 };
 }  // end namespace Eigen
 #endif  // EIGEN_CHOLMODSUPPORT_H
--- a/Eigen/src/CholmodSupport/InternalHeaderCheck.h
+++ b/Eigen/src/CholmodSupport/InternalHeaderCheck.h
@@ -1,3 +0,0 @@
 #ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
 #error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
 #endif
--- a/Eigen/src/Core/ArithmeticSequence.h
+++ b/Eigen/src/Core/ArithmeticSequence.h
@@ -1,239 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_ARITHMETIC_SEQUENCE_H
 #define EIGEN_ARITHMETIC_SEQUENCE_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 // Helper to cleanup the type of the increment:
 template <typename T>
 struct cleanup_seq_incr {
  typedef typename cleanup_index_type<T, DynamicIndex>::type type;
 };
 }  // namespace internal
 //--------------------------------------------------------------------------------
 // seq(first,last,incr) and seqN(first,size,incr)
 //--------------------------------------------------------------------------------
 template <typename FirstType = Index, typename SizeType = Index, typename IncrType = internal::FixedInt<1> >
 class ArithmeticSequence;
 template <typename FirstType, typename SizeType, typename IncrType>
 ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
                   typename internal::cleanup_index_type<SizeType>::type,
                   typename internal::cleanup_seq_incr<IncrType>::type>
 seqN(FirstType first, SizeType size, IncrType incr);
 /** \class ArithmeticSequence
 * \ingroup Core_Module
 *
 * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
 * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
 * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
 *
 * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
 * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
 * only way it is used.
 *
 * \tparam FirstType type of the first element, usually an Index,
 *                   but internally it can be a symbolic expression
 * \tparam SizeType type representing the size of the sequence, usually an Index
 *                  or a compile time integral constant. Internally, it can also be a symbolic expression
 * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is
 * compile-time 1)
 *
 * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
 */
 template <typename FirstType, typename SizeType, typename IncrType>
 class ArithmeticSequence {
 public:
  constexpr ArithmeticSequence() = default;
  constexpr ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
  constexpr ArithmeticSequence(FirstType first, SizeType size, IncrType incr)
      : m_first(first), m_size(size), m_incr(incr) {}
  enum {
    // SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
    IncrAtCompileTime = internal::get_fixed_value<IncrType, DynamicIndex>::value
  };
  /** \returns the size, i.e., number of elements, of the sequence */
  constexpr Index size() const { return m_size; }
  /** \returns the first element \f$ a_0 \f$ in the sequence */
  constexpr Index first() const { return m_first; }
  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
  constexpr Index operator[](Index i) const { return m_first + i * m_incr; }
  constexpr const FirstType& firstObject() const { return m_first; }
  constexpr const SizeType& sizeObject() const { return m_size; }
  constexpr const IncrType& incrObject() const { return m_incr; }
 protected:
  FirstType m_first;
  SizeType m_size;
  IncrType m_incr;
 public:
  constexpr auto reverse() const -> decltype(Eigen::seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr)) {
    return seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr);
  }
 };
 /** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
 *
 * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
 template <typename FirstType, typename SizeType, typename IncrType>
 ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
                   typename internal::cleanup_index_type<SizeType>::type,
                   typename internal::cleanup_seq_incr<IncrType>::type>
 seqN(FirstType first, SizeType size, IncrType incr) {
  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
                            typename internal::cleanup_index_type<SizeType>::type,
                            typename internal::cleanup_seq_incr<IncrType>::type>(first, size, incr);
 }
 /** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
 *
 * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
 template <typename FirstType, typename SizeType>
 ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
                   typename internal::cleanup_index_type<SizeType>::type>
 seqN(FirstType first, SizeType size) {
  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
                            typename internal::cleanup_index_type<SizeType>::type>(first, size);
 }
 #ifdef EIGEN_PARSED_BY_DOXYGEN
 /** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a
 * incr
 *
 * It is essentially an alias to:
 * \code
 * seqN(f, (l-f+incr)/incr, incr);
 * \endcode
 *
 * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
 */
 template <typename FirstType, typename LastType, typename IncrType>
 auto seq(FirstType f, LastType l, IncrType incr);
 /** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
 *
 * It is essentially an alias to:
 * \code
 * seqN(f,l-f+1);
 * \endcode
 *
 * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
 */
 template <typename FirstType, typename LastType>
 auto seq(FirstType f, LastType l);
 #else  // EIGEN_PARSED_BY_DOXYGEN
 template <typename FirstType, typename LastType>
 auto seq(FirstType f, LastType l)
    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
                     (typename internal::cleanup_index_type<LastType>::type(l) -
                      typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()))) {
  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
              (typename internal::cleanup_index_type<LastType>::type(l) -
               typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()));
 }
 template <typename FirstType, typename LastType, typename IncrType>
 auto seq(FirstType f, LastType l, IncrType incr)
    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
                     (typename internal::cleanup_index_type<LastType>::type(l) -
                      typename internal::cleanup_index_type<FirstType>::type(f) +
                      typename internal::cleanup_seq_incr<IncrType>::type(incr)) /
                         typename internal::cleanup_seq_incr<IncrType>::type(incr),
                     typename internal::cleanup_seq_incr<IncrType>::type(incr))) {
  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
              (typename internal::cleanup_index_type<LastType>::type(l) -
               typename internal::cleanup_index_type<FirstType>::type(f) + CleanedIncrType(incr)) /
                  CleanedIncrType(incr),
              CleanedIncrType(incr));
 }
 #endif  // EIGEN_PARSED_BY_DOXYGEN
 namespace placeholders {
 /** \cpp11
 * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
 *
 * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
 * \anchor Eigen_placeholders_lastN
 * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
 template <typename SizeType, typename IncrType>
 auto lastN(SizeType size, IncrType incr)
    -> decltype(seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr)) {
  return seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr);
 }
 /** \cpp11
 * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
 *
 *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
 *
 * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
 template <typename SizeType>
 auto lastN(SizeType size) -> decltype(seqN(Eigen::placeholders::last + fix<1>() - size, size)) {
  return seqN(Eigen::placeholders::last + fix<1>() - size, size);
 }
 }  // namespace placeholders
 /** \namespace Eigen::indexing
  * \ingroup Core_Module
  *
  * The sole purpose of this namespace is to be able to import all functions
  * and symbols that are expected to be used within operator() for indexing
  * and slicing. If you already imported the whole Eigen namespace:
  * \code using namespace Eigen; \endcode
  * then you are already all set. Otherwise, if you don't want/cannot import
  * the whole Eigen namespace, the following line:
  * \code using namespace Eigen::indexing; \endcode
  * is equivalent to:
  * \code
  using Eigen::fix;
  using Eigen::seq;
  using Eigen::seqN;
  using Eigen::placeholders::all;
  using Eigen::placeholders::last;
  using Eigen::placeholders::lastN;  // c++11 only
  using Eigen::placeholders::lastp1;
  \endcode
  */
 namespace indexing {
 using Eigen::fix;
 using Eigen::seq;
 using Eigen::seqN;
 using Eigen::placeholders::all;
 using Eigen::placeholders::last;
 using Eigen::placeholders::lastN;
 using Eigen::placeholders::lastp1;
 }  // namespace indexing
 }  // end namespace Eigen
 #endif  // EIGEN_ARITHMETIC_SEQUENCE_H
--- a/Eigen/src/Core/Array.h
+++ b/Eigen/src/Core/Array.h
@@ -3,27 +3,28 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_ARRAY_H
 #define EIGEN_ARRAY_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
 struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>>
    : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
  typedef ArrayXpr XprKind;
  typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> XprBase;
 };
 }  // namespace internal
 /** \class Array 
  * \ingroup Core_Module
  *
@@ -36,21 +37,30 @@ struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>>
  * API for the %Matrix class provides easy access to linear-algebra
  * operations.
  *
 * See documentation of class Matrix for detailed information on the template parameters
 * storage layout.
 *
  * This class can be extended with the help of the plugin mechanism described on the page
- * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
  *
- * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
+  * \sa \ref TutorialArrayClass, \ref TopicClassHierarchy
  */
-template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+namespace internal {
-class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
 struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
 {
  typedef ArrayXpr XprKind;
  typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
 };
 }
 template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
 class Array
  : public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
 {
  public:
    typedef PlainObjectBase<Array> Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(Array)
-  enum { Options = Options_ };
+    enum { Options = _Options };
    typedef typename Base::PlainObject PlainObject;
  protected:
@@ -60,6 +70,7 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
    using Base::m_storage;
  public:
    using Base::base;
    using Base::coeff;
    using Base::coeffRef;
@@ -71,23 +82,11 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
      * the usage of 'using'. This should be done only for operator=.
      */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived>& other) {
+    EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
    {
      return Base::operator=(other);
    }
  /** Set all the entries to \a value.
   * \sa DenseBase::setConstant(), DenseBase::fill()
   */
  /* This overload is needed because the usage of
   *   using Base::operator=;
   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
   * the usage of 'using'. This should be done only for operator=.
   */
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Scalar& value) {
    Base::setConstant(value);
    return *this;
  }
    /** Copies the value of the expression \a other into \c *this with automatic resizing.
      *
      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
@@ -98,19 +97,18 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
      * remain row-vectors and vectors remain vectors.
      */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
    {
      return Base::_set(other);
    }
-  /**
+    /** This is a special case of the templated operator=. Its purpose is to
-   * \brief Assigns arrays to each other.
+      * prevent a default operator= from hiding the templated operator=.
   *
   * \note This is a special case of the templated operator=. Its purpose is
   * to prevent a default operator= from hiding the templated operator=.
   *
   * \callgraph
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Array& other) { return Base::_set(other); }
+    EIGEN_STRONG_INLINE Array& operator=(const Array& other)
    {
      return Base::_set(other);
    }
    /** Default constructor.
      *
@@ -122,143 +120,129 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
      *
      * \sa resize(Index,Index)
      */
-#ifdef EIGEN_INITIALIZE_COEFFS
+    EIGEN_STRONG_INLINE explicit Array() : Base()
-  EIGEN_DEVICE_FUNC constexpr Array() : Base() { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+    {
-#else
+      Base::_check_template_params();
-  EIGEN_DEVICE_FUNC constexpr Array() = default;
+      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
 #endif
  /** \brief Move constructor */
  EIGEN_DEVICE_FUNC constexpr Array(Array&&) = default;
  EIGEN_DEVICE_FUNC Array& operator=(Array&& other) noexcept(std::is_nothrow_move_assignable<Scalar>::value) {
    Base::operator=(std::move(other));
    return *this;
    }
  /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
   *
   * \only_for_vectors
   *
   * This constructor is for 1D array or vectors with more than 4 coefficients.
   *
   * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
   * constructor must match the fixed number of rows (resp. columns) of \c *this.
   *
   *
   * Example: \include Array_variadic_ctor_cxx11.cpp
   * Output: \verbinclude Array_variadic_ctor_cxx11.out
   *
   * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
   * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
   */
  template <typename... ArgTypes>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3,
                                              const ArgTypes&... args)
      : Base(a0, a1, a2, a3, args...) {}
  /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row.
   * \cpp11
   *
   * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
   *
   * Example: \include Array_initializer_list_23_cxx11.cpp
   * Output: \verbinclude Array_initializer_list_23_cxx11.out
   *
   * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is
   * triggered.
   *
   * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
   * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
   * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
   *
   * Example: \include Array_initializer_list_vector_cxx11.cpp
   * Output: \verbinclude Array_initializer_list_vector_cxx11.out
   *
   * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
   * and implicit transposition is allowed for compile-time 1D arrays only.
   *
   * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
   */
  EIGEN_DEVICE_FUNC constexpr Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
 #ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename T>
+    // FIXME is it still needed ??
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(const T& x) {
+    /** \internal */
-    Base::template _init1<T>(x);
+    Array(internal::constructor_without_unaligned_array_assert)
      : Base(internal::constructor_without_unaligned_array_assert())
    {
      Base::_check_template_params();
      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
    }
 #endif
  template <typename T0, typename T1>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) {
    this->template _init2<T0, T1>(val0, val1);
  }
 #else
  /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
  EIGEN_DEVICE_FUNC explicit Array(const Scalar* data);
    /** Constructs a vector or row-vector with given dimension. \only_for_vectors
      *
      * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
      * it is redundant to pass the dimension here, so it makes more sense to use the default
-   * constructor Array() instead.
+      * constructor Matrix() instead.
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(Index dim);
+    EIGEN_STRONG_INLINE explicit Array(Index dim)
-  /** constructs an initialized 1x1 Array with the given coefficient
+      : Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
-   * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
+    {
-  Array(const Scalar& value);
+      Base::_check_template_params();
-  /** constructs an uninitialized array with \a rows rows and \a cols columns.
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
      eigen_assert(dim >= 0);
      eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
    }
    #ifndef EIGEN_PARSED_BY_DOXYGEN
    template<typename T0, typename T1>
    EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
    {
      Base::_check_template_params();
      this->template _init2<T0,T1>(x, y);
    }
    #else
    /** constructs an uninitialized matrix with \a rows rows and \a cols columns.
      *
-   * This is useful for dynamic-size arrays. For fixed-size arrays,
+      * This is useful for dynamic-size matrices. For fixed-size matrices,
      * it is redundant to pass these parameters, so one should use the default constructor
-   * Array() instead. */
+      * Matrix() instead. */
    Array(Index rows, Index cols);
-  /** constructs an initialized 2D vector with given coefficients
+    /** constructs an initialized 2D vector with given coefficients */
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
+    Array(const Scalar& x, const Scalar& y);
-  Array(const Scalar& val0, const Scalar& val1);
+    #endif
 #endif  // end EIGEN_PARSED_BY_DOXYGEN
-  /** constructs an initialized 3D vector with given coefficients
+    /** constructs an initialized 3D vector with given coefficients */
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+    EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
-   */
+    {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) {
+      Base::_check_template_params();
      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
-    m_storage.data()[0] = val0;
+      m_storage.data()[0] = x;
-    m_storage.data()[1] = val1;
+      m_storage.data()[1] = y;
-    m_storage.data()[2] = val2;
+      m_storage.data()[2] = z;
    }
-  /** constructs an initialized 4D vector with given coefficients
+    /** constructs an initialized 4D vector with given coefficients */
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+    EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
-   */
+    {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2,
+      Base::_check_template_params();
                                              const Scalar& val3) {
      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
-    m_storage.data()[0] = val0;
+      m_storage.data()[0] = x;
-    m_storage.data()[1] = val1;
+      m_storage.data()[1] = y;
-    m_storage.data()[2] = val2;
+      m_storage.data()[2] = z;
-    m_storage.data()[3] = val3;
+      m_storage.data()[3] = w;
    }
    explicit Array(const Scalar *data);
    /** Constructor copying the value of the expression \a other */
    template<typename OtherDerived>
    EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
             : Base(other.rows() * other.cols(), other.rows(), other.cols())
    {
      Base::_check_template_params();
      Base::_set_noalias(other);
    }
    /** Copy constructor */
-  EIGEN_DEVICE_FUNC constexpr Array(const Array&) = default;
+    EIGEN_STRONG_INLINE Array(const Array& other)
            : Base(other.rows() * other.cols(), other.rows(), other.cols())
    {
      Base::_check_template_params();
      Base::_set_noalias(other);
    }
    /** Copy constructor with in-place evaluation */
    template<typename OtherDerived>
    EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
    {
      Base::_check_template_params();
      Base::resize(other.rows(), other.cols());
      other.evalTo(*this);
    }
 private:
  struct PrivateType {};
 public:
    /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Array(
+    EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
-      const EigenBase<OtherDerived>& other,
+      : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
-      std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar, Scalar>::value, PrivateType> =
+    {
-          PrivateType())
+      Base::_check_template_params();
-      : Base(other.derived()) {}
+      Base::resize(other.rows(), other.cols());
      *this = other;
    }
-  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return 1; }
+    /** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
-  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return this->innerSize(); }
+      * data pointers.
      */
    template<typename OtherDerived>
    void swap(ArrayBase<OtherDerived> const & other)
    { this->_swap(other.derived()); }
    inline Index innerStride() const { return 1; }
    inline Index outerStride() const { return this->innerSize(); }
    #ifdef EIGEN_ARRAY_PLUGIN
    #include EIGEN_ARRAY_PLUGIN
    #endif
  private:
    template<typename MatrixType, typename OtherDerived, bool SwapPointers>
    friend struct internal::matrix_swap_impl;
 };
@@ -266,26 +250,19 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
 /** \defgroup arraytypedefs Global array typedefs
  * \ingroup Core_Module
  *
- * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
+  * Eigen defines several typedef shortcuts for most common 1D and 2D array types.
  *
  * The general patterns are the following:
  *
- * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for
+  * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
- * dynamic size, and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c
+  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
- * cd for complex double.
+  * for complex double.
  *
- * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of
+  * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
 * floats.
  *
  * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
  * a fixed-size 1D array of 4 complex floats.
  *
 * With \cpp11, template alias are also defined for common sizes.
 * They follow the same pattern as above except that the scalar type suffix is replaced by a
 * template parameter, i.e.:
 *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
 *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
 *
  * \sa class Array
  */
@@ -318,38 +295,8 @@ EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
 #undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
 #undef EIGEN_MAKE_ARRAY_TYPEDEFS
 #undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)              \
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE
  /** \ingroup arraytypedefs */                                  \
  /** \brief \cpp11 */                                           \
  template <typename Type>                                       \
  using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>; \
  /** \ingroup arraytypedefs */                                  \
  /** \brief \cpp11 */                                           \
  template <typename Type>                                       \
  using Array##SizeSuffix = Array<Type, Size, 1>;
 #define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)        \
  /** \ingroup arraytypedefs */                      \
  /** \brief \cpp11 */                               \
  template <typename Type>                           \
  using Array##Size##X = Array<Type, Size, Dynamic>; \
  /** \ingroup arraytypedefs */                      \
  /** \brief \cpp11 */                               \
  template <typename Type>                           \
  using Array##X##Size = Array<Type, Dynamic, Size>;
 EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
 EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
 EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
 EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
 EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
 EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
 EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
 #undef EIGEN_MAKE_ARRAY_TYPEDEFS
 #undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
 #define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
 using Eigen::Matrix##SizeSuffix##TypeSuffix; \
@@ -360,7 +307,7 @@ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X)
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
 #define EIGEN_USING_ARRAY_TYPEDEFS \
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
@@ -369,6 +316,5 @@ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
 EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
 }  // end namespace Eigen
 #endif // EIGEN_ARRAY_H
--- a/Eigen/src/Core/ArrayBase.h
+++ b/Eigen/src/Core/ArrayBase.h
@@ -3,20 +3,29 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_ARRAYBASE_H
 #define EIGEN_ARRAYBASE_H
-// IWYU pragma: private
+template<typename ExpressionType> class MatrixWrapper;
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 template <typename ExpressionType>
 class MatrixWrapper;
 /** \class ArrayBase
  * \ingroup Core_Module
@@ -25,7 +34,7 @@ class MatrixWrapper;
  *
  * An array is similar to a dense vector or matrix. While matrices are mathematical
  * objects with well defined linear algebra operators, an array is just a collection
- * of scalar values arranged in a one or two dimensional fashion. As the main consequence,
+  * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
  * all operations applied to an array are performed coefficient wise. Furthermore,
  * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
  * constructors allowing to easily write generic code working for both scalar values
@@ -36,12 +45,13 @@ class MatrixWrapper;
  * \tparam Derived is the derived type, e.g., an array or an expression type.
  *
  * This class can be extended with the help of the plugin mechanism described on the page
- * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
  *
  * \sa class MatrixBase, \ref TopicClassHierarchy
  */
-template <typename Derived>
+template<typename Derived> class ArrayBase
-class ArrayBase : public DenseBase<Derived> {
+  : public DenseBase<Derived>
 {
  public:
 #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** The base class for a given storage type. */
@@ -49,30 +59,34 @@ class ArrayBase : public DenseBase<Derived> {
    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
    using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
                typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
    typedef typename internal::traits<Derived>::StorageKind StorageKind;
    typedef typename internal::traits<Derived>::Index Index;
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
    typedef typename NumTraits<Scalar>::Real RealScalar;
    typedef DenseBase<Derived> Base;
  using Base::ColsAtCompileTime;
  using Base::Flags;
  using Base::IsVectorAtCompileTime;
  using Base::MaxColsAtCompileTime;
  using Base::MaxRowsAtCompileTime;
  using Base::MaxSizeAtCompileTime;
    using Base::RowsAtCompileTime;
    using Base::ColsAtCompileTime;
    using Base::SizeAtCompileTime;
    using Base::MaxRowsAtCompileTime;
    using Base::MaxColsAtCompileTime;
    using Base::MaxSizeAtCompileTime;
    using Base::IsVectorAtCompileTime;
    using Base::Flags;
    using Base::CoeffReadCost;
    using Base::derived;
    using Base::const_cast_derived;
    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::coeff;
    using Base::coeffRef;
  using Base::cols;
  using Base::const_cast_derived;
  using Base::derived;
    using Base::lazyAssign;
  using Base::rows;
  using Base::size;
  using Base::operator-;
    using Base::operator=;
    using Base::operator+=;
    using Base::operator-=;
@@ -81,49 +95,102 @@ class ArrayBase : public DenseBase<Derived> {
    typedef typename Base::CoeffReturnType CoeffReturnType;
-  typedef typename Base::PlainObject PlainObject;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** \internal the plain matrix type corresponding to this expression. Note that is not necessarily
      * exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const
      * reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either
      * PlainObject or const PlainObject&.
      */
    typedef Array<typename internal::traits<Derived>::Scalar,
                internal::traits<Derived>::RowsAtCompileTime,
                internal::traits<Derived>::ColsAtCompileTime,
                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
                internal::traits<Derived>::MaxRowsAtCompileTime,
                internal::traits<Derived>::MaxColsAtCompileTime
          > PlainObject;
    /** \internal Represents a matrix with all coefficients equal to one another*/
-  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
 #endif // not EIGEN_PARSED_BY_DOXYGEN
 #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
-#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#   include "../plugins/CommonCwiseUnaryOps.h"
-#include "../plugins/MatrixCwiseUnaryOps.inc"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
-#include "../plugins/ArrayCwiseUnaryOps.inc"
+#   include "../plugins/ArrayCwiseUnaryOps.h"
-#include "../plugins/CommonCwiseBinaryOps.inc"
+#   include "../plugins/CommonCwiseBinaryOps.h"
-#include "../plugins/MatrixCwiseBinaryOps.inc"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
-#include "../plugins/ArrayCwiseBinaryOps.inc"
+#   include "../plugins/ArrayCwiseBinaryOps.h"
 #   ifdef EIGEN_ARRAYBASE_PLUGIN
 #     include EIGEN_ARRAYBASE_PLUGIN
 #   endif
 #undef EIGEN_CURRENT_STORAGE_BASE_CLASS
 #undef EIGEN_DOC_UNARY_ADDONS
    /** Special case of the template operator=, in order to prevent the compiler
      * from generating a default operator= (issue hit with g++ 4.1)
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const ArrayBase& other) {
+    Derived& operator=(const ArrayBase& other)
-    internal::call_assignment(derived(), other.derived());
+    {
-    return derived();
+      return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
    }
-  /** Set all the entries to \a value.
+    Derived& operator+=(const Scalar& scalar)
-   * \sa DenseBase::setConstant(), DenseBase::fill() */
+    { return *this = derived() + scalar; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Scalar& value) {
+    Derived& operator-=(const Scalar& scalar)
-    Base::setConstant(value);
+    { return *this = derived() - scalar; }
    return derived();
  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const Scalar& other) {
+    template<typename OtherDerived>
-    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+    Derived& operator+=(const ArrayBase<OtherDerived>& other);
-                              internal::add_assign_op<Scalar, Scalar>());
+    template<typename OtherDerived>
-    return derived();
+    Derived& operator-=(const ArrayBase<OtherDerived>& other);
  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const Scalar& other) {
+    template<typename OtherDerived>
-    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+    Derived& operator*=(const ArrayBase<OtherDerived>& other);
-                              internal::sub_assign_op<Scalar, Scalar>());
+
    template<typename OtherDerived>
    Derived& operator/=(const ArrayBase<OtherDerived>& other);
  public:
    ArrayBase<Derived>& array() { return *this; }
    const ArrayBase<Derived>& array() const { return *this; }
    /** \returns an \link MatrixBase Matrix \endlink expression of this array
      * \sa MatrixBase::array() */
    MatrixWrapper<Derived> matrix() { return derived(); }
    const MatrixWrapper<Derived> matrix() const { return derived(); }
 //     template<typename Dest>
 //     inline void evalTo(Dest& dst) const { dst = matrix(); }
  protected:
    ArrayBase() : Base() {}
  private:
    explicit ArrayBase(Index);
    ArrayBase(Index,Index);
    template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
  protected:
    // mixing arrays and matrices is not legal
    template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
    {EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
    // mixing arrays and matrices is not legal
    template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
    {EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
 };
 /** replaces \c *this by \c *this - \a other.
  *
  * \returns a reference to \c *this
  */
 template<typename Derived>
 template<typename OtherDerived>
 EIGEN_STRONG_INLINE Derived &
 ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
 {
  SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
  tmp = other.derived();
  return derived();
 }
@@ -131,19 +198,13 @@ class ArrayBase : public DenseBase<Derived> {
  *
  * \returns a reference to \c *this
  */
 template<typename Derived>
 template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const ArrayBase<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived &
-    call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
-    return derived();
+{
-  }
+  SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
-
+  tmp = other.derived();
  /** replaces \c *this by \c *this - \a other.
   *
   * \returns a reference to \c *this
   */
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const ArrayBase<OtherDerived>& other) {
    call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
  return derived();
 }
@@ -151,9 +212,13 @@ class ArrayBase : public DenseBase<Derived> {
  *
  * \returns a reference to \c *this
  */
 template<typename Derived>
 template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*=(const ArrayBase<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived &
-    call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar, typename OtherDerived::Scalar>());
+ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
 {
  SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, OtherDerived> tmp(derived());
  tmp = other.derived();
  return derived();
 }
@@ -161,50 +226,14 @@ class ArrayBase : public DenseBase<Derived> {
  *
  * \returns a reference to \c *this
  */
 template<typename Derived>
 template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator/=(const ArrayBase<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived &
-    call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar, typename OtherDerived::Scalar>());
+ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
 {
  SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, OtherDerived> tmp(derived());
  tmp = other.derived();
  return derived();
 }
 public:
  EIGEN_DEVICE_FUNC constexpr ArrayBase<Derived>& array() { return *this; }
  EIGEN_DEVICE_FUNC constexpr const ArrayBase<Derived>& array() const { return *this; }
  /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
   * \sa MatrixBase::array() */
  EIGEN_DEVICE_FUNC constexpr MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
  EIGEN_DEVICE_FUNC constexpr const MatrixWrapper<const Derived> matrix() const {
    return MatrixWrapper<const Derived>(derived());
  }
 protected:
  EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
 private:
  explicit ArrayBase(Index);
  ArrayBase(Index, Index);
  template <typename OtherDerived>
  explicit ArrayBase(const ArrayBase<OtherDerived>&);
 protected:
  // mixing arrays and matrices is not legal
  template <typename OtherDerived>
  Derived& operator+=(const MatrixBase<OtherDerived>&) {
    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
    return *this;
  }
  // mixing arrays and matrices is not legal
  template <typename OtherDerived>
  Derived& operator-=(const MatrixBase<OtherDerived>&) {
    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
    return *this;
  }
 };
 }  // end namespace Eigen
 #endif // EIGEN_ARRAYBASE_H
--- a/Eigen/src/Core/ArrayWrapper.h
+++ b/Eigen/src/Core/ArrayWrapper.h
@@ -3,92 +3,133 @@
 //
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_ARRAYWRAPPER_H
 #define EIGEN_ARRAYWRAPPER_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 /** \class ArrayWrapper
  * \ingroup Core_Module
  *
  * \brief Expression of a mathematical vector or matrix as an array object
  *
  * This class is the return type of MatrixBase::array(), and most of the time
- * this is the only way it is used.
+  * this is the only way it is use.
  *
  * \sa MatrixBase::array(), class MatrixWrapper
  */
 namespace internal {
 template<typename ExpressionType>
-struct traits<ArrayWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+struct traits<ArrayWrapper<ExpressionType> >
  : public traits<typename remove_all<typename ExpressionType::Nested>::type >
 {
  typedef ArrayXpr XprKind;
  // Let's remove NestByRefBit
  enum {
    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
 };
-};
+}
 }  // namespace internal
 template<typename ExpressionType>
-class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
+class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
 {
  public:
    typedef ArrayBase<ArrayWrapper> Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
  typedef internal::remove_all_t<ExpressionType> NestedExpression;
-  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+    typedef typename internal::conditional<
-      ScalarWithConstIfNotLvalue;
+                       internal::is_lvalue<ExpressionType>::value,
                       Scalar,
                       const Scalar
                     >::type ScalarWithConstIfNotLvalue;
-  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+    typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
-  using Base::coeffRef;
+    inline ArrayWrapper(const ExpressionType& matrix) : m_expression(matrix) {}
-  EIGEN_DEVICE_FUNC constexpr explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix)
+    inline Index rows() const { return m_expression.rows(); }
-      : m_expression(matrix) {}
+    inline Index cols() const { return m_expression.cols(); }
    inline Index outerStride() const { return m_expression.outerStride(); }
    inline Index innerStride() const { return m_expression.innerStride(); }
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+    inline const Scalar* data() const { return m_expression.data(); }
  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
-  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline const CoeffReturnType coeff(Index row, Index col) const
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+    {
-
+      return m_expression.coeff(row, col);
  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
    return m_expression.coeffRef(rowId, colId);
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+    inline Scalar& coeffRef(Index row, Index col)
    {
      return m_expression.const_cast_derived().coeffRef(row, col);
    }
    inline const Scalar& coeffRef(Index row, Index col) const
    {
      return m_expression.const_cast_derived().coeffRef(row, col);
    }
    inline const CoeffReturnType coeff(Index index) const
    {
      return m_expression.coeff(index);
    }
    inline Scalar& coeffRef(Index index)
    {
      return m_expression.const_cast_derived().coeffRef(index);
    }
    inline const Scalar& coeffRef(Index index) const
    {
      return m_expression.const_cast_derived().coeffRef(index);
    }
    template<int LoadMode>
    inline const PacketScalar packet(Index row, Index col) const
    {
      return m_expression.template packet<LoadMode>(row, col);
    }
    template<int LoadMode>
    inline void writePacket(Index row, Index col, const PacketScalar& x)
    {
      m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
    }
    template<int LoadMode>
    inline const PacketScalar packet(Index index) const
    {
      return m_expression.template packet<LoadMode>(index);
    }
    template<int LoadMode>
    inline void writePacket(Index index, const PacketScalar& x)
    {
      m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
    }
    template<typename Dest>
-  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
+    inline void evalTo(Dest& dst) const { dst = m_expression; }
    dst = m_expression;
  }
  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
    return m_expression;
  }
  /** Forwards the resizing request to the nested expression
   * \sa DenseBase::resize(Index)  */
  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
  /** Forwards the resizing request to the nested expression
   * \sa DenseBase::resize(Index,Index)*/
  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
  protected:
-  NestedExpressionType m_expression;
+    const NestedExpressionType m_expression;
 };
 /** \class MatrixWrapper
@@ -97,70 +138,102 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
  * \brief Expression of an array as a mathematical vector or matrix
  *
  * This class is the return type of ArrayBase::matrix(), and most of the time
- * this is the only way it is used.
+  * this is the only way it is use.
  *
  * \sa MatrixBase::matrix(), class ArrayWrapper
  */
 namespace internal {
 template<typename ExpressionType>
-struct traits<MatrixWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+struct traits<MatrixWrapper<ExpressionType> >
 : public traits<typename remove_all<typename ExpressionType::Nested>::type >
 {
  typedef MatrixXpr XprKind;
  // Let's remove NestByRefBit
  enum {
    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
 };
-};
+}
 }  // namespace internal
 template<typename ExpressionType>
-class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> > {
+class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
 {
  public:
    typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
  typedef internal::remove_all_t<ExpressionType> NestedExpression;
-  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+    typedef typename internal::conditional<
-      ScalarWithConstIfNotLvalue;
+                       internal::is_lvalue<ExpressionType>::value,
                       Scalar,
                       const Scalar
                     >::type ScalarWithConstIfNotLvalue;
-  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+    typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
-  using Base::coeffRef;
+    inline MatrixWrapper(const ExpressionType& matrix) : m_expression(matrix) {}
-  EIGEN_DEVICE_FUNC constexpr explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+    inline Index rows() const { return m_expression.rows(); }
    inline Index cols() const { return m_expression.cols(); }
    inline Index outerStride() const { return m_expression.outerStride(); }
    inline Index innerStride() const { return m_expression.innerStride(); }
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+    inline const Scalar* data() const { return m_expression.data(); }
  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
-  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline const CoeffReturnType coeff(Index row, Index col) const
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+    {
-
+      return m_expression.coeff(row, col);
  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
    return m_expression.derived().coeffRef(rowId, colId);
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+    inline Scalar& coeffRef(Index row, Index col)
-
+    {
-  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
+      return m_expression.const_cast_derived().coeffRef(row, col);
    return m_expression;
    }
-  /** Forwards the resizing request to the nested expression
+    inline const Scalar& coeffRef(Index row, Index col) const
-   * \sa DenseBase::resize(Index)  */
+    {
-  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
+      return m_expression.derived().coeffRef(row, col);
-  /** Forwards the resizing request to the nested expression
+    }
-   * \sa DenseBase::resize(Index,Index)*/
+
-  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+    inline const CoeffReturnType coeff(Index index) const
    {
      return m_expression.coeff(index);
    }
    inline Scalar& coeffRef(Index index)
    {
      return m_expression.const_cast_derived().coeffRef(index);
    }
    inline const Scalar& coeffRef(Index index) const
    {
      return m_expression.const_cast_derived().coeffRef(index);
    }
    template<int LoadMode>
    inline const PacketScalar packet(Index row, Index col) const
    {
      return m_expression.template packet<LoadMode>(row, col);
    }
    template<int LoadMode>
    inline void writePacket(Index row, Index col, const PacketScalar& x)
    {
      m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
    }
    template<int LoadMode>
    inline const PacketScalar packet(Index index) const
    {
      return m_expression.template packet<LoadMode>(index);
    }
    template<int LoadMode>
    inline void writePacket(Index index, const PacketScalar& x)
    {
      m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
    }
  protected:
-  NestedExpressionType m_expression;
+    const NestedExpressionType m_expression;
 };
 }  // end namespace Eigen
 #endif // EIGEN_ARRAYWRAPPER_H
--- a/Eigen/src/Core/Assign.h
+++ b/Eigen/src/Core/Assign.h
@@ -5,80 +5,589 @@
 // Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_ASSIGN_H
 #define EIGEN_ASSIGN_H
-// IWYU pragma: private
+namespace internal {
 #include "./InternalHeaderCheck.h"
-namespace Eigen {
+/***************************************************************************
 * Part 1 : the logic deciding a strategy for traversal and unrolling       *
 ***************************************************************************/
 template <typename Derived, typename OtherDerived>
 struct assign_traits
 {
 public:
  enum {
    DstIsAligned = Derived::Flags & AlignedBit,
    DstHasDirectAccess = Derived::Flags & DirectAccessBit,
    SrcIsAligned = OtherDerived::Flags & AlignedBit,
    JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
  };
 private:
  enum {
    InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime)
              : int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime)
              : int(Derived::RowsAtCompileTime),
    InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime)
              : int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime)
              : int(Derived::MaxRowsAtCompileTime),
    MaxSizeAtCompileTime = Derived::SizeAtCompileTime,
    PacketSize = packet_traits<typename Derived::Scalar>::size
  };
  enum {
    StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
    MightVectorize = StorageOrdersAgree
                  && (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
    MayInnerVectorize  = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
                       && int(DstIsAligned) && int(SrcIsAligned),
    MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
    MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
                       && (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
      /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
         so it's only good for large enough sizes. */
    MaySliceVectorize  = MightVectorize && DstHasDirectAccess
                       && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize)
      /* slice vectorization can be slow, so we only want it if the slices are big, which is
         indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
         in a fixed-size matrix */
  };
 public:
  enum {
    Traversal = int(MayInnerVectorize)  ? int(InnerVectorizedTraversal)
              : int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
              : int(MaySliceVectorize)  ? int(SliceVectorizedTraversal)
              : int(MayLinearize)       ? int(LinearTraversal)
                                        : int(DefaultTraversal),
    Vectorized = int(Traversal) == InnerVectorizedTraversal
              || int(Traversal) == LinearVectorizedTraversal
              || int(Traversal) == SliceVectorizedTraversal
  };
 private:
  enum {
    UnrollingLimit      = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
    MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic
                       && int(OtherDerived::CoeffReadCost) != Dynamic
                       && int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
    MayUnrollInner      = int(InnerSize) != Dynamic
                       && int(OtherDerived::CoeffReadCost) != Dynamic
                       && int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
  };
 public:
  enum {
    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
                ? (
                    int(MayUnrollCompletely) ? int(CompleteUnrolling)
                  : int(MayUnrollInner)      ? int(InnerUnrolling)
                                             : int(NoUnrolling)
                  )
              : int(Traversal) == int(LinearVectorizedTraversal)
                ? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) )
              : int(Traversal) == int(LinearTraversal)
                ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) )
              : int(NoUnrolling)
  };
 #ifdef EIGEN_DEBUG_ASSIGN
  static void debug()
  {
    EIGEN_DEBUG_VAR(DstIsAligned)
    EIGEN_DEBUG_VAR(SrcIsAligned)
    EIGEN_DEBUG_VAR(JointAlignment)
    EIGEN_DEBUG_VAR(InnerSize)
    EIGEN_DEBUG_VAR(InnerMaxSize)
    EIGEN_DEBUG_VAR(PacketSize)
    EIGEN_DEBUG_VAR(StorageOrdersAgree)
    EIGEN_DEBUG_VAR(MightVectorize)
    EIGEN_DEBUG_VAR(MayLinearize)
    EIGEN_DEBUG_VAR(MayInnerVectorize)
    EIGEN_DEBUG_VAR(MayLinearVectorize)
    EIGEN_DEBUG_VAR(MaySliceVectorize)
    EIGEN_DEBUG_VAR(Traversal)
    EIGEN_DEBUG_VAR(UnrollingLimit)
    EIGEN_DEBUG_VAR(MayUnrollCompletely)
    EIGEN_DEBUG_VAR(MayUnrollInner)
    EIGEN_DEBUG_VAR(Unrolling)
  }
 #endif
 };
 /***************************************************************************
 * Part 2 : meta-unrollers
 ***************************************************************************/
 /************************
 *** Default traversal ***
 ************************/
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct assign_DefaultTraversal_CompleteUnrolling
 {
  enum {
    outer = Index / Derived1::InnerSizeAtCompileTime,
    inner = Index % Derived1::InnerSizeAtCompileTime
  };
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    dst.copyCoeffByOuterInner(outer, inner, src);
    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
  }
 };
 template<typename Derived1, typename Derived2, int Stop>
 struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
 };
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct assign_DefaultTraversal_InnerUnrolling
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
  {
    dst.copyCoeffByOuterInner(outer, Index, src);
    assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
  }
 };
 template<typename Derived1, typename Derived2, int Stop>
 struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
 };
 /***********************
 *** Linear traversal ***
 ***********************/
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct assign_LinearTraversal_CompleteUnrolling
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    dst.copyCoeff(Index, src);
    assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
  }
 };
 template<typename Derived1, typename Derived2, int Stop>
 struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
 };
 /**************************
 *** Inner vectorization ***
 **************************/
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct assign_innervec_CompleteUnrolling
 {
  enum {
    outer = Index / Derived1::InnerSizeAtCompileTime,
    inner = Index % Derived1::InnerSizeAtCompileTime,
    JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
  };
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
    assign_innervec_CompleteUnrolling<Derived1, Derived2,
      Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
  }
 };
 template<typename Derived1, typename Derived2, int Stop>
 struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
 };
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct assign_innervec_InnerUnrolling
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
  {
    dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
    assign_innervec_InnerUnrolling<Derived1, Derived2,
      Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
  }
 };
 template<typename Derived1, typename Derived2, int Stop>
 struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
 };
 /***************************************************************************
 * Part 3 : implementation of all cases
 ***************************************************************************/
 template<typename Derived1, typename Derived2,
         int Traversal = assign_traits<Derived1, Derived2>::Traversal,
         int Unrolling = assign_traits<Derived1, Derived2>::Unrolling>
 struct assign_impl;
 /************************
 *** Default traversal ***
 ************************/
 template<typename Derived1, typename Derived2, int Unrolling>
 struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling>
 {
  inline static void run(Derived1 &, const Derived2 &) { }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling>
 {
  typedef typename Derived1::Index Index;
  inline static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index innerSize = dst.innerSize();
    const Index outerSize = dst.outerSize();
    for(Index outer = 0; outer < outerSize; ++outer)
      for(Index inner = 0; inner < innerSize; ++inner)
        dst.copyCoeffByOuterInner(outer, inner, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
      ::run(dst, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling>
 {
  typedef typename Derived1::Index Index;
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index outerSize = dst.outerSize();
    for(Index outer = 0; outer < outerSize; ++outer)
      assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
        ::run(dst, src, outer);
  }
 };
 /***********************
 *** Linear traversal ***
 ***********************/
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling>
 {
  typedef typename Derived1::Index Index;
  inline static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index size = dst.size();
    for(Index i = 0; i < size; ++i)
      dst.copyCoeff(i, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
      ::run(dst, src);
  }
 };
 /**************************
 *** Inner vectorization ***
 **************************/
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling>
 {
  typedef typename Derived1::Index Index;
  inline static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index innerSize = dst.innerSize();
    const Index outerSize = dst.outerSize();
    const Index packetSize = packet_traits<typename Derived1::Scalar>::size;
    for(Index outer = 0; outer < outerSize; ++outer)
      for(Index inner = 0; inner < innerSize; inner+=packetSize)
        dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling>
 {
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
      ::run(dst, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling>
 {
  typedef typename Derived1::Index Index;
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index outerSize = dst.outerSize();
    for(Index outer = 0; outer < outerSize; ++outer)
      assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
        ::run(dst, src, outer);
  }
 };
 /***************************
 *** Linear vectorization ***
 ***************************/
 template <bool IsAligned = false>
 struct unaligned_assign_impl
 {
  template <typename Derived, typename OtherDerived>
  static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {}
 };
 template <>
 struct unaligned_assign_impl<false>
 {
  // MSVC must not inline this functions. If it does, it fails to optimize the
  // packet access path.
 #ifdef _MSC_VER
  template <typename Derived, typename OtherDerived>
  static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
 #else
  template <typename Derived, typename OtherDerived>
  static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
 #endif
  {
    for (typename Derived::Index index = start; index < end; ++index)
      dst.copyCoeff(index, src);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling>
 {
  typedef typename Derived1::Index Index;
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    const Index size = dst.size();
    typedef packet_traits<typename Derived1::Scalar> PacketTraits;
    enum {
      packetSize = PacketTraits::size,
      dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
      srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
    };
    const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0
                             : first_aligned(&dst.coeffRef(0), size);
    const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
    unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart);
    for(Index index = alignedStart; index < alignedEnd; index += packetSize)
    {
      dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src);
    }
    unaligned_assign_impl<>::run(src,dst,alignedEnd,size);
  }
 };
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling>
 {
  typedef typename Derived1::Index Index;
  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
  {
    enum { size = Derived1::SizeAtCompileTime,
           packetSize = packet_traits<typename Derived1::Scalar>::size,
           alignedSize = (size/packetSize)*packetSize };
    assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
  }
 };
 /**************************
 *** Slice vectorization ***
 ***************************/
 template<typename Derived1, typename Derived2>
 struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling>
 {
  typedef typename Derived1::Index Index;
  inline static void run(Derived1 &dst, const Derived2 &src)
  {
    typedef packet_traits<typename Derived1::Scalar> PacketTraits;
    enum {
      packetSize = PacketTraits::size,
      alignable = PacketTraits::AlignedOnScalar,
      dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
      srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
    };
    const Index packetAlignedMask = packetSize - 1;
    const Index innerSize = dst.innerSize();
    const Index outerSize = dst.outerSize();
    const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0;
    Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0
                       : first_aligned(&dst.coeffRef(0,0), innerSize);
    for(Index outer = 0; outer < outerSize; ++outer)
    {
      const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
      // do the non-vectorizable part of the assignment
      for(Index inner = 0; inner<alignedStart ; ++inner)
        dst.copyCoeffByOuterInner(outer, inner, src);
      // do the vectorizable part of the assignment
      for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
        dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src);
      // do the non-vectorizable part of the assignment
      for(Index inner = alignedEnd; inner<innerSize ; ++inner)
        dst.copyCoeffByOuterInner(outer, inner, src);
      alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
    }
  }
 };
 } // end namespace internal
 /***************************************************************************
 * Part 4 : implementation of DenseBase methods
 ***************************************************************************/
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::lazyAssign(
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
-    const DenseBase<OtherDerived>& other) {
+  ::lazyAssign(const DenseBase<OtherDerived>& other)
-  enum { SameType = internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value };
+{
  enum{
    SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
  };
  EIGEN_STATIC_ASSERT_LVALUE(Derived)
  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT(
+  EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
      SameType,
      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
 #ifdef EIGEN_DEBUG_ASSIGN
  internal::assign_traits<Derived, OtherDerived>::debug();
 #endif
  eigen_assert(rows() == other.rows() && cols() == other.cols());
-  internal::call_assignment_no_alias(derived(), other.derived());
+  internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
-
+                                                       : int(InvalidTraversal)>::run(derived(),other.derived());
 #ifndef EIGEN_NO_DEBUG
  checkTransposeAliasing(other.derived());
 #endif
  return derived();
 }
 namespace internal {
 template<typename Derived, typename OtherDerived,
         bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
         bool NeedToTranspose = Derived::IsVectorAtCompileTime
                && OtherDerived::IsVectorAtCompileTime
                && ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
                         // revert to || as soon as not needed anymore.
                    (int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
                && int(Derived::SizeAtCompileTime) != 1>
 struct assign_selector;
 template<typename Derived, typename OtherDerived>
 struct assign_selector<Derived,OtherDerived,false,false> {
  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
 };
 template<typename Derived, typename OtherDerived>
 struct assign_selector<Derived,OtherDerived,true,false> {
  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
 };
 template<typename Derived, typename OtherDerived>
 struct assign_selector<Derived,OtherDerived,false,true> {
  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
 };
 template<typename Derived, typename OtherDerived>
 struct assign_selector<Derived,OtherDerived,true,true> {
  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
 };
 } // end namespace internal
 template<typename Derived>
 template<typename OtherDerived>
 EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
 {
  return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
 }
 template<typename Derived>
 EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
 {
  return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
 }
 template<typename Derived>
 EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
 {
  return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
 }
 template<typename Derived>
 template <typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
-    const DenseBase<OtherDerived>& other) {
+{
-  internal::call_assignment(derived(), other.derived());
+  return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
  return derived();
 }
 template <typename Derived>
 EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other) {
  internal::call_assignment(derived(), other.derived());
  return derived();
 }
 template <typename Derived>
 EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other) {
  internal::call_assignment(derived(), other.derived());
  return derived();
 }
 template<typename Derived>
 template <typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
-    const DenseBase<OtherDerived>& other) {
+{
  internal::call_assignment(derived(), other.derived());
  return derived();
 }
 template <typename Derived>
 template <typename OtherDerived>
 EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
    const EigenBase<OtherDerived>& other) {
  internal::call_assignment(derived(), other.derived());
  return derived();
 }
 template <typename Derived>
 template <typename OtherDerived>
 EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
    const ReturnByValue<OtherDerived>& other) {
  other.derived().evalTo(derived());
  return derived();
 }
-}  // end namespace Eigen
+template<typename Derived>
 template<typename OtherDerived>
 EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
 {
  other.evalTo(derived());
  return derived();
 }
 #endif // EIGEN_ASSIGN_H
--- a/Eigen/src/Core/AssignEvaluator.h
+++ b/Eigen/src/Core/AssignEvaluator.h
--- a/Eigen/src/Core/Assign_AOCL.h
+++ b/Eigen/src/Core/Assign_AOCL.h
@@ -1,301 +0,0 @@
 /*
 * This Source Code Form is subject to the terms of the Mozilla Public
 * License, v. 2.0. If a copy of the MPL was not distributed with this
 * file, You can obtain one at https://mozilla.org/MPL/2.0/.
 *
 * Assign_AOCL.h - AOCL Vectorized Math Dispatch Layer for Eigen
 *
 * Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 *
 * Description:
 * ------------
 * This file implements a high-performance dispatch layer that automatically
 * routes Eigen's element-wise mathematical operations to AMD Optimizing CPU
 * Libraries (AOCL) Vector Math Library (VML) functions when beneficial for
 * performance.
 *
 * The dispatch system uses C++ template specialization to intercept Eigen's
 * assignment operations and redirect them to AOCL's VRDA functions, which
 * provide optimized implementations for AMD Zen architectures.
 *
 * Key Features:
 * -------------
 * 1. Automatic Dispatch: Seamlessly routes supported operations to AOCL without
 *    requiring code changes in user applications
 *
 * 2. Performance Optimization: Uses AOCL VRDA functions optimized for Zen
 * family processors with automatic SIMD instruction selection (AVX2, AVX-512)
 *
 * 3. Threshold-Based Activation: Only activates for vectors larger than
 *    EIGEN_AOCL_VML_THRESHOLD (default: 128 elements) to avoid overhead on
 * small vectors
 *
 * 4. Precision-Specific Handling:
 *    - Double precision: AOCL VRDA vectorized functions
 *    - Single precision: Scalar fallback (preserves correctness)
 *
 * 5. Memory Layout Compatibility: Ensures direct memory access and compatible
 *    storage orders between source and destination for optimal performance
 *
 * Supported Operations:
 * ---------------------
 * UNARY OPERATIONS (vector → vector):
 * - Transcendental: exp(), sin(), cos(), sqrt(), log(), log10(), log2()
 *
 * BINARY OPERATIONS (vector op vector → vector):
 * - Arithmetic: +, *, pow()
 *
 * Template Specialization Mechanism:
 * -----------------------------------
 * The system works by specializing Eigen's Assignment template for:
 * 1. CwiseUnaryOp with scalar_*_op functors (unary operations)
 * 2. CwiseBinaryOp with scalar_*_op functors (binary operations)
 * 3. Dense2Dense assignment context with AOCL-compatible traits
 *
 * Dispatch conditions (all must be true):
 * - Source and destination have DirectAccessBit (contiguous memory)
 * - Compatible storage orders (both row-major or both column-major)
 * - Vector size ≥ EIGEN_AOCL_VML_THRESHOLD or Dynamic size
 * - Supported data type (currently double precision for VRDA)
 *
 * Integration Example:
 * --------------------
 * // Standard Eigen code - no changes required
 * VectorXd x = VectorXd::Random(10000);
 * VectorXd y = VectorXd::Random(10000);
 * VectorXd result;
 *
 * // These operations are automatically dispatched to AOCL:
 * result = x.array().exp();              // → amd_vrda_exp()
 * result = x.array().sin();              // → amd_vrda_sin()
 * result = x.array() + y.array();        // → amd_vrda_add()
 * result = x.array().pow(y.array());     // → amd_vrda_pow()
 *
 * Configuration:
 * --------------
 * Required preprocessor definitions:
 * - EIGEN_USE_AOCL_ALL or EIGEN_USE_AOCL_MT: Enable AOCL integration
 * - EIGEN_USE_AOCL_VML: Enable Vector Math Library dispatch
 *
 * Compilation Requirements:
 * -------------------------
 * Include paths:
 * - AOCL headers: -I${AOCL_ROOT}/include
 * - Eigen headers: -I/path/to/eigen
 *
 * Link libraries:
 * - AOCL MathLib: -lamdlibm
 * - Standard math: -lm
 *
 * Compiler flags:
 * - Optimization: -O3 (required for inlining)
 * - Architecture: -march=znver5 or -march=native
 * - Vectorization: -mfma -mavx512f (if supported)
 *
 * Platform Support:
 * ------------------
 * - Primary: Linux x86_64 with AMD Zen family processors
 * - Compilers: GCC 8+, Clang 10+, AOCC (recommended)
 * - AOCL Version: 4.0+ (with VRDA support)
 *
 * Error Handling:
 * ---------------
 * - Graceful fallback to scalar operations for unsupported configurations
 * - Compile-time detection of AOCL availability
 * - Runtime size and alignment validation with eigen_assert()
 *
 * Developer:
 * ----------
 * Name: Sharad Saurabh Bhaskar
 * Email: shbhaska@amd.com
 * Organization: Advanced Micro Devices, Inc.
 */
 #ifndef EIGEN_ASSIGN_AOCL_H
 #define EIGEN_ASSIGN_AOCL_H
 namespace Eigen {
 namespace internal {
 // Traits for unary operations.
 template <typename Dst, typename Src> class aocl_assign_traits {
 private:
  enum {
    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
    SrcHasDirectAccess = !!(Src::Flags & DirectAccessBit),
    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
                                             : int(Dst::RowsAtCompileTime),
    LargeEnough =
        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
  };
 public:
  enum {
    EnableAoclVML = DstHasDirectAccess && SrcHasDirectAccess &&
                    StorageOrdersAgree && LargeEnough,
    Traversal = LinearTraversal
  };
 };
 // Traits for binary operations (e.g., add, pow).
 template <typename Dst, typename Lhs, typename Rhs>
 class aocl_assign_binary_traits {
 private:
  enum {
    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
    LhsHasDirectAccess = !!(Lhs::Flags & DirectAccessBit),
    RhsHasDirectAccess = !!(Rhs::Flags & DirectAccessBit),
    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Lhs::IsRowMajor)) &&
                         (int(Dst::IsRowMajor) == int(Rhs::IsRowMajor)),
    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
                                             : int(Dst::RowsAtCompileTime),
    LargeEnough =
        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
  };
 public:
  enum {
    EnableAoclVML = DstHasDirectAccess && LhsHasDirectAccess &&
                    RhsHasDirectAccess && StorageOrdersAgree && LargeEnough
  };
 };
 // Unary operation dispatch for float (scalar fallback).
 #define EIGEN_AOCL_VML_UNARY_CALL_FLOAT(EIGENOP)                               \
  template <typename DstXprType, typename SrcXprNested>                        \
  struct Assignment<                                                           \
      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>,    \
      assign_op<float, float>, Dense2Dense,                                    \
      std::enable_if_t<                                                        \
          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>           \
        SrcXprType;                                                            \
    static void run(DstXprType &dst, const SrcXprType &src,                    \
                    const assign_op<float, float> &) {                         \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
      Eigen::Index n = dst.size();                                             \
      if (n <= 0)                                                              \
        return;                                                                \
      const float *input =                                                     \
          reinterpret_cast<const float *>(src.nestedExpression().data());      \
      float *output = reinterpret_cast<float *>(dst.data());                   \
      for (Eigen::Index i = 0; i < n; ++i) {                                   \
        output[i] = std::EIGENOP(input[i]);                                    \
      }                                                                        \
    }                                                                          \
  };
 // Unary operation dispatch for double (AOCL vectorized).
 #define EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(EIGENOP, AOCLOP)                      \
  template <typename DstXprType, typename SrcXprNested>                        \
  struct Assignment<                                                           \
      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>,   \
      assign_op<double, double>, Dense2Dense,                                  \
      std::enable_if_t<                                                        \
          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>          \
        SrcXprType;                                                            \
    static void run(DstXprType &dst, const SrcXprType &src,                    \
                    const assign_op<double, double> &) {                       \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
      Eigen::Index n = dst.size();                                             \
      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
      if (n <= 0)                                                              \
        return;                                                                \
      const double *input =                                                    \
          reinterpret_cast<const double *>(src.nestedExpression().data());     \
      double *output = reinterpret_cast<double *>(dst.data());                 \
      int aocl_n = internal::convert_index<int>(n);                            \
      AOCLOP(aocl_n, const_cast<double *>(input), output);                     \
    }                                                                          \
  };
 // Instantiate unary calls for float (scalar).
 // EIGEN_AOCL_VML_UNARY_CALL_FLOAT(exp)
 // Instantiate unary calls for double (AOCL vectorized).
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp2, amd_vrda_exp2)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp, amd_vrda_exp)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sin, amd_vrda_sin)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cos, amd_vrda_cos)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sqrt, amd_vrda_sqrt)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cbrt, amd_vrda_cbrt)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(abs, amd_vrda_fabs)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log, amd_vrda_log)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log10, amd_vrda_log10)
 EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log2, amd_vrda_log2)
 // Binary operation dispatch for float (scalar fallback).
 #define EIGEN_AOCL_VML_BINARY_CALL_FLOAT(EIGENOP, STDFUNC)                     \
  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
  struct Assignment<                                                           \
      DstXprType,                                                              \
      CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,         \
                    RhsXprNested>,                                             \
      assign_op<float, float>, Dense2Dense,                                    \
      std::enable_if_t<aocl_assign_binary_traits<                              \
          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,   \
                          RhsXprNested>                                        \
        SrcXprType;                                                            \
    static void run(DstXprType &dst, const SrcXprType &src,                    \
                    const assign_op<float, float> &) {                         \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
      Eigen::Index n = dst.size();                                             \
      if (n <= 0)                                                              \
        return;                                                                \
      const float *lhs = reinterpret_cast<const float *>(src.lhs().data());    \
      const float *rhs = reinterpret_cast<const float *>(src.rhs().data());    \
      float *output = reinterpret_cast<float *>(dst.data());                   \
      for (Eigen::Index i = 0; i < n; ++i) {                                   \
        output[i] = STDFUNC(lhs[i], rhs[i]);                                   \
      }                                                                        \
    }                                                                          \
  };
 // Binary operation dispatch for double (AOCL vectorized).
 #define EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(EIGENOP, AOCLOP)                     \
  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
  struct Assignment<                                                           \
      DstXprType,                                                              \
      CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested,       \
                    RhsXprNested>,                                             \
      assign_op<double, double>, Dense2Dense,                                  \
      std::enable_if_t<aocl_assign_binary_traits<                              \
          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested, \
                          RhsXprNested>                                        \
        SrcXprType;                                                            \
    static void run(DstXprType &dst, const SrcXprType &src,                    \
                    const assign_op<double, double> &) {                       \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
      Eigen::Index n = dst.size();                                             \
      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
      if (n <= 0)                                                              \
        return;                                                                \
      const double *lhs = reinterpret_cast<const double *>(src.lhs().data());  \
      const double *rhs = reinterpret_cast<const double *>(src.rhs().data());  \
      double *output = reinterpret_cast<double *>(dst.data());                 \
      int aocl_n = internal::convert_index<int>(n);                            \
      AOCLOP(aocl_n, const_cast<double *>(lhs), const_cast<double *>(rhs), output); \
    }                                                                          \
  };
 // Instantiate binary calls for float (scalar).
 // EIGEN_AOCL_VML_BINARY_CALL_FLOAT(sum, std::plus<float>)  // Using
 // scalar_sum_op for addition EIGEN_AOCL_VML_BINARY_CALL_FLOAT(pow, std::pow)
 // Instantiate binary calls for double (AOCL vectorized).
 EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(sum, amd_vrda_add) // Using scalar_sum_op for addition
 EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(pow, amd_vrda_pow)
 EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(max, amd_vrda_fmax)
 EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(min, amd_vrda_fmin)
 } // namespace internal
 } // namespace Eigen
 #endif // EIGEN_ASSIGN_AOCL_H
--- a/Eigen/src/Core/Assign_MKL.h
+++ b/Eigen/src/Core/Assign_MKL.h
@@ -1,183 +0,0 @@
 /*
 Copyright (c) 2011, Intel Corporation. All rights reserved.
 Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:
 * Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.
 * Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.
 * Neither the name of Intel Corporation nor the names of its contributors may
   be used to endorse or promote products derived from this software without
   specific prior written permission.
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 ********************************************************************************
 *   Content : Eigen bindings to Intel(R) MKL
 *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
 ********************************************************************************
 */
 #ifndef EIGEN_ASSIGN_VML_H
 #define EIGEN_ASSIGN_VML_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename Dst, typename Src>
 class vml_assign_traits {
 private:
  enum {
    DstHasDirectAccess = Dst::Flags & DirectAccessBit,
    SrcHasDirectAccess = Src::Flags & DirectAccessBit,
    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
                : int(Dst::Flags) & RowMajorBit ? int(Dst::ColsAtCompileTime)
                                                : int(Dst::RowsAtCompileTime),
    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
                   : int(Dst::Flags) & RowMajorBit ? int(Dst::MaxColsAtCompileTime)
                                                   : int(Dst::MaxRowsAtCompileTime),
    MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
    MightEnableVml = bool(StorageOrdersAgree) && bool(DstHasDirectAccess) && bool(SrcHasDirectAccess) &&
                     Src::InnerStrideAtCompileTime == 1 && Dst::InnerStrideAtCompileTime == 1,
    MightLinearize = bool(MightEnableVml) && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
    VmlSize = bool(MightLinearize) ? MaxSizeAtCompileTime : InnerMaxSize,
    LargeEnough = (VmlSize == Dynamic) || VmlSize >= EIGEN_MKL_VML_THRESHOLD
  };
 public:
  enum { EnableVml = MightEnableVml && LargeEnough, Traversal = MightLinearize ? LinearTraversal : DefaultTraversal };
 };
 #define EIGEN_PP_EXPAND(ARG) ARG
 #if !defined(EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
 #define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
 #else
 #define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
 #endif
 #define EIGEN_VMLMODE_EXPAND_x_
 #define EIGEN_VMLMODE_PREFIX_xLA vm
 #define EIGEN_VMLMODE_PREFIX_x_ v
 #define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x, VMLMODE)
 #define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                      \
  template <typename DstXprType, typename SrcXprNested>                                                    \
  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>,              \
                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                       \
    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
      resize_if_allowed(dst, src, func);                                                                   \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == (int)LinearTraversal) {                \
        VMLOP(dst.size(), (const VMLTYPE *)src.nestedExpression().data(),                                  \
              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
      } else {                                                                                             \
        const Index outerSize = dst.outerSize();                                                           \
        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer, 0))         \
                                                    : &(src.nestedExpression().coeffRef(0, outer));        \
          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr,                                                 \
                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
        }                                                                                                  \
      }                                                                                                    \
    }                                                                                                      \
  };
 #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                \
  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), s##VMLOP), float, float, VMLMODE) \
  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), d##VMLOP), double, double, VMLMODE)
 #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                   \
  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), c##VMLOP), scomplex, \
                                   MKL_Complex8, VMLMODE)                                                 \
  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), z##VMLOP), dcomplex, \
                                   MKL_Complex16, VMLMODE)
 #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)  \
  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
 // EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(cbrt, Cbrt, _)
 #define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                        \
  template <typename DstXprType, typename SrcXprNested, typename Plain>                                    \
  struct Assignment<DstXprType,                                                                            \
                    CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,               \
                                  const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>,   \
                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,                       \
                          const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>            \
        SrcXprType;                                                                                        \
    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
      resize_if_allowed(dst, src, func);                                                                   \
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
      VMLTYPE exponent = reinterpret_cast<const VMLTYPE &>(src.rhs().functor().m_other);                   \
      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == LinearTraversal) {                     \
        VMLOP(dst.size(), (const VMLTYPE *)src.lhs().data(), exponent,                                     \
              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
      } else {                                                                                             \
        const Index outerSize = dst.outerSize();                                                           \
        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
          const EIGENTYPE *src_ptr =                                                                       \
              src.IsRowMajor ? &(src.lhs().coeffRef(outer, 0)) : &(src.lhs().coeffRef(0, outer));          \
          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr, exponent,                                       \
                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
        }                                                                                                  \
      }                                                                                                    \
    }                                                                                                      \
  };
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
 }  // end namespace internal
 }  // end namespace Eigen
 #endif  // EIGEN_ASSIGN_VML_H
--- a/Eigen/src/Core/BandMatrix.h
+++ b/Eigen/src/Core/BandMatrix.h
@@ -3,23 +3,36 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_BANDMATRIX_H
 #define EIGEN_BANDMATRIX_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template<typename Derived>
-class BandMatrixBase : public EigenBase<Derived> {
+class BandMatrixBase : public EigenBase<Derived>
 {
  public:
    enum {
      Flags = internal::traits<Derived>::Flags,
      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
@@ -33,20 +46,23 @@ class BandMatrixBase : public EigenBase<Derived> {
    };
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
-  typedef typename DenseMatrixType::StorageIndex StorageIndex;
+    typedef typename DenseMatrixType::Index Index;
    typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
    typedef EigenBase<Derived> Base;
  protected:
    enum {
-    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic,
+      DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
-    SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)
+                            ? 1 + Supers + Subs
                            : Dynamic,
      SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
    };
  public:
-  using Base::cols;
+    
    using Base::derived;
    using Base::rows;
    using Base::cols;
    /** \returns the number of super diagonals */
    inline Index supers() const { return derived().supers(); }
@@ -63,90 +79,94 @@ class BandMatrixBase : public EigenBase<Derived> {
    /** \returns a vector expression of the \a i -th column,
      * only the meaningful part is returned.
      * \warning the internal storage must be column major. */
-  inline Block<CoefficientsType, Dynamic, 1> col(Index i) {
+    inline Block<CoefficientsType,Dynamic,1> col(Index i)
-    EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+    {
      EIGEN_STATIC_ASSERT((Options&RowMajor)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
      Index start = 0;
      Index len = coeffs().rows();
-    if (i <= supers()) {
+      if (i<=supers())
      {
        start = supers()-i;
        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
-    } else if (i >= rows() - subs())
+      }
      else if (i>=rows()-subs())
        len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
      return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
    }
    /** \returns a vector expression of the main diagonal */
-  inline Block<CoefficientsType, 1, SizeAtCompileTime> diagonal() {
+    inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
-    return Block<CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
  }
    /** \returns a vector expression of the main diagonal (const version) */
-  inline const Block<const CoefficientsType, 1, SizeAtCompileTime> diagonal() const {
+    inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
-    return Block<const CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
  }
-  template <int Index>
+    template<int Index> struct DiagonalIntReturnType {
  struct DiagonalIntReturnType {
      enum {
-      ReturnOpposite =
+        ReturnOpposite = (Options&SelfAdjoint) && (((Index)>0 && Supers==0) || ((Index)<0 && Subs==0)),
          (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
        Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
        ActualIndex = ReturnOpposite ? -Index : Index,
-      DiagonalSize =
+        DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
          (RowsAtCompileTime == Dynamic || ColsAtCompileTime == Dynamic)
                     ? Dynamic
-              : (ActualIndex < 0 ? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
+                     : (ActualIndex<0
-                                 : min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+                     ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
                     : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
      };
      typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
-    typedef std::conditional_t<Conjugate, CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, BuildType>, BuildType>
+      typedef typename internal::conditional<Conjugate,
-        Type;
+                 CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
                 BuildType>::type Type;
    };
    /** \returns a vector expression of the \a N -th sub or super diagonal */
-  template <int N>
+    template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
-  inline typename DiagonalIntReturnType<N>::Type diagonal() {
+    {
      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
    }
    /** \returns a vector expression of the \a N -th sub or super diagonal */
-  template <int N>
+    template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
-  inline const typename DiagonalIntReturnType<N>::Type diagonal() const {
+    {
      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
    }
    /** \returns a vector expression of the \a i -th sub or super diagonal */
-  inline Block<CoefficientsType, 1, Dynamic> diagonal(Index i) {
+    inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
    {
      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
      return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
    }
    /** \returns a vector expression of the \a i -th sub or super diagonal */
-  inline const Block<const CoefficientsType, 1, Dynamic> diagonal(Index i) const {
+    inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
    {
      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
-    return Block<const CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1,
+      return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
                                                     diagonalLength(i));
    }
-  template <typename Dest>
+    template<typename Dest> inline void evalTo(Dest& dst) const
-  inline void evalTo(Dest& dst) const {
+    {
      dst.resize(rows(),cols());
      dst.setZero();
      dst.diagonal() = diagonal();
-    for (Index i = 1; i <= supers(); ++i) dst.diagonal(i) = diagonal(i);
+      for (Index i=1; i<=supers();++i)
-    for (Index i = 1; i <= subs(); ++i) dst.diagonal(-i) = diagonal(-i);
+        dst.diagonal(i) = diagonal(i);
      for (Index i=1; i<=subs();++i)
        dst.diagonal(-i) = diagonal(-i);
    }
-  DenseMatrixType toDenseMatrix() const {
+    DenseMatrixType toDenseMatrix() const
    {
      DenseMatrixType res(rows(),cols());
      evalTo(res);
      return res;
    }
  protected:
-  inline Index diagonalLength(Index i) const {
+
-    return i < 0 ? (std::min)(cols(), rows() + i) : (std::min)(rows(), cols() - i);
+    inline Index diagonalLength(Index i) const
-  }
+    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
 };
 /**
@@ -155,12 +175,12 @@ class BandMatrixBase : public EigenBase<Derived> {
  *
  * \brief Represents a rectangular matrix with a banded storage
  *
- * \tparam Scalar_ Numeric type, i.e. float, double, int
+  * \param _Scalar Numeric type, i.e. float, double, int
- * \tparam Rows_ Number of rows, or \b Dynamic
+  * \param Rows Number of rows, or \b Dynamic
- * \tparam Cols_ Number of columns, or \b Dynamic
+  * \param Cols Number of columns, or \b Dynamic
- * \tparam Supers_ Number of super diagonal
+  * \param Supers Number of super diagonal
- * \tparam Subs_ Number of sub diagonal
+  * \param Subs Number of sub diagonal
- * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
+  * \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to
  *                 column-major. The latter controls whether the matrix represents a selfadjoint 
  *                 matrix in which case either Supers of Subs have to be null.
@@ -168,116 +188,126 @@ class BandMatrixBase : public EigenBase<Derived> {
  * \sa class TridiagonalMatrix
  */
-template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
-struct traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-  typedef Scalar_ Scalar;
+{
  typedef _Scalar Scalar;
  typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
+  typedef DenseIndex Index;
  enum {
    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    RowsAtCompileTime = Rows_,
+    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = Cols_,
+    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = Rows_,
+    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = Cols_,
+    MaxColsAtCompileTime = _Cols,
    Flags = LvalueBit,
-    Supers = Supers_,
+    Supers = _Supers,
-    Subs = Subs_,
+    Subs = _Subs,
-    Options = Options_,
+    Options = _Options,
    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
  };
-  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor>
+  typedef Matrix<Scalar,DataRowsAtCompileTime,ColsAtCompileTime,Options&RowMajor?RowMajor:ColMajor> CoefficientsType;
      CoefficientsType;
 };
-template <typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
+template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
-class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_, Rows, Cols, Supers, Subs, Options> > {
+class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
 {
  public:
    typedef typename internal::traits<BandMatrix>::Scalar Scalar;
-  typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
+    typedef typename internal::traits<BandMatrix>::Index Index;
    typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
-  explicit inline BandMatrix(Index rows = Rows, Index cols = Cols, Index supers = Supers, Index subs = Subs)
+    inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
-      : m_coeffs(1 + supers + subs, cols), m_rows(rows), m_supers(supers), m_subs(subs) {}
+      : m_coeffs(1+supers+subs,cols),
        m_rows(rows), m_supers(supers), m_subs(subs)
    {
    }
    /** \returns the number of columns */
-  constexpr Index rows() const { return m_rows.value(); }
+    inline Index rows() const { return m_rows.value(); }
    /** \returns the number of rows */
-  constexpr Index cols() const { return m_coeffs.cols(); }
+    inline Index cols() const { return m_coeffs.cols(); }
    /** \returns the number of super diagonals */
-  constexpr Index supers() const { return m_supers.value(); }
+    inline Index supers() const { return m_supers.value(); }
    /** \returns the number of sub diagonals */
-  constexpr Index subs() const { return m_subs.value(); }
+    inline Index subs() const { return m_subs.value(); }
    inline const CoefficientsType& coeffs() const { return m_coeffs; }
    inline CoefficientsType& coeffs() { return m_coeffs; }
  protected:
    CoefficientsType m_coeffs;
    internal::variable_if_dynamic<Index, Rows>   m_rows;
    internal::variable_if_dynamic<Index, Supers> m_supers;
    internal::variable_if_dynamic<Index, Subs>   m_subs;
 };
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
 class BandMatrixWrapper;
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-struct traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-  typedef typename CoefficientsType_::Scalar Scalar;
+{
-  typedef typename CoefficientsType_::StorageKind StorageKind;
+  typedef typename _CoefficientsType::Scalar Scalar;
-  typedef typename CoefficientsType_::StorageIndex StorageIndex;
+  typedef typename _CoefficientsType::StorageKind StorageKind;
  typedef typename _CoefficientsType::Index Index;
  enum {
-    CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
+    CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
-    RowsAtCompileTime = Rows_,
+    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = Cols_,
+    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = Rows_,
+    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = Cols_,
+    MaxColsAtCompileTime = _Cols,
    Flags = LvalueBit,
-    Supers = Supers_,
+    Supers = _Supers,
-    Subs = Subs_,
+    Subs = _Subs,
-    Options = Options_,
+    Options = _Options,
    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
  };
-  typedef CoefficientsType_ CoefficientsType;
+  typedef _CoefficientsType CoefficientsType;
 };
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-class BandMatrixWrapper
+class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-    : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+{
  public:
    typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
    typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
-  typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
+    typedef typename internal::traits<BandMatrixWrapper>::Index Index;
-  explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows = Rows_, Index cols = Cols_,
+    inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
-                                    Index supers = Supers_, Index subs = Subs_)
+      : m_coeffs(coeffs),
-      : m_coeffs(coeffs), m_rows(rows), m_supers(supers), m_subs(subs) {
+        m_rows(rows), m_supers(supers), m_subs(subs)
    {
      EIGEN_UNUSED_VARIABLE(cols);
-    // eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
+      //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
    }
    /** \returns the number of columns */
-  constexpr Index rows() const { return m_rows.value(); }
+    inline Index rows() const { return m_rows.value(); }
    /** \returns the number of rows */
-  constexpr Index cols() const { return m_coeffs.cols(); }
+    inline Index cols() const { return m_coeffs.cols(); }
    /** \returns the number of super diagonals */
-  constexpr Index supers() const { return m_supers.value(); }
+    inline Index supers() const { return m_supers.value(); }
    /** \returns the number of sub diagonals */
-  constexpr Index subs() const { return m_subs.value(); }
+    inline Index subs() const { return m_subs.value(); }
    inline const CoefficientsType& coeffs() const { return m_coeffs; }
  protected:
    const CoefficientsType& m_coeffs;
-  internal::variable_if_dynamic<Index, Rows_> m_rows;
+    internal::variable_if_dynamic<Index, _Rows>   m_rows;
-  internal::variable_if_dynamic<Index, Supers_> m_supers;
+    internal::variable_if_dynamic<Index, _Supers> m_supers;
-  internal::variable_if_dynamic<Index, Subs_> m_subs;
+    internal::variable_if_dynamic<Index, _Subs>   m_subs;
 };
 /**
@@ -286,53 +316,31 @@ class BandMatrixWrapper
  *
  * \brief Represents a tridiagonal matrix with a compact banded storage
  *
- * \tparam Scalar Numeric type, i.e. float, double, int
+  * \param _Scalar Numeric type, i.e. float, double, int
- * \tparam Size Number of rows and cols, or \b Dynamic
+  * \param Size Number of rows and cols, or \b Dynamic
- * \tparam Options Can be 0 or \b SelfAdjoint
+  * \param _Options Can be 0 or \b SelfAdjoint
  *
  * \sa class BandMatrix
  */
 template<typename Scalar, int Size, int Options>
-class TridiagonalMatrix : public BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> {
+class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
 {
    typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
-  typedef typename Base::StorageIndex StorageIndex;
+    typedef typename Base::Index Index;
  public:
-  explicit TridiagonalMatrix(Index size = Size) : Base(size, size, Options & SelfAdjoint ? 0 : 1, 1) {}
+    TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
  inline typename Base::template DiagonalIntReturnType<1>::Type super() { return Base::template diagonal<1>(); }
  inline const typename Base::template DiagonalIntReturnType<1>::Type super() const {
    return Base::template diagonal<1>();
  }
  inline typename Base::template DiagonalIntReturnType<-1>::Type sub() { return Base::template diagonal<-1>(); }
  inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const {
    return Base::template diagonal<-1>();
  }
    inline typename Base::template DiagonalIntReturnType<1>::Type super()
    { return Base::template diagonal<1>(); }
    inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
    { return Base::template diagonal<1>(); }
    inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
    { return Base::template diagonal<-1>(); }
    inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
    { return Base::template diagonal<-1>(); }
  protected:
 };
 struct BandShape {};
 template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
 struct evaluator_traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> >
    : public evaluator_traits_base<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
  typedef BandShape Shape;
 };
 template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
 struct evaluator_traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> >
    : public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
  typedef BandShape Shape;
 };
 template <>
 struct AssignmentKind<DenseShape, BandShape> {
  typedef EigenBase2EigenBase Kind;
 };
 } // end namespace internal
 }  // end namespace Eigen
 #endif // EIGEN_BANDMATRIX_H
--- a/Eigen/src/Core/Block.h
+++ b/Eigen/src/Core/Block.h
@@ -4,90 +4,43 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_BLOCK_H
 #define EIGEN_BLOCK_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename XprType_, int BlockRows, int BlockCols, bool InnerPanel_>
 struct traits<Block<XprType_, BlockRows, BlockCols, InnerPanel_>> : traits<XprType_> {
  typedef typename traits<XprType_>::Scalar Scalar;
  typedef typename traits<XprType_>::StorageKind StorageKind;
  typedef typename traits<XprType_>::XprKind XprKind;
  typedef typename ref_selector<XprType_>::type XprTypeNested;
  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
  enum {
    MatrixRows = traits<XprType_>::RowsAtCompileTime,
    MatrixCols = traits<XprType_>::ColsAtCompileTime,
    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
    MaxRowsAtCompileTime = BlockRows == 0                 ? 0
                           : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
                                                          : int(traits<XprType_>::MaxRowsAtCompileTime),
    MaxColsAtCompileTime = BlockCols == 0                 ? 0
                           : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
                                                          : int(traits<XprType_>::MaxColsAtCompileTime),
    XprTypeIsRowMajor = (int(traits<XprType_>::Flags) & RowMajorBit) != 0,
    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
                                                                            : XprTypeIsRowMajor,
    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType_>::ret)
                                                            : int(outer_stride_at_compile_time<XprType_>::ret),
    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType_>::ret)
                                                            : int(inner_stride_at_compile_time<XprType_>::ret),
    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
    FlagsLvalueBit = is_lvalue<XprType_>::value ? LvalueBit : 0,
    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
    Flags = (traits<XprType_>::Flags & (DirectAccessBit | (InnerPanel_ ? CompressedAccessBit : 0))) | FlagsLvalueBit |
            FlagsRowMajorBit,
    // FIXME DirectAccessBit should not be handled by expressions
    //
    // Alignment is needed by MapBase's assertions
    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the
    // respective evaluator
    Alignment = 0,
    InnerPanel = InnerPanel_ ? 1 : 0
  };
 };
 template <typename XprType, int BlockRows = Dynamic, int BlockCols = Dynamic, bool InnerPanel = false,
          bool HasDirectAccess = internal::has_direct_access<XprType>::ret>
 class BlockImpl_dense;
 }  // end namespace internal
 template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind>
 class BlockImpl;
 /** \class Block
  * \ingroup Core_Module
  *
  * \brief Expression of a fixed-size or dynamic-size block
  *
- * \tparam XprType the type of the expression in which we are taking a block
+  * \param XprType the type of the expression in which we are taking a block
- * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
+  * \param BlockRows the number of rows of the block we are taking at compile time (optional)
- * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
+  * \param BlockCols the number of columns of the block we are taking at compile time (optional)
- * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
+  * \param _DirectAccessStatus \internal used for partial specialization
 *         to set of columns of a column major matrix (optional). The parameter allows to determine
 *         at compile time whether aligned access is possible on the block expression.
  *
  * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
  * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
  * most of the time this is the only way it is used.
  *
- * However, if you want to directly manipulate block expressions,
+  * However, if you want to directly maniputate block expressions,
  * for instance if you want to write a function returning such an expression, you
  * will need to use this class.
  *
@@ -105,100 +58,68 @@ class BlockImpl;
  *
  * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
  */
 template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
 class Block
    : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> {
  typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
  using BlockHelper = internal::block_xpr_helper<Block>;
 public:
  // typedef typename Impl::Base Base;
  typedef Impl Base;
  EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
  typedef internal::remove_all_t<XprType> NestedExpression;
  /** Column or Row constructor
   */
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index i) : Impl(xpr, i) {
    eigen_assert((i >= 0) && (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && i < xpr.rows()) ||
                              ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) && i < xpr.cols())));
  }
  /** Fixed-size constructor
   */
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol)
      : Impl(xpr, startRow, startCol) {
    EIGEN_STATIC_ASSERT(RowsAtCompileTime != Dynamic && ColsAtCompileTime != Dynamic,
                        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
    eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows() && startCol >= 0 &&
                 BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
  }
  /** Dynamic-size constructor
   */
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol, Index blockRows,
                                                        Index blockCols)
      : Impl(xpr, startRow, startCol, blockRows, blockCols) {
    eigen_assert((RowsAtCompileTime == Dynamic || RowsAtCompileTime == blockRows) &&
                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == blockCols));
    eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 &&
                 blockCols >= 0 && startCol <= xpr.cols() - blockCols);
  }
  // convert nested blocks (e.g. Block<Block<MatrixType>>) to a simple block expression (Block<MatrixType>)
  using ConstUnwindReturnType = Block<const typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
  using UnwindReturnType = Block<typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstUnwindReturnType unwind() const {
    return ConstUnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
                                 this->rows(), this->cols());
  }
  template <typename T = Block, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UnwindReturnType unwind() {
    return UnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
                            this->rows(), this->cols());
  }
 };
 // The generic default implementation for dense block simply forward to the internal::BlockImpl_dense
 // that must be specialized for direct and non-direct access...
 template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
 class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
    : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> {
  typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
  typedef typename XprType::StorageIndex StorageIndex;
 public:
  typedef Impl Base;
  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr, i) {}
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol)
      : Impl(xpr, startRow, startCol) {}
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol,
                                                            Index blockRows, Index blockCols)
      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
 };
 namespace internal {
 /** \internal Internal implementation of dense Blocks in the general case. */
 template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
-class BlockImpl_dense : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel>>::type {
+struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
-  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+{
-  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+  typedef typename traits<XprType>::Scalar Scalar;
  typedef typename traits<XprType>::StorageKind StorageKind;
  typedef typename traits<XprType>::XprKind XprKind;
  typedef typename nested<XprType>::type XprTypeNested;
  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
  enum{
    MatrixRows = traits<XprType>::RowsAtCompileTime,
    MatrixCols = traits<XprType>::ColsAtCompileTime,
    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
    MaxRowsAtCompileTime = BlockRows==0 ? 0
                         : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
                         : int(traits<XprType>::MaxRowsAtCompileTime),
    MaxColsAtCompileTime = BlockCols==0 ? 0
                         : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
                         : int(traits<XprType>::MaxColsAtCompileTime),
    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
               : XprTypeIsRowMajor,
    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
                             ? int(inner_stride_at_compile_time<XprType>::ret)
                             : int(outer_stride_at_compile_time<XprType>::ret),
    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
                             ? int(outer_stride_at_compile_time<XprType>::ret)
                             : int(inner_stride_at_compile_time<XprType>::ret),
    MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
                       && (InnerStrideAtCompileTime == 1)
                        ? PacketAccessBit : 0,
    MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
    Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
                                        DirectAccessBit |
                                        MaskPacketAccessBit |
                                        MaskAlignedBit),
    Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
  };
 };
 }
 template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
  : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
 {
  public:
-  typedef typename internal::dense_xpr_base<BlockType>::type Base;
+
-  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+    typedef typename internal::dense_xpr_base<Block>::type Base;
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+    EIGEN_DENSE_PUBLIC_INTERFACE(Block)
    class InnerIterator;
    /** Column or Row constructor
      */
-  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index i)
+    inline Block(XprType& xpr, Index i)
      : m_xpr(xpr),
        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
@@ -207,190 +128,196 @@ class BlockImpl_dense : public internal::dense_xpr_base<Block<XprType, BlockRows
        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols == 1 ? 1 : xpr.cols()) {}
+        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
    {
      eigen_assert( (i>=0) && (
          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
    }
    /** Fixed-size constructor
      */
-  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+    inline Block(XprType& xpr, Index startRow, Index startCol)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(BlockRows), m_blockCols(BlockCols) {}
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
        m_blockRows(BlockRows), m_blockCols(BlockCols)
    {
      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
      eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
             && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
    }
    /** Dynamic-size constructor
      */
-  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+    inline Block(XprType& xpr,
-                                              Index blockCols)
+          Index startRow, Index startCol,
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) {}
+          Index blockRows, Index blockCols)
      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
                          m_blockRows(blockRows), m_blockCols(blockCols)
    {
      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
          && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
    }
-  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_blockRows.value(); }
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_blockCols.value(); }
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index rowId, Index colId) {
+    inline Index rows() const { return m_blockRows.value(); }
    inline Index cols() const { return m_blockCols.value(); }
    inline Scalar& coeffRef(Index row, Index col)
    {
      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+      return m_xpr.const_cast_derived()
               .coeffRef(row + m_startRow.value(), col + m_startCol.value());
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    inline const Scalar& coeffRef(Index row, Index col) const
-    return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+    {
      return m_xpr.derived()
               .coeffRef(row + m_startRow.value(), col + m_startCol.value());
    }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const {
+    EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
-    return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
+    {
      return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
    }
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) {
+    inline Scalar& coeffRef(Index index)
    {
      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+      return m_xpr.const_cast_derived()
             .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
                       m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
+    inline const Scalar& coeffRef(Index index) const
-    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+    {
      return m_xpr.const_cast_derived()
             .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
                       m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
    }
-  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const {
+    inline const CoeffReturnType coeff(Index index) const
-    return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+    {
      return m_xpr
             .coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
                    m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
    }
    template<int LoadMode>
-  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const {
+    inline PacketScalar packet(Index row, Index col) const
-    return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
+    {
      return m_xpr.template packet<Unaligned>
              (row + m_startRow.value(), col + m_startCol.value());
    }
    template<int LoadMode>
-  EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val) {
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
-    m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
+    {
      m_xpr.const_cast_derived().template writePacket<Unaligned>
              (row + m_startRow.value(), col + m_startCol.value(), x);
    }
    template<int LoadMode>
-  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const {
+    inline PacketScalar packet(Index index) const
-    return m_xpr.template packet<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+    {
      return m_xpr.template packet<Unaligned>
              (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
    }
    template<int LoadMode>
-  EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val) {
+    inline void writePacket(Index index, const PacketScalar& x)
-    m_xpr.template writePacket<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+    {
-                                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
+      m_xpr.const_cast_derived().template writePacket<Unaligned>
         (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
    }
    #ifdef EIGEN_PARSED_BY_DOXYGEN
    /** \sa MapBase::data() */
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const;
+    inline const Scalar* data() const;
-  EIGEN_DEVICE_FUNC inline Index innerStride() const;
+    inline Index innerStride() const;
-  EIGEN_DEVICE_FUNC inline Index outerStride() const;
+    inline Index outerStride() const;
    #endif
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const {
    return m_xpr;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
  protected:
-  XprTypeNested m_xpr;
+
-  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
+    const typename XprType::Nested m_xpr;
-      m_startRow;
+    const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
-  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
+    const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
-      m_startCol;
+    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
-  const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
+    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
  const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
 };
-/** \internal Internal implementation of dense Blocks in the direct access case.*/
+/** \internal */
 template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel, true>
+class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
-    : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel>> {
+  : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
-  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+{
  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
  enum { XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0 };
  /** \internal Returns base+offset (unless base is null, in which case returns null).
   * Adding an offset to nullptr is undefined behavior, so we must avoid it.
   */
  template <typename Scalar>
  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static Scalar* add_to_nullable_pointer(Scalar* base, Index offset) {
    return base != nullptr ? base + offset : nullptr;
  }
  public:
-  typedef MapBase<BlockType> Base;
+
-  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+    typedef MapBase<Block> Base;
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+    EIGEN_DENSE_PUBLIC_INTERFACE(Block)
    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
    /** Column or Row constructor
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index i)
+    inline Block(XprType& xpr, Index i)
-      : Base((BlockRows == 0 || BlockCols == 0)
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(
-                 ? nullptr
+              (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
-                 : add_to_nullable_pointer(
+              (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
-                       xpr.data(),
+             BlockRows==1 ? 1 : xpr.rows(),
-                       i * (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor)) ||
+             BlockCols==1 ? 1 : xpr.cols()),
-                                    ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) &&
+        m_xpr(xpr)
-                                     (XprTypeIsRowMajor))
+    {
-                                ? xpr.innerStride()
+      eigen_assert( (i>=0) && (
-                                : xpr.outerStride())),
+          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
-             BlockRows == 1 ? 1 : xpr.rows(), BlockCols == 1 ? 1 : xpr.cols()),
+        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
        m_xpr(xpr),
        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0) {
      init();
    }
    /** Fixed-size constructor
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+    inline Block(XprType& xpr, Index startRow, Index startCol)
-      : Base((BlockRows == 0 || BlockCols == 0)
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
-                 ? nullptr
+    {
-                 : add_to_nullable_pointer(xpr.data(),
+      eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
-                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+             && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol))),
        m_xpr(xpr),
        m_startRow(startRow),
        m_startCol(startCol) {
      init();
    }
    /** Dynamic-size constructor
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+    inline Block(XprType& xpr,
-                                                        Index blockCols)
+          Index startRow, Index startCol,
-      : Base((blockRows == 0 || blockCols == 0)
+          Index blockRows, Index blockCols)
-                 ? nullptr
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
-                 : add_to_nullable_pointer(xpr.data(),
+        m_xpr(xpr)
-                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+    {
-                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol)),
+      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
-             blockRows, blockCols),
+             && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
-        m_xpr(xpr),
+      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
-        m_startRow(startRow),
+             && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
        m_startCol(startCol) {
      init();
    }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const noexcept {
    return m_xpr;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
    /** \sa MapBase::innerStride() */
-  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept {
+    inline Index innerStride() const
-    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.innerStride() : m_xpr.outerStride();
+    {
      return internal::traits<Block>::HasSameStorageOrderAsXprType
             ? m_xpr.innerStride()
             : m_xpr.outerStride();
    }
    /** \sa MapBase::outerStride() */
-  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept {
+    inline Index outerStride() const
-    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+    {
      return m_outerStride;
    }
  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
  #ifndef __SUNPRO_CC
  // FIXME sunstudio is not friendly with the above friend...
  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
@@ -399,29 +326,24 @@ class BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel, true>
    #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** \internal used by allowAligned() */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows,
+    inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
-                                                        Index blockCols)
+      : Base(data, blockRows, blockCols), m_xpr(xpr)
-      : Base(data, blockRows, blockCols), m_xpr(xpr) {
+    {
      init();
    }
    #endif
  protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void init() {
+    void init()
-    m_outerStride =
+    {
-        internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+      m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
                    ? m_xpr.outerStride()
                    : m_xpr.innerStride();
    }
-  XprTypeNested m_xpr;
+    const typename XprType::Nested m_xpr;
  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
      m_startRow;
  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
      m_startCol;
    Index m_outerStride;
 };
 }  // end namespace internal
 }  // end namespace Eigen
 #endif // EIGEN_BLOCK_H
--- a/Eigen/src/Core/BooleanRedux.h
+++ b/Eigen/src/Core/BooleanRedux.h
@@ -0,0 +1,149 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
 // License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_ALLANDANY_H
 #define EIGEN_ALLANDANY_H
 namespace internal {
 template<typename Derived, int UnrollCount>
 struct all_unroller
 {
  enum {
    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
    row = (UnrollCount-1) % Derived::RowsAtCompileTime
  };
  inline static bool run(const Derived &mat)
  {
    return all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col);
  }
 };
 template<typename Derived>
 struct all_unroller<Derived, 1>
 {
  inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
 };
 template<typename Derived>
 struct all_unroller<Derived, Dynamic>
 {
  inline static bool run(const Derived &) { return false; }
 };
 template<typename Derived, int UnrollCount>
 struct any_unroller
 {
  enum {
    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
    row = (UnrollCount-1) % Derived::RowsAtCompileTime
  };
  inline static bool run(const Derived &mat)
  {
    return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
  }
 };
 template<typename Derived>
 struct any_unroller<Derived, 1>
 {
  inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
 };
 template<typename Derived>
 struct any_unroller<Derived, Dynamic>
 {
  inline static bool run(const Derived &) { return false; }
 };
 } // end namespace internal
 /** \returns true if all coefficients are true
  *
  * Example: \include MatrixBase_all.cpp
  * Output: \verbinclude MatrixBase_all.out
  *
  * \sa any(), Cwise::operator<()
  */
 template<typename Derived>
 inline bool DenseBase<Derived>::all() const
 {
  enum {
    unroll = SizeAtCompileTime != Dynamic
          && CoeffReadCost != Dynamic
          && NumTraits<Scalar>::AddCost != Dynamic
          && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
  };
  if(unroll)
    return internal::all_unroller<Derived,
                           unroll ? int(SizeAtCompileTime) : Dynamic
     >::run(derived());
  else
  {
    for(Index j = 0; j < cols(); ++j)
      for(Index i = 0; i < rows(); ++i)
        if (!coeff(i, j)) return false;
    return true;
  }
 }
 /** \returns true if at least one coefficient is true
  *
  * \sa all()
  */
 template<typename Derived>
 inline bool DenseBase<Derived>::any() const
 {
  enum {
    unroll = SizeAtCompileTime != Dynamic
          && CoeffReadCost != Dynamic
          && NumTraits<Scalar>::AddCost != Dynamic
          && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
  };
  if(unroll)
    return internal::any_unroller<Derived,
                           unroll ? int(SizeAtCompileTime) : Dynamic
           >::run(derived());
  else
  {
    for(Index j = 0; j < cols(); ++j)
      for(Index i = 0; i < rows(); ++i)
        if (coeff(i, j)) return true;
    return false;
  }
 }
 /** \returns the number of coefficients which evaluate to true
  *
  * \sa all(), any()
  */
 template<typename Derived>
 inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
 {
  return derived().template cast<bool>().template cast<Index>().sum();
 }
 #endif // EIGEN_ALLANDANY_H
--- a/Eigen/src/Core/CMakeLists.txt
+++ b/Eigen/src/Core/CMakeLists.txt
@@ -0,0 +1,10 @@
 FILE(GLOB Eigen_Core_SRCS "*.h")
 INSTALL(FILES
  ${Eigen_Core_SRCS}
  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
  )
 ADD_SUBDIRECTORY(products)
 ADD_SUBDIRECTORY(util)
 ADD_SUBDIRECTORY(arch)
--- a/Eigen/src/Core/CommaInitializer.h
+++ b/Eigen/src/Core/CommaInitializer.h
@@ -4,18 +4,28 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_COMMAINITIALIZER_H
 #define EIGEN_COMMAINITIALIZER_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 /** \class CommaInitializer
  * \ingroup Core_Module
  *
@@ -25,46 +35,40 @@ namespace Eigen {
  * the return type of MatrixBase::operator<<, and most of the time this is the only
  * way it is used.
  *
- * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
+  * \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
  */
 template<typename XprType>
-struct CommaInitializer {
+struct CommaInitializer
 {
  typedef typename XprType::Scalar Scalar;
  typedef typename XprType::Index Index;
-  EIGEN_DEVICE_FUNC constexpr CommaInitializer(XprType& xpr, const Scalar& s)
+  inline CommaInitializer(XprType& xpr, const Scalar& s)
-      : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) {
+    : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
-    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0 && "Cannot comma-initialize a 0x0 matrix (operator<<)");
+  {
    m_xpr.coeffRef(0,0) = s;
  }
  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
+  inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
-      : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) {
+    : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
-    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols() &&
+  {
-                 "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.block(0, 0, other.rows(), other.cols()) = other;
    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(),
                                                                                           other.cols()) = other;
  }
  /* Copy/Move constructor which transfers ownership. This is crucial in
   * absence of return value optimization to avoid assertions during destruction. */
  EIGEN_DEVICE_FUNC inline CommaInitializer(const CommaInitializer& o)
      : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
    // Mark original object as finished. In absence of R-value references we need to const_cast:
    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
  }
  /* inserts a scalar value in the target matrix */
-  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const Scalar& s) {
+  CommaInitializer& operator,(const Scalar& s)
-    if (m_col == m_xpr.cols()) {
+  {
    if (m_col==m_xpr.cols())
    {
      m_row+=m_currentBlockRows;
      m_col = 0;
      m_currentBlockRows = 1;
-      eigen_assert(m_row < m_xpr.rows() && "Too many rows passed to comma initializer (operator<<)");
+      eigen_assert(m_row<m_xpr.rows()
        && "Too many rows passed to comma initializer (operator<<)");
    }
-    eigen_assert(m_col < m_xpr.cols() && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_col<m_xpr.cols()
      && "Too many coefficients passed to comma initializer (operator<<)");
    eigen_assert(m_currentBlockRows==1);
    m_xpr.coeffRef(m_row, m_col++) = s;
    return *this;
@@ -72,29 +76,34 @@ struct CommaInitializer {
  /* inserts a matrix expression in the target matrix */
  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const DenseBase<OtherDerived>& other) {
+  CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
-    if (m_col == m_xpr.cols() && (other.cols() != 0 || other.rows() != m_currentBlockRows)) {
+  {
    if (m_col==m_xpr.cols())
    {
      m_row+=m_currentBlockRows;
      m_col = 0;
      m_currentBlockRows = other.rows();
-      eigen_assert(m_row + m_currentBlockRows <= m_xpr.rows() &&
+      eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
-                   "Too many rows passed to comma initializer (operator<<)");
+        && "Too many rows passed to comma initializer (operator<<)");
    }
-    eigen_assert((m_col + other.cols() <= m_xpr.cols()) &&
+    eigen_assert(m_col<m_xpr.cols()
-                 "Too many coefficients passed to comma initializer (operator<<)");
+      && "Too many coefficients passed to comma initializer (operator<<)");
    eigen_assert(m_currentBlockRows==other.rows());
-    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(m_row, m_col, other.rows(),
+    if (OtherDerived::SizeAtCompileTime != Dynamic)
-                                                                                           other.cols()) = other;
+      m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
                              OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
                    (m_row, m_col) = other;
    else
      m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
    m_col += other.cols();
    return *this;
  }
-  EIGEN_DEVICE_FUNC inline ~CommaInitializer()
+  inline ~CommaInitializer()
 #if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
      noexcept(false)  // Eigen::eigen_assert_exception
 #endif
  {
-    finished();
+    eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
         && m_col == m_xpr.cols()
         && "Too few coefficients passed to comma initializer (operator<<)");
  }
  /** \returns the built matrix once all its coefficients have been set.
@@ -104,11 +113,7 @@ struct CommaInitializer {
    * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
    * \endcode
    */
-  EIGEN_DEVICE_FUNC inline XprType& finished() {
+  inline XprType& finished() { return m_xpr; }
    eigen_assert(((m_row + m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0) && m_col == m_xpr.cols() &&
                 "Too few coefficients passed to comma initializer (operator<<)");
    return m_xpr;
  }
  XprType& m_xpr;   // target expression
  Index m_row;              // current row id
@@ -125,24 +130,21 @@ struct CommaInitializer {
  * Example: \include MatrixBase_set.cpp
  * Output: \verbinclude MatrixBase_set.out
  *
 * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary
 * order.
 *
  * \sa CommaInitializer::finished(), class CommaInitializer
  */
 template<typename Derived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(const Scalar& s) {
+inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
 {
  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
 }
 /** \sa operator<<(const Scalar&) */
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(
+inline CommaInitializer<Derived>
-    const DenseBase<OtherDerived>& other) {
+DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
 {
  return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
 }
 }  // end namespace Eigen
 #endif // EIGEN_COMMAINITIALIZER_H
--- a/Eigen/src/Core/ConditionEstimator.h
+++ b/Eigen/src/Core/ConditionEstimator.h
@@ -1,164 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@gmail.com)
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_CONDITIONESTIMATOR_H
 #define EIGEN_CONDITIONESTIMATOR_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename Vector, typename RealVector, bool IsComplex>
 struct rcond_compute_sign {
  static inline Vector run(const Vector& v) {
    const RealVector v_abs = v.cwiseAbs();
    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
        .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
  }
 };
 // Partial specialization to avoid elementwise division for real vectors.
 template <typename Vector>
 struct rcond_compute_sign<Vector, Vector, false> {
  static inline Vector run(const Vector& v) {
    return (v.array() < static_cast<typename Vector::RealScalar>(0))
        .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
  }
 };
 /**
 * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
 * \a matrix that implements .solve() and .adjoint().solve() methods.
 *
 * This function implements Algorithms 4.1 and 5.1 from
 *   Higham, "Experience with a Matrix Norm Estimator",
 *   SIAM J. Sci. Stat. Comput., 11(4):804-809, 1990.
 * with Higham's alternating-sign safety-net estimate from
 *   Higham and Tisseur, "A Block Algorithm for Matrix 1-Norm Estimation,
 *   with an Application to 1-Norm Pseudospectra", SIAM J. Matrix Anal. Appl.,
 *   21(4):1185-1201, 2000.
 *
 * The Hager/Higham gradient ascent uses at most 5 iterations of 2 solves
 * each, giving a total cost of O(n^2).
 *
 * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, LLT.
 *
 * \sa FullPivLU, PartialPivLU, LDLT, LLT.
 */
 template <typename Decomposition>
 typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec) {
  typedef typename Decomposition::MatrixType MatrixType;
  typedef typename Decomposition::Scalar Scalar;
  typedef typename Decomposition::RealScalar RealScalar;
  typedef typename internal::plain_col_type<MatrixType>::type Vector;
  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
  eigen_assert(dec.rows() == dec.cols());
  const Index n = dec.rows();
  if (n == 0) return RealScalar(0);
    // Disable Index to float conversion warning
 #ifdef __INTEL_COMPILER
 #pragma warning push
 #pragma warning(disable : 2259)
 #endif
  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
 #ifdef __INTEL_COMPILER
 #pragma warning pop
 #endif
  // lower_bound is a lower bound on
  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
  // and is the objective maximized by the supergradient ascent algorithm below.
  RealScalar lower_bound = v.template lpNorm<1>();
  if (n == 1) return lower_bound;
  // Gradient ascent: the optimum is achieved at a unit vector e_j. Each
  // iteration follows the supergradient to find which unit vector to probe next.
  RealScalar old_lower_bound = lower_bound;
  Vector sign_vector(n);
  Vector old_sign_vector;
  Index v_max_abs_index = -1;
  Index old_v_max_abs_index = v_max_abs_index;
  for (int k = 0; k < 4; ++k) {
    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
      // Break if the sign vector stagnated.
      break;
    }
    // Supergradient: z = A^{-T} * sign(v), pick argmax |z_i|.
    v = dec.adjoint().solve(sign_vector);
    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
    if (v_max_abs_index == old_v_max_abs_index) {
      // Optimality: supergradient points to the same unit vector.
      break;
    }
    // Probe the best unit vector: v = A^{-1} * e_j.
    v = dec.solve(Vector::Unit(n, v_max_abs_index));
    lower_bound = v.template lpNorm<1>();
    if (lower_bound <= old_lower_bound) {
      // No improvement from the gradient step.
      break;
    }
    if (!is_complex) {
      old_sign_vector = sign_vector;
    }
    old_v_max_abs_index = v_max_abs_index;
    old_lower_bound = lower_bound;
  }
  // Higham's alternating-sign estimate: an independent safety-net that catches
  // cases where the gradient ascent converges to a local maximum due to exact
  // cancellation patterns (especially with permutations and backsubstitutions).
  //   v_i = (-1)^i * (1 + i/(n-1)), then estimate = 2*||A^{-1}*v||_1 / (3*n).
  Scalar alternating_sign(RealScalar(1));
  for (Index i = 0; i < n; ++i) {
    // The static_cast is needed when Scalar is complex and RealScalar uses expression templates.
    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
    alternating_sign = -alternating_sign;
  }
  v = dec.solve(v);
  const RealScalar alt_est = (RealScalar(2) * v.template lpNorm<1>()) / (RealScalar(3) * RealScalar(n));
  return numext::maxi(lower_bound, alt_est);
 }
 /** \brief Reciprocal condition number estimator.
 *
 * Computing a decomposition of a dense matrix takes O(n^3) operations, while
 * this method estimates the condition number quickly and reliably in O(n^2)
 * operations.
 *
 * \returns an estimate of the reciprocal condition number
 * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
 * its decomposition. Supports the following decompositions: FullPivLU,
 * PartialPivLU, LDLT, and LLT.
 *
 * \sa FullPivLU, PartialPivLU, LDLT, LLT.
 */
 template <typename Decomposition>
 typename Decomposition::RealScalar rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm,
                                                         const Decomposition& dec) {
  typedef typename Decomposition::RealScalar RealScalar;
  eigen_assert(dec.rows() == dec.cols());
  if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
  if (numext::is_exactly_zero(matrix_norm)) return RealScalar(0);
  if (dec.rows() == 1) return RealScalar(1);
  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
  return (numext::is_exactly_zero(inverse_matrix_norm) ? RealScalar(0)
                                                       : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
 }
 }  // namespace internal
 }  // namespace Eigen
 #endif
--- a/Eigen/src/Core/CoreEvaluators.h
+++ b/Eigen/src/Core/CoreEvaluators.h
--- a/Eigen/src/Core/CoreIterators.h
+++ b/Eigen/src/Core/CoreIterators.h
@@ -1,141 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_COREITERATORS_H
 #define EIGEN_COREITERATORS_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 /* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
 */
 namespace internal {
 template <typename XprType, typename EvaluatorKind>
 class inner_iterator_selector;
 }
 /** \class InnerIterator
 * \brief An InnerIterator allows to loop over the element of any matrix expression.
 *
 * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is
 * constructed.
 *
 * TODO: add a usage example
 */
 template <typename XprType>
 class InnerIterator {
 protected:
  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
  typedef internal::evaluator<XprType> EvaluatorType;
  typedef typename internal::traits<XprType>::Scalar Scalar;
 public:
  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
  InnerIterator(const XprType &xpr, const Index &outerId) : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize()) {}
  /// \returns the value of the current coefficient.
  EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
  /** Increment the iterator \c *this to the next non-zero coefficient.
   * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
   */
  EIGEN_STRONG_INLINE InnerIterator &operator++() {
    m_iter.operator++();
    return *this;
  }
  EIGEN_STRONG_INLINE InnerIterator &operator+=(Index i) {
    m_iter.operator+=(i);
    return *this;
  }
  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) const {
    InnerIterator result(*this);
    result += i;
    return result;
  }
  /// \returns the column or row index of the current coefficient.
  EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
  /// \returns the row index of the current coefficient.
  EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
  /// \returns the column index of the current coefficient.
  EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
  /// \returns \c true if the iterator \c *this still references a valid coefficient.
  EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
 protected:
  EvaluatorType m_eval;
  IteratorType m_iter;
 private:
  // If you get here, then you're not using the right InnerIterator type, e.g.:
  //   SparseMatrix<double,RowMajor> A;
  //   SparseMatrix<double>::InnerIterator it(A,0);
  template <typename T>
  InnerIterator(const EigenBase<T> &, Index outer);
 };
 namespace internal {
 // Generic inner iterator implementation for dense objects
 template <typename XprType>
 class inner_iterator_selector<XprType, IndexBased> {
 protected:
  typedef evaluator<XprType> EvaluatorType;
  typedef typename traits<XprType>::Scalar Scalar;
  enum { IsRowMajor = (XprType::Flags & RowMajorBit) == RowMajorBit };
 public:
  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
      : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize) {}
  EIGEN_STRONG_INLINE Scalar value() const {
    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner) : m_eval.coeff(m_inner, m_outer);
  }
  EIGEN_STRONG_INLINE inner_iterator_selector &operator++() {
    m_inner++;
    return *this;
  }
  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
  inline Index row() const { return IsRowMajor ? m_outer : index(); }
  inline Index col() const { return IsRowMajor ? index() : m_outer; }
  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner >= 0; }
 protected:
  const EvaluatorType &m_eval;
  Index m_inner;
  const Index m_outer;
  const Index m_end;
 };
 // For iterator-based evaluator, inner-iterator is already implemented as
 // evaluator<>::InnerIterator
 template <typename XprType>
 class inner_iterator_selector<XprType, IteratorBased> : public evaluator<XprType>::InnerIterator {
 protected:
  typedef typename evaluator<XprType>::InnerIterator Base;
  typedef evaluator<XprType> EvaluatorType;
 public:
  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId,
                                              const Index & /*innerSize*/)
      : Base(eval, outerId) {}
 };
 }  // end namespace internal
 }  // end namespace Eigen
 #endif  // EIGEN_COREITERATORS_H
--- a/Eigen/src/Core/CwiseBinaryOp.h
+++ b/Eigen/src/Core/CwiseBinaryOp.h
@@ -1,27 +1,58 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_CWISE_BINARY_OP_H
 #define EIGEN_CWISE_BINARY_OP_H
-// IWYU pragma: private
+/** \class CwiseBinaryOp
-#include "./InternalHeaderCheck.h"
+  * \ingroup Core_Module
-
+  *
-namespace Eigen {
+  * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
  *
  * \param BinaryOp template functor implementing the operator
  * \param Lhs the type of the left-hand side
  * \param Rhs the type of the right-hand side
  *
  * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
  * It is the return type of binary operators, by which we mean only those binary operators where
  * both the left-hand side and the right-hand side are Eigen expressions.
  * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
  *
  * Most of the time, this is the only way that it is used, so you typically don't have to name
  * CwiseBinaryOp types explicitly.
  *
  * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
  */
 namespace internal {
 template<typename BinaryOp, typename Lhs, typename Rhs>
-struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
+struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
 {
  // we must not inherit from traits<Lhs> since it has
  // the potential to cause problems with MSVC
-  typedef remove_all_t<Lhs> Ancestor;
+  typedef typename remove_all<Lhs>::type Ancestor;
  typedef typename traits<Ancestor>::XprKind XprKind;
  enum {
    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
@@ -32,111 +63,150 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
  // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
  // we still want to handle the case when the result type is different.
-  typedef typename result_of<BinaryOp(const typename Lhs::Scalar&, const typename Rhs::Scalar&)>::type Scalar;
+  typedef typename result_of<
-  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
+                     BinaryOp(
-                                              BinaryOp>::ret StorageKind;
+                       typename Lhs::Scalar,
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex, typename traits<Rhs>::StorageIndex>::type
+                       typename Rhs::Scalar
-      StorageIndex;
+                     )
                   >::type Scalar;
  typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
                                           typename traits<Rhs>::StorageKind>::ret StorageKind;
  typedef typename promote_index_type<typename traits<Lhs>::Index,
                                         typename traits<Rhs>::Index>::type Index;
  typedef typename Lhs::Nested LhsNested;
  typedef typename Rhs::Nested RhsNested;
-  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
  enum {
-    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
+    LhsCoeffReadCost = _LhsNested::CoeffReadCost,
-                                        LhsNested_::Flags & RowMajorBit, RhsNested_::Flags & RowMajorBit>::value
+    RhsCoeffReadCost = _RhsNested::CoeffReadCost,
    LhsFlags = _LhsNested::Flags,
    RhsFlags = _RhsNested::Flags,
    SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
    StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit),
    Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
        HereditaryBits
      | (int(LhsFlags) & int(RhsFlags) &
           ( AlignedBit
           | (StorageOrdersAgree ? LinearAccessBit : 0)
           | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
           )
        )
     ),
    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
    CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost
  };
 };
 } // end namespace internal
 // we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
 // that would take two operands of different types. If there were such an example, then this check should be
 // moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
 // currently they take only one typename Scalar template parameter.
 // It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
 // So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
 // add together a float matrix and a double matrix.
 #define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
  EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex<BINOP>::ret \
                        ? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
                        : int(internal::is_same<LHS, RHS>::value)), \
    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
 template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
 class CwiseBinaryOpImpl;
-/** \class CwiseBinaryOp
+template<typename BinaryOp, typename Lhs, typename Rhs>
- * \ingroup Core_Module
+class CwiseBinaryOp : internal::no_assignment_operator,
- *
+  public CwiseBinaryOpImpl<
- * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
+          BinaryOp, Lhs, Rhs,
- *
+          typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
- * \tparam BinaryOp template functor implementing the operator
+                                           typename internal::traits<Rhs>::StorageKind>::ret>
- * \tparam LhsType the type of the left-hand side
+{
 * \tparam RhsType the type of the right-hand side
 *
 * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
 * It is the return type of binary operators, by which we mean only those binary operators where
 * both the left-hand side and the right-hand side are Eigen expressions.
 * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
 *
 * Most of the time, this is the only way that it is used, so you typically don't have to name
 * CwiseBinaryOp types explicitly.
 *
 * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class
 * CwiseNullaryOp
 */
 template <typename BinaryOp, typename LhsType, typename RhsType>
 class CwiseBinaryOp : public CwiseBinaryOpImpl<BinaryOp, LhsType, RhsType,
                                               typename internal::cwise_promote_storage_type<
                                                   typename internal::traits<LhsType>::StorageKind,
                                                   typename internal::traits<RhsType>::StorageKind, BinaryOp>::ret>,
                      internal::no_assignment_operator {
  public:
  typedef internal::remove_all_t<BinaryOp> Functor;
  typedef internal::remove_all_t<LhsType> Lhs;
  typedef internal::remove_all_t<RhsType> Rhs;
    typedef typename CwiseBinaryOpImpl<
-      BinaryOp, LhsType, RhsType,
+        BinaryOp, Lhs, Rhs,
-      typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
+        typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
-                                                    typename internal::traits<Rhs>::StorageKind, BinaryOp>::ret>::Base
+                                         typename internal::traits<Rhs>::StorageKind>::ret>::Base Base;
      Base;
    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp, typename Lhs::Scalar, typename Rhs::Scalar)
+    typedef typename internal::nested<Lhs>::type LhsNested;
    typedef typename internal::nested<Rhs>::type RhsNested;
    typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
    typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
    EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
      : m_lhs(lhs), m_rhs(rhs), m_functor(func)
    {
      EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
      // require the sizes to match
      EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
-
+      eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
  typedef typename internal::ref_selector<LhsType>::type LhsNested;
  typedef typename internal::ref_selector<RhsType>::type RhsNested;
  typedef std::remove_reference_t<LhsNested> LhsNested_;
  typedef std::remove_reference_t<RhsNested> RhsNested_;
 #if EIGEN_COMP_MSVC
  // Required for Visual Studio, which may fail to inline the copy constructor otherwise.
  EIGEN_STRONG_INLINE CwiseBinaryOp(const CwiseBinaryOp<BinaryOp, LhsType, RhsType>&) = default;
 #endif
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs,
                                                                const BinaryOp& func = BinaryOp())
      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func) {
    eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
    }
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept {
+    EIGEN_STRONG_INLINE Index rows() const {
      // return the fixed size type if available to enable compile time optimizations
-    return internal::traits<internal::remove_all_t<LhsNested>>::RowsAtCompileTime == Dynamic ? m_rhs.rows()
+      if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
-                                                                                             : m_lhs.rows();
+        return m_rhs.rows();
      else
        return m_lhs.rows();
    }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept {
+    EIGEN_STRONG_INLINE Index cols() const {
      // return the fixed size type if available to enable compile time optimizations
-    return internal::traits<internal::remove_all_t<LhsNested>>::ColsAtCompileTime == Dynamic ? m_rhs.cols()
+      if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
-                                                                                             : m_lhs.cols();
+        return m_rhs.cols();
      else
        return m_lhs.cols();
    }
    /** \returns the left hand side nested expression */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const LhsNested_& lhs() const { return m_lhs; }
+    const _LhsNested& lhs() const { return m_lhs; }
    /** \returns the right hand side nested expression */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const RhsNested_& rhs() const { return m_rhs; }
+    const _RhsNested& rhs() const { return m_rhs; }
    /** \returns the functor representing the binary operation */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const BinaryOp& functor() const { return m_functor; }
+    const BinaryOp& functor() const { return m_functor; }
  protected:
-  LhsNested m_lhs;
+    const LhsNested m_lhs;
-  RhsNested m_rhs;
+    const RhsNested m_rhs;
    const BinaryOp m_functor;
 };
-// Generic API dispatcher
+template<typename BinaryOp, typename Lhs, typename Rhs>
-template <typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
-class CwiseBinaryOpImpl : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type {
+  : public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
 {
    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
  public:
-  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type Base;
+
    typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
    EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
    EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
    {
      return derived().functor()(derived().lhs().coeff(row, col),
                                 derived().rhs().coeff(row, col));
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
    {
      return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
                                          derived().rhs().template packet<LoadMode>(row, col));
    }
    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
    {
      return derived().functor()(derived().lhs().coeff(index),
                                 derived().rhs().coeff(index));
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
    {
      return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
                                          derived().rhs().template packet<LoadMode>(index));
    }
 };
 /** replaces \c *this by \c *this - \a other.
@@ -145,8 +215,11 @@ class CwiseBinaryOpImpl : public internal::generic_xpr_base<CwiseBinaryOp<Binary
  */
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Derived& MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived &
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
 {
  SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
  tmp = other.derived();
  return derived();
 }
@@ -156,11 +229,12 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Derived& MatrixBase<Derived>::operator-=(c
  */
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Derived& MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived &
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
 {
  SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
  tmp = other.derived();
  return derived();
 }
 }  // end namespace Eigen
 #endif // EIGEN_CWISE_BINARY_OP_H
--- a/Eigen/src/Core/CwiseNullaryOp.h
+++ b/Eigen/src/Core/CwiseNullaryOp.h
--- a/Eigen/src/Core/CwiseTernaryOp.h
+++ b/Eigen/src/Core/CwiseTernaryOp.h
@@ -1,171 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_CWISE_TERNARY_OP_H
 #define EIGEN_CWISE_TERNARY_OP_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
 struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> {
  // we must not inherit from traits<Arg1> since it has
  // the potential to cause problems with MSVC
  typedef remove_all_t<Arg1> Ancestor;
  typedef typename traits<Ancestor>::XprKind XprKind;
  enum {
    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
  };
  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
  // (see CwiseTernaryOp constructor),
  // we still want to handle the case when the result type is different.
  typedef typename result_of<TernaryOp(const typename Arg1::Scalar&, const typename Arg2::Scalar&,
                                       const typename Arg3::Scalar&)>::type Scalar;
  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
  typedef typename Arg1::Nested Arg1Nested;
  typedef typename Arg2::Nested Arg2Nested;
  typedef typename Arg3::Nested Arg3Nested;
  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
  enum { Flags = Arg1Nested_::Flags & RowMajorBit };
 };
 }  // end namespace internal
 template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
 class CwiseTernaryOpImpl;
 /** \class CwiseTernaryOp
 * \ingroup Core_Module
 *
 * \brief Generic expression where a coefficient-wise ternary operator is
 * applied to two expressions
 *
 * \tparam TernaryOp template functor implementing the operator
 * \tparam Arg1Type the type of the first argument
 * \tparam Arg2Type the type of the second argument
 * \tparam Arg3Type the type of the third argument
 *
 * This class represents an expression where a coefficient-wise ternary
 * operator is applied to three expressions.
 * It is the return type of ternary operators, by which we mean only those
 * ternary operators where
 * all three arguments are Eigen expressions.
 * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
 * CwiseTernaryOp.
 *
 * Most of the time, this is the only way that it is used, so you typically
 * don't have to name
 * CwiseTernaryOp types explicitly.
 *
 * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
 * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
 * class CwiseUnaryOp, class CwiseNullaryOp
 */
 template <typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type>
 class CwiseTernaryOp : public CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
                                                 typename internal::traits<Arg1Type>::StorageKind>,
                       internal::no_assignment_operator {
 public:
  typedef internal::remove_all_t<Arg1Type> Arg1;
  typedef internal::remove_all_t<Arg2Type> Arg2;
  typedef internal::remove_all_t<Arg3Type> Arg3;
  // require the sizes to match
  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
  // The index types should match
  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
                                         typename internal::traits<Arg2Type>::StorageKind>::value),
                      STORAGE_KIND_MUST_MATCH)
  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
                                         typename internal::traits<Arg3Type>::StorageKind>::value),
                      STORAGE_KIND_MUST_MATCH)
  typedef typename CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
                                      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2, const Arg3& a3,
                                                       const TernaryOp& func = TernaryOp())
      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() && a1.rows() == a3.rows() && a1.cols() == a3.cols());
  }
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index rows() const {
    // return the fixed size type if available to enable compile time
    // optimizations
    if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
        internal::traits<internal::remove_all_t<Arg2Nested>>::RowsAtCompileTime == Dynamic)
      return m_arg3.rows();
    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
             internal::traits<internal::remove_all_t<Arg3Nested>>::RowsAtCompileTime == Dynamic)
      return m_arg2.rows();
    else
      return m_arg1.rows();
  }
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index cols() const {
    // return the fixed size type if available to enable compile time
    // optimizations
    if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
        internal::traits<internal::remove_all_t<Arg2Nested>>::ColsAtCompileTime == Dynamic)
      return m_arg3.cols();
    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
             internal::traits<internal::remove_all_t<Arg3Nested>>::ColsAtCompileTime == Dynamic)
      return m_arg2.cols();
    else
      return m_arg1.cols();
  }
  /** \returns the first argument nested expression */
  EIGEN_DEVICE_FUNC constexpr const Arg1Nested_& arg1() const { return m_arg1; }
  /** \returns the first argument nested expression */
  EIGEN_DEVICE_FUNC constexpr const Arg2Nested_& arg2() const { return m_arg2; }
  /** \returns the third argument nested expression */
  EIGEN_DEVICE_FUNC constexpr const Arg3Nested_& arg3() const { return m_arg3; }
  /** \returns the functor representing the ternary operation */
  EIGEN_DEVICE_FUNC constexpr const TernaryOp& functor() const { return m_functor; }
 protected:
  Arg1Nested m_arg1;
  Arg2Nested m_arg2;
  Arg3Nested m_arg3;
  const TernaryOp m_functor;
 };
 // Generic API dispatcher
 template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
 class CwiseTernaryOpImpl : public internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type {
 public:
  typedef typename internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type Base;
 };
 }  // end namespace Eigen
 #endif  // EIGEN_CWISE_TERNARY_OP_H
--- a/Eigen/src/Core/CwiseUnaryOp.h
+++ b/Eigen/src/Core/CwiseUnaryOp.h
@@ -1,41 +1,38 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_CWISE_UNARY_OP_H
 #define EIGEN_CWISE_UNARY_OP_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename UnaryOp, typename XprType>
 struct traits<CwiseUnaryOp<UnaryOp, XprType> > : traits<XprType> {
  typedef typename result_of<UnaryOp(const typename XprType::Scalar&)>::type Scalar;
  typedef typename XprType::Nested XprTypeNested;
  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
  enum { Flags = XprTypeNested_::Flags & RowMajorBit };
 };
 }  // namespace internal
 template <typename UnaryOp, typename XprType, typename StorageKind>
 class CwiseUnaryOpImpl;
 /** \class CwiseUnaryOp
  * \ingroup Core_Module
  *
  * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
  *
- * \tparam UnaryOp template functor implementing the operator
+  * \param UnaryOp template functor implementing the operator
- * \tparam XprType the type of the expression to which we are applying the unary operator
+  * \param XprType the type of the expression to which we are applying the unary operator
  *
  * This class represents an expression where a unary operator is applied to an expression.
  * It is the return type of all operations taking exactly 1 input expression, regardless of the
@@ -48,48 +45,93 @@ class CwiseUnaryOpImpl;
  *
  * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
  */
 namespace internal {
 template<typename UnaryOp, typename XprType>
-class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>,
+struct traits<CwiseUnaryOp<UnaryOp, XprType> >
-                     internal::no_assignment_operator {
+ : traits<XprType>
 {
  typedef typename result_of<
                     UnaryOp(typename XprType::Scalar)
                   >::type Scalar;
  typedef typename XprType::Nested XprTypeNested;
  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
  enum {
    Flags = _XprTypeNested::Flags & (
      HereditaryBits | LinearAccessBit | AlignedBit
      | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
    CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
  };
 };
 }
 template<typename UnaryOp, typename XprType, typename StorageKind>
 class CwiseUnaryOpImpl;
 template<typename UnaryOp, typename XprType>
 class CwiseUnaryOp : internal::no_assignment_operator,
  public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>
 {
  public:
    typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
  typedef typename internal::ref_selector<XprType>::type XprTypeNested;
  typedef internal::remove_all_t<XprType> NestedExpression;
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit CwiseUnaryOp(const XprType& xpr,
+    inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
                                                                        const UnaryOp& func = UnaryOp())
      : m_xpr(xpr), m_functor(func) {}
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_xpr.rows(); }
+    EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_xpr.cols(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
    /** \returns the functor representing the unary operation */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const UnaryOp& functor() const { return m_functor; }
+    const UnaryOp& functor() const { return m_functor; }
    /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression()
+    const typename internal::remove_all<typename XprType::Nested>::type&
-      const {
+    nestedExpression() const { return m_xpr; }
    return m_xpr;
  }
    /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE internal::remove_all_t<XprTypeNested>& nestedExpression() {
+    typename internal::remove_all<typename XprType::Nested>::type&
-    return m_xpr;
+    nestedExpression() { return m_xpr.const_cast_derived(); }
  }
  protected:
-  XprTypeNested m_xpr;
+    const typename XprType::Nested m_xpr;
    const UnaryOp m_functor;
 };
-// Generic API dispatcher
+// This is the generic implementation for dense storage.
-template <typename UnaryOp, typename XprType, typename StorageKind>
+// It can be used for any expression types implementing the dense concept.
-class CwiseUnaryOpImpl : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type {
+template<typename UnaryOp, typename XprType>
 class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
  : public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
 {
  public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
+
    typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
    typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
    EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
    {
      return derived().functor()(derived().nestedExpression().coeff(row, col));
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
    {
      return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
    }
    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
    {
      return derived().functor()(derived().nestedExpression().coeff(index));
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
    {
      return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
    }
 };
 }  // end namespace Eigen
 #endif // EIGEN_CWISE_UNARY_OP_H
--- a/Eigen/src/Core/CwiseUnaryView.h
+++ b/Eigen/src/Core/CwiseUnaryView.h
@@ -3,167 +3,146 @@
 //
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_CWISE_UNARY_VIEW_H
 #define EIGEN_CWISE_UNARY_VIEW_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
 template <typename ViewOp, typename MatrixType, typename StrideType>
 struct traits<CwiseUnaryView<ViewOp, MatrixType, StrideType> > : traits<MatrixType> {
  typedef typename result_of<ViewOp(typename traits<MatrixType>::Scalar&)>::type1 ScalarRef;
  static_assert(std::is_reference<ScalarRef>::value, "Views must return a reference type.");
  typedef remove_all_t<ScalarRef> Scalar;
  typedef typename MatrixType::Nested MatrixTypeNested;
  typedef remove_all_t<MatrixTypeNested> MatrixTypeNested_;
  enum {
    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
    Flags =
        traits<MatrixTypeNested_>::Flags &
        (RowMajorBit | FlagsLvalueBit | DirectAccessBit),  // FIXME DirectAccessBit should not be handled by expressions
    MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
    // need to cast the sizeof's from size_t to int explicitly, otherwise:
    // "error: no integral type can represent all of the enumerator values
    InnerStrideAtCompileTime =
        StrideType::InnerStrideAtCompileTime == 0
            ? (MatrixTypeInnerStride == Dynamic
                   ? int(Dynamic)
                   : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
            : int(StrideType::InnerStrideAtCompileTime),
    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
                                   ? (outer_stride_at_compile_time<MatrixType>::ret == Dynamic
                                          ? int(Dynamic)
                                          : outer_stride_at_compile_time<MatrixType>::ret *
                                                int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
                                   : int(StrideType::OuterStrideAtCompileTime)
  };
 };
 // Generic API dispatcher
 template <typename ViewOp, typename XprType, typename StrideType, typename StorageKind,
          bool Mutable = !std::is_const<XprType>::value>
 class CwiseUnaryViewImpl : public generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type {
 public:
  typedef typename generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type Base;
 };
 template <typename ViewOp, typename MatrixType, typename StrideType>
 class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false>
    : public dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type {
 public:
  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
  typedef typename dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type Base;
  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
  EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeffRef(0)); }
  EIGEN_DEVICE_FUNC constexpr Index innerStride() const {
    return StrideType::InnerStrideAtCompileTime != 0 ? int(StrideType::InnerStrideAtCompileTime)
                                                     : derived().nestedExpression().innerStride() *
                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
  }
  EIGEN_DEVICE_FUNC constexpr Index outerStride() const {
    return StrideType::OuterStrideAtCompileTime != 0 ? int(StrideType::OuterStrideAtCompileTime)
                                                     : derived().nestedExpression().outerStride() *
                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
  }
 protected:
  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
  // Allow const access to coeffRef for the case of direct access being enabled.
  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
    return internal::evaluator<Derived>(derived()).coeffRef(index);
  }
  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index col) const {
    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
  }
 };
 template <typename ViewOp, typename MatrixType, typename StrideType>
 class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, true>
    : public CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> {
 public:
  typedef CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> Base;
  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
  using Base::data;
  EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
    return internal::evaluator<Derived>(derived()).coeffRef(index);
  }
 protected:
  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
 };
 }  // namespace internal
 /** \class CwiseUnaryView
  * \ingroup Core_Module
  *
  * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
  *
- * \tparam ViewOp template functor implementing the view
+  * \param ViewOp template functor implementing the view
- * \tparam MatrixType the type of the matrix we are applying the unary operator
+  * \param MatrixType the type of the matrix we are applying the unary operator
  *
  * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
  * It is the return type of real() and imag(), and most of the time this is the only way it is used.
  *
  * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
  */
 template <typename ViewOp, typename MatrixType, typename StrideType>
 class CwiseUnaryView : public internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
                                                           typename internal::traits<MatrixType>::StorageKind> {
 public:
  typedef typename internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
                                                typename internal::traits<MatrixType>::StorageKind>::Base Base;
  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
  typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
  typedef internal::remove_all_t<MatrixType> NestedExpression;
-  explicit EIGEN_DEVICE_FUNC constexpr inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
+namespace internal {
 template<typename ViewOp, typename MatrixType>
 struct traits<CwiseUnaryView<ViewOp, MatrixType> >
 : traits<MatrixType>
 {
  typedef typename result_of<
                     ViewOp(typename traits<MatrixType>::Scalar)
                   >::type Scalar;
  typedef typename MatrixType::Nested MatrixTypeNested;
  typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
  enum {
    Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
    CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
    MatrixTypeInnerStride =  inner_stride_at_compile_time<MatrixType>::ret,
    // need to cast the sizeof's from size_t to int explicitly, otherwise:
    // "error: no integral type can represent all of the enumerator values
    InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
                             ? int(Dynamic)
                             : int(MatrixTypeInnerStride)
                               * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
  };
 };
 }
 template<typename ViewOp, typename MatrixType, typename StorageKind>
 class CwiseUnaryViewImpl;
 template<typename ViewOp, typename MatrixType>
 class CwiseUnaryView : internal::no_assignment_operator,
  public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
 {
  public:
    typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
    inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp())
      : m_matrix(mat), m_functor(func) {}
    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
    /** \returns the functor representing unary operation */
-  EIGEN_DEVICE_FUNC constexpr const ViewOp& functor() const { return m_functor; }
+    const ViewOp& functor() const { return m_functor; }
    /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<MatrixTypeNested>& nestedExpression() const {
+    const typename internal::remove_all<typename MatrixType::Nested>::type&
-    return m_matrix;
+    nestedExpression() const { return m_matrix; }
  }
    /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC constexpr std::remove_reference_t<MatrixTypeNested>& nestedExpression() { return m_matrix; }
+    typename internal::remove_all<typename MatrixType::Nested>::type&
    nestedExpression() { return m_matrix.const_cast_derived(); }
  protected:
-  MatrixTypeNested m_matrix;
+    // FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC
    const typename internal::nested<MatrixType>::type m_matrix;
    ViewOp m_functor;
 };
-}  // namespace Eigen
+template<typename ViewOp, typename MatrixType>
 class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
  : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
 {
  public:
    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
    typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
    inline Index innerStride() const
    {
      return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
    }
    inline Index outerStride() const
    {
      return derived().nestedExpression().outerStride();
    }
    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
    {
      return derived().functor()(derived().nestedExpression().coeff(row, col));
    }
    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
      return derived().functor()(derived().nestedExpression().coeff(index));
    }
    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
    {
      return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
    }
    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
    {
      return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
    }
 };
 #endif // EIGEN_CWISE_UNARY_VIEW_H
--- a/Eigen/src/Core/DenseBase.h
+++ b/Eigen/src/Core/DenseBase.h
@@ -4,21 +4,28 @@
 // Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DENSEBASE_H
 #define EIGEN_DENSEBASE_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 // The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
 EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
 /** \class DenseBase
  * \ingroup Core_Module
  *
@@ -30,65 +37,67 @@ EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SI
  * \tparam Derived is the derived type, e.g., a matrix type or an expression.
  *
  * This class can be extended with the help of the plugin mechanism described on the page
- * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
  *
- * \sa \blank \ref TopicClassHierarchy
+  * \sa \ref TopicClassHierarchy
  */
-template <typename Derived>
+template<typename Derived> class DenseBase
 class DenseBase
 #ifndef EIGEN_PARSED_BY_DOXYGEN
-    : public DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value>
+  : public internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
                                     typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>
 #else
-    : public DenseCoeffsBase<Derived, DirectWriteAccessors>
+  : public DenseCoeffsBase<Derived>
 #endif // not EIGEN_PARSED_BY_DOXYGEN
 {
  public:
-  /** Inner iterator type to iterate over the coefficients of a row or column.
+    using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
-   * \sa class InnerIterator
+                typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
-   */
+
-  typedef Eigen::InnerIterator<Derived> InnerIterator;
+    class InnerIterator;
    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-  /**
+    /** \brief The type of indices 
-   * \brief The type used to store indices
+      * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
-   * \details This typedef is relevant for types that store multiple indices such as
+      * \sa \ref TopicPreprocessorDirectives.
   *          PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
   * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
      */
-  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+    typedef typename internal::traits<Derived>::Index Index; 
  /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
    typedef typename internal::traits<Derived>::Scalar Scalar;
-
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
  /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
   *
   * It is an alias for the Scalar type */
  typedef Scalar value_type;
    typedef typename NumTraits<Scalar>::Real RealScalar;
  typedef DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value> Base;
    typedef DenseCoeffsBase<Derived> Base;
    using Base::derived;
    using Base::const_cast_derived;
    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::rowIndexByOuterInner;
    using Base::colIndexByOuterInner;
    using Base::coeff;
    using Base::coeffByOuterInner;
-  using Base::colIndexByOuterInner;
+    using Base::packet;
-  using Base::cols;
+    using Base::packetByOuterInner;
-  using Base::const_cast_derived;
+    using Base::writePacket;
-  using Base::derived;
+    using Base::writePacketByOuterInner;
-  using Base::rowIndexByOuterInner;
+    using Base::coeffRef;
-  using Base::rows;
+    using Base::coeffRefByOuterInner;
-  using Base::size;
+    using Base::copyCoeff;
    using Base::copyCoeffByOuterInner;
    using Base::copyPacket;
    using Base::copyPacketByOuterInner;
    using Base::operator();
    using Base::operator[];
  using Base::colStride;
  using Base::innerStride;
  using Base::outerStride;
  using Base::rowStride;
  using Base::stride;
  using Base::w;
    using Base::x;
    using Base::y;
    using Base::z;
    using Base::w;
    using Base::stride;
    using Base::innerStride;
    using Base::outerStride;
    using Base::rowStride;
    using Base::colStride;
    typedef typename Base::CoeffReturnType CoeffReturnType;
    enum {
@@ -105,7 +114,9 @@ class DenseBase
          * it is set to the \a Dynamic constant.
          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-    SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+
      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
        /**< This is equal to the number of coefficients, i.e. the number of
          * rows times the number of columns, or to \a Dynamic if this is not
          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
@@ -132,8 +143,8 @@ class DenseBase
          * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
          */
-    MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                          internal::traits<Derived>::MaxColsAtCompileTime),
+                                                      internal::traits<Derived>::MaxColsAtCompileTime>::ret),
        /**< This value is equal to the maximum possible number of coefficients that this expression
          * might have. If this expression might have an arbitrarily high number of coefficients,
          * this value is set to \a Dynamic.
@@ -144,20 +155,13 @@ class DenseBase
          * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
          */
-    IsVectorAtCompileTime =
+      IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
-        internal::traits<Derived>::RowsAtCompileTime == 1 || internal::traits<Derived>::ColsAtCompileTime == 1,
+                           || internal::traits<Derived>::MaxColsAtCompileTime == 1,
        /**< This is set to true if either the number of rows or the number of
          * columns is known at compile-time to be equal to 1. Indeed, in that case,
          * we are dealing with a column-vector (if there is only one column) or with
          * a row-vector (if there is only one row). */
    NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0
                    : bool(IsVectorAtCompileTime)  ? 1
                                                   : 2,
    /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
     * and 2 for matrices.
     */
      Flags = internal::traits<Derived>::Flags,
        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
          * constructed from this one. See the \ref flags "list of flags".
@@ -166,50 +170,36 @@ class DenseBase
      IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
-                             : int(IsRowMajor)          ? int(ColsAtCompileTime)
+                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-                                                        : int(RowsAtCompileTime),
+
      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
        /**< This is a rough measure of how expensive it is to read one coefficient from
          * this expression.
          */
      InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
      OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
    };
-  typedef typename internal::find_best_packet<Scalar, SizeAtCompileTime>::type PacketScalar;
+    enum { ThisConstantIsPrivateInPlainObjectBase };
-  enum { IsPlainObjectBase = 0 };
+    /** \returns the number of nonzero coefficients which is in practice the number
-
+      * of stored coefficients. */
-  /** The plain matrix type corresponding to this expression.
+    inline Index nonZeros() const { return size(); }
-   * \sa PlainObject */
+    /** \returns true if either the number of rows or the number of columns is equal to 1.
-  typedef Matrix<typename internal::traits<Derived>::Scalar, internal::traits<Derived>::RowsAtCompileTime,
+      * In other words, this function returns
-                 internal::traits<Derived>::ColsAtCompileTime,
+      * \code rows()==1 || cols()==1 \endcode
-                 AutoAlign | (internal::traits<Derived>::Flags & RowMajorBit ? RowMajor : ColMajor),
+      * \sa rows(), cols(), IsVectorAtCompileTime. */
                 internal::traits<Derived>::MaxRowsAtCompileTime, internal::traits<Derived>::MaxColsAtCompileTime>
      PlainMatrix;
  /** The plain array type corresponding to this expression.
   * \sa PlainObject */
  typedef Array<typename internal::traits<Derived>::Scalar, internal::traits<Derived>::RowsAtCompileTime,
                internal::traits<Derived>::ColsAtCompileTime,
                AutoAlign | (internal::traits<Derived>::Flags & RowMajorBit ? RowMajor : ColMajor),
                internal::traits<Derived>::MaxRowsAtCompileTime, internal::traits<Derived>::MaxColsAtCompileTime>
      PlainArray;
  /** \brief The plain matrix or array type corresponding to this expression.
   *
   * This is not necessarily exactly the return type of eval(). In the case of plain matrices,
   * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
   * that the return type of eval() is either PlainObject or const PlainObject&.
   */
  typedef std::conditional_t<internal::is_same<typename internal::traits<Derived>::XprKind, MatrixXpr>::value,
                             PlainMatrix, PlainArray>
      PlainObject;
    /** \returns the outer size.
      *
      * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
      * column-major matrix, and the number of rows for a row-major matrix. */
-  EIGEN_DEVICE_FUNC constexpr Index outerSize() const {
+    Index outerSize() const
-    return IsVectorAtCompileTime ? 1 : int(IsRowMajor) ? this->rows() : this->cols();
+    {
      return IsVectorAtCompileTime ? 1
           : int(IsRowMajor) ? this->rows() : this->cols();
    }
    /** \returns the inner size.
@@ -217,173 +207,182 @@ class DenseBase
      * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a 
      * column-major matrix, and the number of columns for a row-major matrix. */
-  EIGEN_DEVICE_FUNC constexpr Index innerSize() const {
+    Index innerSize() const
-    return IsVectorAtCompileTime ? this->size() : int(IsRowMajor) ? this->cols() : this->rows();
+    {
      return IsVectorAtCompileTime ? this->size()
           : int(IsRowMajor) ? this->cols() : this->rows();
    }
    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-   * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and
+      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-   * does nothing else.
+      * nothing else.
      */
-  EIGEN_DEVICE_FUNC void resize(Index newSize) {
+    void resize(Index size)
-    EIGEN_ONLY_USED_FOR_DEBUG(newSize);
+    {
-    eigen_assert(newSize == this->size() && "DenseBase::resize() does not actually allow to resize.");
+      EIGEN_ONLY_USED_FOR_DEBUG(size);
      eigen_assert(size == this->size()
                && "DenseBase::resize() does not actually allow to resize.");
    }
    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-   * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and
+      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-   * does nothing else.
+      * nothing else.
      */
-  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) {
+    void resize(Index rows, Index cols)
    {
      EIGEN_ONLY_USED_FOR_DEBUG(rows);
      EIGEN_ONLY_USED_FOR_DEBUG(cols);
-    eigen_assert(rows == this->rows() && cols == this->cols() &&
+      eigen_assert(rows == this->rows() && cols == this->cols()
-                 "DenseBase::resize() does not actually allow to resize.");
+                && "DenseBase::resize() does not actually allow to resize.");
    }
 #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** \internal Represents a matrix with all coefficients equal to one another*/
-  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
-  /** \internal Represents a matrix with all coefficients equal to zero*/
+    /** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */
-  typedef CwiseNullaryOp<internal::scalar_zero_op<Scalar>, PlainObject> ZeroReturnType;
+    typedef CwiseNullaryOp<internal::linspaced_op<Scalar,false>,Derived> SequentialLinSpacedReturnType;
  /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
  EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>, PlainObject> SequentialLinSpacedReturnType;
    /** \internal Represents a vector with linearly spaced coefficients that allows random access. */
-  typedef CwiseNullaryOp<internal::linspaced_op<Scalar>, PlainObject> RandomAccessLinSpacedReturnType;
+    typedef CwiseNullaryOp<internal::linspaced_op<Scalar,true>,Derived> RandomAccessLinSpacedReturnType;
  /** \internal Represents a vector with equally spaced coefficients that allows random access. */
  typedef CwiseNullaryOp<internal::equalspaced_op<Scalar>, PlainObject> RandomAccessEqualSpacedReturnType;
    /** \internal the return type of MatrixBase::eigenvalues() */
-  typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real,
+    typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
                 internal::traits<Derived>::ColsAtCompileTime, 1>
      EigenvaluesReturnType;
 #endif // not EIGEN_PARSED_BY_DOXYGEN
    /** Copies \a other into *this. \returns a reference to *this. */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other);
+    Derived& operator=(const DenseBase<OtherDerived>& other);
    /** Special case of the template operator=, in order to prevent the compiler
      * from generating a default operator= (issue hit with g++ 4.1)
      */
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const DenseBase& other);
+    Derived& operator=(const DenseBase& other);
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr Derived& operator=(const EigenBase<OtherDerived>& other);
+    Derived& operator=(const EigenBase<OtherDerived> &other);
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr Derived& operator+=(const EigenBase<OtherDerived>& other);
+    Derived& operator+=(const EigenBase<OtherDerived> &other);
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr Derived& operator-=(const EigenBase<OtherDerived>& other);
+    Derived& operator-=(const EigenBase<OtherDerived> &other);
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC Derived& operator=(const ReturnByValue<OtherDerived>& func);
+    Derived& operator=(const ReturnByValue<OtherDerived>& func);
-  /** \internal
+#ifndef EIGEN_PARSED_BY_DOXYGEN
-   * Copies \a other into *this without evaluating other. \returns a reference to *this. */
+    /** Copies \a other into *this without evaluating other. \returns a reference to *this. */
    template<typename OtherDerived>
-  /** \deprecated */
+    Derived& lazyAssign(const DenseBase<OtherDerived>& other);
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC constexpr Derived& lazyAssign(const DenseBase<OtherDerived>& other);
+#endif // not EIGEN_PARSED_BY_DOXYGEN
-  EIGEN_DEVICE_FUNC CommaInitializer<Derived> operator<<(const Scalar& s);
+    CommaInitializer<Derived> operator<< (const Scalar& s);
    template<unsigned int Added,unsigned int Removed>
-  /** \deprecated it now returns \c *this */
+    const Flagged<Derived, Added, Removed> flagged() const;
  EIGEN_DEPRECATED const Derived& flagged() const {
    return derived();
  }
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC CommaInitializer<Derived> operator<<(const DenseBase<OtherDerived>& other);
+    CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
-  typedef Transpose<Derived> TransposeReturnType;
+    Eigen::Transpose<Derived> transpose();
-  EIGEN_DEVICE_FUNC TransposeReturnType transpose();
+    typedef const Transpose<const Derived> ConstTransposeReturnType;
-  typedef Transpose<const Derived> ConstTransposeReturnType;
+    ConstTransposeReturnType transpose() const;
-  EIGEN_DEVICE_FUNC const ConstTransposeReturnType transpose() const;
+    void transposeInPlace();
-  EIGEN_DEVICE_FUNC void transposeInPlace();
+#ifndef EIGEN_NO_DEBUG
  protected:
    template<typename OtherDerived>
    void checkTransposeAliasing(const OtherDerived& other) const;
  public:
 #endif
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(Index rows, Index cols, const Scalar& value);
+    typedef VectorBlock<Derived> SegmentReturnType;
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(Index size, const Scalar& value);
+    typedef const VectorBlock<const Derived> ConstSegmentReturnType;
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(const Scalar& value);
+    template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
    template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
-  EIGEN_DEPRECATED_WITH_REASON("The method may result in accuracy loss. Use .EqualSpaced() instead.")
+    // Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
-  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Sequential_t, Index size, const Scalar& low,
+    SegmentReturnType segment(Index start, Index size);
-                                                                           const Scalar& high);
+    typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const;
  EIGEN_DEPRECATED_WITH_REASON("The method may result in accuracy loss. Use .EqualSpaced() instead.")
  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Sequential_t, const Scalar& low,
                                                                           const Scalar& high);
-  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Index size, const Scalar& low,
+    SegmentReturnType head(Index size);
-                                                                           const Scalar& high);
+    typename DenseBase::ConstSegmentReturnType head(Index size) const;
  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(const Scalar& low, const Scalar& high);
-  EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType EqualSpaced(Index size, const Scalar& low,
+    SegmentReturnType tail(Index size);
-                                                                               const Scalar& step);
+    typename DenseBase::ConstSegmentReturnType tail(Index size) const;
-  EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType EqualSpaced(const Scalar& low, const Scalar& step);
+
    template<int Size> typename FixedSegmentReturnType<Size>::Type head();
    template<int Size> typename ConstFixedSegmentReturnType<Size>::Type head() const;
    template<int Size> typename FixedSegmentReturnType<Size>::Type tail();
    template<int Size> typename ConstFixedSegmentReturnType<Size>::Type tail() const;
    template<int Size> typename FixedSegmentReturnType<Size>::Type segment(Index start);
    template<int Size> typename ConstFixedSegmentReturnType<Size>::Type segment(Index start) const;
    static const ConstantReturnType
    Constant(Index rows, Index cols, const Scalar& value);
    static const ConstantReturnType
    Constant(Index size, const Scalar& value);
    static const ConstantReturnType
    Constant(const Scalar& value);
    static const SequentialLinSpacedReturnType
    LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
    static const RandomAccessLinSpacedReturnType
    LinSpaced(Index size, const Scalar& low, const Scalar& high);
    static const SequentialLinSpacedReturnType
    LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
    static const RandomAccessLinSpacedReturnType
    LinSpaced(const Scalar& low, const Scalar& high);
    template<typename CustomNullaryOp>
-  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(Index rows, Index cols,
+    static const CwiseNullaryOp<CustomNullaryOp, Derived>
-                                                                                          const CustomNullaryOp& func);
+    NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
    template<typename CustomNullaryOp>
-  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(Index size,
+    static const CwiseNullaryOp<CustomNullaryOp, Derived>
-                                                                                          const CustomNullaryOp& func);
+    NullaryExpr(Index size, const CustomNullaryOp& func);
    template<typename CustomNullaryOp>
-  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(const CustomNullaryOp& func);
+    static const CwiseNullaryOp<CustomNullaryOp, Derived>
    NullaryExpr(const CustomNullaryOp& func);
-  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero(Index rows, Index cols);
+    static const ConstantReturnType Zero(Index rows, Index cols);
-  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero(Index size);
+    static const ConstantReturnType Zero(Index size);
-  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero();
+    static const ConstantReturnType Zero();
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
+    static const ConstantReturnType Ones(Index rows, Index cols);
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
+    static const ConstantReturnType Ones(Index size);
-  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
+    static const ConstantReturnType Ones();
-  EIGEN_DEVICE_FUNC void fill(const Scalar& value);
+    void fill(const Scalar& value);
-  EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
+    Derived& setConstant(const Scalar& value);
-  EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
+    Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
-  EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
+    Derived& setLinSpaced(const Scalar& low, const Scalar& high);
-  EIGEN_DEVICE_FUNC Derived& setEqualSpaced(Index size, const Scalar& low, const Scalar& step);
+    Derived& setZero();
-  EIGEN_DEVICE_FUNC Derived& setEqualSpaced(const Scalar& low, const Scalar& step);
+    Derived& setOnes();
-  EIGEN_DEVICE_FUNC Derived& setZero();
+    Derived& setRandom();
  EIGEN_DEVICE_FUNC Derived& setOnes();
  EIGEN_DEVICE_FUNC Derived& setRandom();
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr bool isApprox(const DenseBase<OtherDerived>& other,
+    bool isApprox(const DenseBase<OtherDerived>& other,
-                                            const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+                  RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
-  EIGEN_DEVICE_FUNC constexpr bool isMuchSmallerThan(
+    bool isMuchSmallerThan(const RealScalar& other,
-      const RealScalar& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+                           RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr bool isMuchSmallerThan(
+    bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
-      const DenseBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+                           RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
-  EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value,
+    bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
-                                            const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    bool isConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
-  EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value,
+    bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
-                                    const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    bool isOnes(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
  EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
  EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-  EIGEN_DEVICE_FUNC inline bool hasNaN() const;
+    inline Derived& operator*=(const Scalar& other);
-  EIGEN_DEVICE_FUNC inline bool allFinite() const;
+    inline Derived& operator/=(const Scalar& other);
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator*=(const Scalar& other);
  template <bool Enable = internal::complex_array_access<Scalar>::value, typename = std::enable_if_t<Enable>>
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator*=(const RealScalar& other);
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator/=(const Scalar& other);
  template <bool Enable = internal::complex_array_access<Scalar>::value, typename = std::enable_if_t<Enable>>
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator/=(const RealScalar& other);
  typedef internal::add_const_on_value_type_t<typename internal::eval<Derived>::type> EvalReturnType;
    /** \returns the matrix or vector obtained by evaluating this expression.
      *
      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
      * a const reference, in order to avoid a useless copy.
   *
   * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page
   * \endlink.
      */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvalReturnType eval() const {
+    EIGEN_STRONG_INLINE const typename internal::eval<Derived>::type eval() const
    {
      // Even though MSVC does not honor strong inlining when the return type
      // is a dynamic matrix, we desperately need strong inlining for fixed
      // size types on MSVC.
@@ -394,280 +393,151 @@ class DenseBase
      *
      */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(const DenseBase<OtherDerived>& other) {
+    void swap(const DenseBase<OtherDerived>& other,
-    EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase, THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+              int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
-    eigen_assert(rows() == other.rows() && cols() == other.cols());
+    {
-    call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+      SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
    }
    /** swaps *this with the matrix or array \a other.
      *
      */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(PlainObjectBase<OtherDerived>& other) {
+    void swap(PlainObjectBase<OtherDerived>& other)
-    eigen_assert(rows() == other.rows() && cols() == other.cols());
+    {
-    call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
+      SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
    }
  EIGEN_DEVICE_FUNC constexpr inline const NestByValue<Derived> nestByValue() const;
  EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
  EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
  template <bool Enable>
  EIGEN_DEVICE_FUNC inline const std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&>
  forceAlignedAccessIf() const;
  template <bool Enable>
  EIGEN_DEVICE_FUNC inline std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&> forceAlignedAccessIf();
-  EIGEN_DEVICE_FUNC Scalar sum() const;
+    inline const NestByValue<Derived> nestByValue() const;
-  EIGEN_DEVICE_FUNC Scalar mean() const;
+    inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
-  EIGEN_DEVICE_FUNC Scalar trace() const;
+    inline ForceAlignedAccess<Derived> forceAlignedAccess();
    template<bool Enable> inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
    template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
-  EIGEN_DEVICE_FUNC Scalar prod() const;
+    Scalar sum() const;
    Scalar mean() const;
    Scalar trace() const;
-  template <int NaNPropagation>
+    Scalar prod() const;
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
  template <int NaNPropagation>
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
-  // By default, the fastest version with undefined NaN propagation semantics is
+    typename internal::traits<Derived>::Scalar minCoeff() const;
-  // used.
+    typename internal::traits<Derived>::Scalar maxCoeff() const;
  // TODO(rmlarsen): Replace with default template argument (C++14 is now the minimum standard).
  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff() const {
    return minCoeff<PropagateFast>();
  }
  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff() const {
    return maxCoeff<PropagateFast>();
  }
  template <int NaNPropagation, typename IndexType>
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
  template <int NaNPropagation, typename IndexType>
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
  template <int NaNPropagation, typename IndexType>
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
  template <int NaNPropagation, typename IndexType>
  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
  // TODO(rmlarsen): Replace these methods with a default template argument (C++14 is now the minimum standard).
    template<typename IndexType>
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const {
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
    return minCoeff<PropagateFast>(row, col);
  }
    template<typename IndexType>
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const {
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
    return maxCoeff<PropagateFast>(row, col);
  }
    template<typename IndexType>
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const {
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
    return minCoeff<PropagateFast>(index);
  }
    template<typename IndexType>
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const {
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
    return maxCoeff<PropagateFast>(index);
  }
    template<typename BinaryOp>
-  EIGEN_DEVICE_FUNC Scalar redux(const BinaryOp& func) const;
+    typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
    redux(const BinaryOp& func) const;
    template<typename Visitor>
-  EIGEN_DEVICE_FUNC void visit(Visitor& func) const;
+    void visit(Visitor& func) const;
-  /** \returns a WithFormat proxy object allowing to print a matrix the with given
+    inline const WithFormat<Derived> format(const IOFormat& fmt) const;
   * format \a fmt.
   *
   * See class IOFormat for some examples.
   *
   * \sa class IOFormat, class WithFormat
   */
  inline const WithFormat<Derived> format(const IOFormat& fmt) const { return WithFormat<Derived>(derived(), fmt); }
    /** \returns the unique coefficient of a 1x1 expression */
-  EIGEN_DEVICE_FUNC CoeffReturnType value() const {
+    CoeffReturnType value() const
-    EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1);
+    {
      EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
      eigen_assert(this->rows() == 1 && this->cols() == 1);
      return derived().coeff(0,0);
    }
-  EIGEN_DEVICE_FUNC bool all() const;
+/////////// Array module ///////////
-  EIGEN_DEVICE_FUNC bool any() const;
+
-  EIGEN_DEVICE_FUNC Index count() const;
+    bool all(void) const;
    bool any(void) const;
    Index count() const;
    typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
    typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
    typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
    typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
-  /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions
+    ConstRowwiseReturnType rowwise() const;
-   *
+    RowwiseReturnType rowwise();
-   * Example: \include MatrixBase_rowwise.cpp
+    ConstColwiseReturnType colwise() const;
-   * Output: \verbinclude MatrixBase_rowwise.out
+    ColwiseReturnType colwise();
   *
   * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
   */
  // Code moved here due to a CUDA compiler bug
  EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const { return ConstRowwiseReturnType(derived()); }
  EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
-  /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index rows, Index cols);
-   *
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index size);
-   * Example: \include MatrixBase_colwise.cpp
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random();
   * Output: \verbinclude MatrixBase_colwise.out
   *
   * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
   */
  EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const { return ConstColwiseReturnType(derived()); }
  EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
  typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>, PlainObject> RandomReturnType;
  static const RandomReturnType Random(Index rows, Index cols);
  static const RandomReturnType Random(Index size);
  static const RandomReturnType Random();
    template<typename ThenDerived,typename ElseDerived>
-  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    const Select<Derived,ThenDerived,ElseDerived>
-      internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+    select(const DenseBase<ThenDerived>& thenMatrix,
-                                         typename DenseBase<ElseDerived>::Scalar, Scalar>,
+           const DenseBase<ElseDerived>& elseMatrix) const;
      ThenDerived, ElseDerived, Derived>
  select(const DenseBase<ThenDerived>& thenMatrix, const DenseBase<ElseDerived>& elseMatrix) const;
    template<typename ThenDerived>
-  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
-      internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+    select(const DenseBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
                                         typename DenseBase<ThenDerived>::Scalar, Scalar>,
      ThenDerived, typename DenseBase<ThenDerived>::ConstantReturnType, Derived>
  select(const DenseBase<ThenDerived>& thenMatrix, const typename DenseBase<ThenDerived>::Scalar& elseScalar) const;
    template<typename ElseDerived>
-  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
-      internal::scalar_boolean_select_op<typename DenseBase<ElseDerived>::Scalar,
+    select(typename ElseDerived::Scalar thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
                                         typename DenseBase<ElseDerived>::Scalar, Scalar>,
      typename DenseBase<ElseDerived>::ConstantReturnType, ElseDerived, Derived>
  select(const typename DenseBase<ElseDerived>::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
-  template <int p>
+    template<int p> RealScalar lpNorm() const;
  RealScalar lpNorm() const;
    template<int RowFactor, int ColFactor>
-  EIGEN_DEVICE_FUNC const Replicate<Derived, RowFactor, ColFactor> replicate() const;
+    const Replicate<Derived,RowFactor,ColFactor> replicate() const;
-  /**
+    const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
   * \return an expression of the replication of \c *this
   *
   * Example: \include MatrixBase_replicate_int_int.cpp
   * Output: \verbinclude MatrixBase_replicate_int_int.out
   *
   * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
   */
  // Code moved here due to a CUDA compiler bug
  EIGEN_DEVICE_FUNC const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const {
    return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
  }
    typedef Reverse<Derived, BothDirections> ReverseReturnType;
    typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
-  EIGEN_DEVICE_FUNC ReverseReturnType reverse();
+    ReverseReturnType reverse();
-  /** This is the const version of reverse(). */
+    ConstReverseReturnType reverse() const;
-  // Code moved here due to a CUDA compiler bug
+    void reverseInPlace();
  EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const { return ConstReverseReturnType(derived()); }
  EIGEN_DEVICE_FUNC void reverseInPlace();
 #ifdef EIGEN_PARSED_BY_DOXYGEN
  /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
   * iterator type as returned by the begin() and end() methods.
   */
  typedef random_access_iterator_type iterator;
  /** This is the const version of iterator (aka read-only) */
  typedef random_access_iterator_type const_iterator;
 #else
  typedef std::conditional_t<(Flags & DirectAccessBit) == DirectAccessBit,
                             internal::pointer_based_stl_iterator<Derived>,
                             internal::generic_randaccess_stl_iterator<Derived>>
      iterator_type;
  typedef std::conditional_t<(Flags & DirectAccessBit) == DirectAccessBit,
                             internal::pointer_based_stl_iterator<const Derived>,
                             internal::generic_randaccess_stl_iterator<const Derived>>
      const_iterator_type;
  // Stl-style iterators are supported only for vectors.
  typedef std::conditional_t<IsVectorAtCompileTime, iterator_type, void> iterator;
  typedef std::conditional_t<IsVectorAtCompileTime, const_iterator_type, void> const_iterator;
 #endif
  inline iterator begin();
  inline const_iterator begin() const;
  inline const_iterator cbegin() const;
  inline iterator end();
  inline const_iterator end() const;
  inline const_iterator cend() const;
  using RealViewReturnType = std::conditional_t<NumTraits<Scalar>::IsComplex, RealView<Derived>, Derived&>;
  using ConstRealViewReturnType =
      std::conditional_t<NumTraits<Scalar>::IsComplex, RealView<const Derived>, const Derived&>;
  EIGEN_DEVICE_FUNC RealViewReturnType realView();
  EIGEN_DEVICE_FUNC ConstRealViewReturnType realView() const;
 #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#   include "../plugins/BlockMethods.h"
 #define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
 #define EIGEN_DOC_UNARY_ADDONS(X, Y)
 #include "../plugins/CommonCwiseUnaryOps.inc"
 #include "../plugins/BlockMethods.inc"
 // Defines operator()(const RowIndices&, const ColIndices&) and other indexed view methods.
 #include "../plugins/IndexedViewMethods.inc"
 #include "../plugins/ReshapedMethods.inc"
 #   ifdef EIGEN_DENSEBASE_PLUGIN
 #     include EIGEN_DENSEBASE_PLUGIN
 #   endif
 #undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+
-#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
+#ifdef EIGEN2_SUPPORT
-#undef EIGEN_DOC_UNARY_ADDONS
+
    Block<Derived> corner(CornerType type, Index cRows, Index cCols);
    const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const;
    template<int CRows, int CCols>
    Block<Derived, CRows, CCols> corner(CornerType type);
    template<int CRows, int CCols>
    const Block<Derived, CRows, CCols> corner(CornerType type) const;
 #endif // EIGEN2_SUPPORT
    // disable the use of evalTo for dense objects with a nice compilation error
-  template <typename Dest>
+    template<typename Dest> inline void evalTo(Dest& ) const
-  EIGEN_DEVICE_FUNC inline void evalTo(Dest&) const {
+    {
-    EIGEN_STATIC_ASSERT((internal::is_same<Dest, void>::value),
+      EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
                        THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
    }
  protected:
  EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
    /** Default constructor. Do nothing. */
-#ifdef EIGEN_INTERNAL_DEBUGGING
+    DenseBase()
-  EIGEN_DEVICE_FUNC constexpr DenseBase() {
+    {
      /* Just checks for self-consistency of the flags.
-     * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
+       * Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down
       */
-    EIGEN_STATIC_ASSERT(
+#ifdef EIGEN_INTERNAL_DEBUGGING
-        (internal::check_implication(MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1, int(IsRowMajor)) &&
+      EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
-         internal::check_implication(MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1, int(!IsRowMajor))),
+                        && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
                          INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
  }
 #else
  EIGEN_DEVICE_FUNC constexpr DenseBase() = default;
 #endif
    }
  private:
-  EIGEN_DEVICE_FUNC explicit DenseBase(int);
+    explicit DenseBase(int);
-  EIGEN_DEVICE_FUNC DenseBase(int, int);
+    DenseBase(int,int);
-  template <typename OtherDerived>
+    template<typename OtherDerived> explicit DenseBase(const DenseBase<OtherDerived>&);
  EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
 };
 /** Free-function swap.
 */
 template <typename DerivedA, typename DerivedB>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    // Use forwarding references to capture all combinations of cv-qualified l+r-value cases.
    std::enable_if_t<std::is_base_of<DenseBase<std::decay_t<DerivedA>>, std::decay_t<DerivedA>>::value &&
                         std::is_base_of<DenseBase<std::decay_t<DerivedB>>, std::decay_t<DerivedB>>::value,
                     void>
    swap(DerivedA&& a, DerivedB&& b) {
  a.swap(b);
 }
 }  // end namespace Eigen
 #endif // EIGEN_DENSEBASE_H
--- a/Eigen/src/Core/DenseCoeffsBase.h
+++ b/Eigen/src/Core/DenseCoeffsBase.h
@@ -3,30 +3,39 @@
 //
 // Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DENSECOEFFSBASE_H
 #define EIGEN_DENSECOEFFSBASE_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
-template <typename T>
+template<typename T> struct add_const_on_value_type_if_arithmetic
-struct add_const_on_value_type_if_arithmetic {
+{
-  typedef std::conditional_t<is_arithmetic<T>::value, T, add_const_on_value_type_t<T>> type;
+  typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
 };
-}  // namespace internal
+}
 /** \brief Base class providing read-only coefficient access to matrices and arrays.
  * \ingroup Core_Module
  * \tparam Derived Type of the derived class
- *
+  * \tparam #ReadOnlyAccessors Constant indicating read-only access
 * \note #ReadOnlyAccessors Constant indicating read-only access
  *
  * This class defines the \c operator() \c const function and friends, which can be used to read specific
  * entries of a matrix or array.
@@ -35,9 +44,11 @@ struct add_const_on_value_type_if_arithmetic {
  *     \ref TopicClassHierarchy
  */
 template<typename Derived>
-class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
+class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
 {
  public:
    typedef typename internal::traits<Derived>::StorageKind StorageKind;
    typedef typename internal::traits<Derived>::Index Index;
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
@@ -45,36 +56,34 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
    // - This is the return type of the coeff() method.
    // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
    // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
-  // - The DirectAccessBit means exactly that the underlying data of coefficients can be directly accessed as a plain
+    // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
  // strided array, which means exactly that the underlying data of coefficients does exist in memory, which means
  // exactly that the coefficients is const-referencable, which means exactly that we can have coeff() return a const
  // reference. For example, Map<const Matrix> have DirectAccessBit but not LvalueBit, so that Map<const Matrix>.coeff()
  // does points to a const Scalar& which exists in memory, while does not allow coeffRef() as it would not provide a
  // lvalue. Notice that DirectAccessBit and LvalueBit are mutually orthogonal.
  // - The is_arithmetic check is required since "const int", "const double", etc. will cause warnings on some systems
    // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
    // not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
-  typedef std::conditional_t<bool(internal::traits<Derived>::Flags&(LvalueBit | DirectAccessBit)), const Scalar&,
+    typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
-                             std::conditional_t<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>>
+                         const Scalar&,
-      CoeffReturnType;
+                         typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
                     >::type CoeffReturnType;
-  typedef typename internal::add_const_on_value_type_if_arithmetic<typename internal::packet_traits<Scalar>::type>::type
+    typedef typename internal::add_const_on_value_type_if_arithmetic<
-      PacketReturnType;
+                         typename internal::packet_traits<Scalar>::type
                     >::type PacketReturnType;
    typedef EigenBase<Derived> Base;
  using Base::cols;
  using Base::derived;
    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::derived;
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const {
+    EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
    {
      return int(Derived::RowsAtCompileTime) == 1 ? 0
          : int(Derived::ColsAtCompileTime) == 1 ? inner
          : int(Derived::Flags)&RowMajorBit ? outer
          : inner;
    }
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const {
+    EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
    {
      return int(Derived::ColsAtCompileTime) == 1 ? 0
          : int(Derived::RowsAtCompileTime) == 1 ? inner
          : int(Derived::Flags)&RowMajorBit ? inner
@@ -95,32 +104,30 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      *
      * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
      */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeff(Index row, Index col) const {
+    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
-    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    {
-    return internal::evaluator<Derived>(derived()).coeff(row, col);
+      eigen_internal_assert(row >= 0 && row < rows()
                        && col >= 0 && col < cols());
      return derived().coeff(row, col);
    }
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeffByOuterInner(Index outer, Index inner) const {
+    EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
-    return coeff(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+    {
      return coeff(rowIndexByOuterInner(outer, inner),
                   colIndexByOuterInner(outer, inner));
    }
    /** \returns the coefficient at given the given row and column.
      *
      * \sa operator()(Index,Index), operator[](Index)
      */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator()(Index row, Index col) const {
+    EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
-    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    {
-    return coeff(row, col);
+      eigen_assert(row >= 0 && row < rows()
          && col >= 0 && col < cols());
      return derived().coeff(row, col);
    }
 #ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
  /** \returns the coefficient at given the given row and column.
   *
   * \sa operator[](Index,Index), operator[](Index)
   */
  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator[](Index row, Index col) const { return operator()(row, col); }
 #endif
    /** Short version: don't use this function, use
      * \link operator[](Index) const \endlink instead.
      *
@@ -136,13 +143,14 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
      */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeff(Index index) const {
+    EIGEN_STRONG_INLINE CoeffReturnType
-    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+    coeff(Index index) const
-                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+    {
      eigen_internal_assert(index >= 0 && index < size());
-    return internal::evaluator<Derived>(derived()).coeff(index);
+      return derived().coeff(index);
    }
    /** \returns the coefficient at given index.
      *
      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
@@ -151,11 +159,15 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      * z() const, w() const
      */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator[](Index index) const {
+    EIGEN_STRONG_INLINE CoeffReturnType
    operator[](Index index) const
    {
      #ifndef EIGEN2_SUPPORT
      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
      #endif
      eigen_assert(index >= 0 && index < size());
-    return coeff(index);
+      return derived().coeff(index);
    }
    /** \returns the coefficient at given index.
@@ -168,35 +180,32 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      * z() const, w() const
      */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator()(Index index) const {
+    EIGEN_STRONG_INLINE CoeffReturnType
    operator()(Index index) const
    {
      eigen_assert(index >= 0 && index < size());
-    return coeff(index);
+      return derived().coeff(index);
    }
    /** equivalent to operator[](0).  */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType x() const { return (*this)[0]; }
+    EIGEN_STRONG_INLINE CoeffReturnType
    x() const { return (*this)[0]; }
    /** equivalent to operator[](1).  */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType y() const {
+    EIGEN_STRONG_INLINE CoeffReturnType
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 2, OUT_OF_RANGE_ACCESS);
+    y() const { return (*this)[1]; }
    return (*this)[1];
  }
    /** equivalent to operator[](2).  */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType z() const {
+    EIGEN_STRONG_INLINE CoeffReturnType
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 3, OUT_OF_RANGE_ACCESS);
+    z() const { return (*this)[2]; }
    return (*this)[2];
  }
    /** equivalent to operator[](3).  */
-  EIGEN_DEVICE_FUNC constexpr CoeffReturnType w() const {
+    EIGEN_STRONG_INLINE CoeffReturnType
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 4, OUT_OF_RANGE_ACCESS);
+    w() const { return (*this)[3]; }
    return (*this)[3];
  }
    /** \internal
      * \returns the packet of coefficients starting at the given row and column. It is your responsibility
@@ -209,16 +218,20 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      */
    template<int LoadMode>
-  EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const {
+    EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
-    typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
+    {
-    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+      eigen_internal_assert(row >= 0 && row < rows()
-    return internal::evaluator<Derived>(derived()).template packet<LoadMode, DefaultPacketType>(row, col);
+                      && col >= 0 && col < cols());
      return derived().template packet<LoadMode>(row,col);
    }
    /** \internal */
    template<int LoadMode>
-  EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const {
+    EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
-    return packet<LoadMode>(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+    {
      return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
                              colIndexByOuterInner(outer, inner));
    }
    /** \internal
@@ -232,12 +245,10 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
      */
    template<int LoadMode>
-  EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+    {
                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
    typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
      eigen_internal_assert(index >= 0 && index < size());
-    return internal::evaluator<Derived>(derived()).template packet<LoadMode, DefaultPacketType>(index);
+      return derived().template packet<LoadMode>(index);
    }
  protected:
@@ -264,8 +275,7 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
 /** \brief Base class providing read/write coefficient access to matrices and arrays.
  * \ingroup Core_Module
  * \tparam Derived Type of the derived class
- *
+  * \tparam #WriteAccessors Constant indicating read/write access
 * \note #WriteAccessors Constant indicating read/write access
  *
  * This class defines the non-const \c operator() function and friends, which can be used to write specific
  * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
@@ -274,28 +284,31 @@ class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
  * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
  */
 template<typename Derived>
-class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> {
+class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
 {
  public:
    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
    typedef typename internal::traits<Derived>::StorageKind StorageKind;
    typedef typename internal::traits<Derived>::Index Index;
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
    typedef typename NumTraits<Scalar>::Real RealScalar;
    using Base::coeff;
-  using Base::colIndexByOuterInner;
+    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::derived;
    using Base::rowIndexByOuterInner;
-  using Base::rows;
+    using Base::colIndexByOuterInner;
  using Base::size;
    using Base::operator[];
    using Base::operator();
  using Base::w;
    using Base::x;
    using Base::y;
    using Base::z;
    using Base::w;
    /** Short version: don't use this function, use
      * \link operator()(Index,Index) \endlink instead.
@@ -311,31 +324,33 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
      *
      * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
      */
-  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index row, Index col) {
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
-    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    {
-    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
+      eigen_internal_assert(row >= 0 && row < rows()
                        && col >= 0 && col < cols());
      return derived().coeffRef(row, col);
    }
-  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRefByOuterInner(Index outer, Index inner) {
+    EIGEN_STRONG_INLINE Scalar&
-    return coeffRef(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+    coeffRefByOuterInner(Index outer, Index inner)
    {
      return coeffRef(rowIndexByOuterInner(outer, inner),
                      colIndexByOuterInner(outer, inner));
    }
    /** \returns a reference to the coefficient at given the given row and column.
      *
      * \sa operator[](Index)
      */
-  EIGEN_DEVICE_FUNC constexpr Scalar& operator()(Index row, Index col) {
+
-    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    EIGEN_STRONG_INLINE Scalar&
-    return coeffRef(row, col);
+    operator()(Index row, Index col)
    {
      eigen_assert(row >= 0 && row < rows()
          && col >= 0 && col < cols());
      return derived().coeffRef(row, col);
    }
 #ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
  /** \returns a reference to the coefficient at given the given row and column.
   *
   * \sa operator[](Index)
   */
  EIGEN_DEVICE_FUNC constexpr Scalar& operator[](Index row, Index col) { return operator()(row, col); }
 #endif
    /** Short version: don't use this function, use
      * \link operator[](Index) \endlink instead.
@@ -352,11 +367,11 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
      * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
      */
-  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index index) {
+    EIGEN_STRONG_INLINE Scalar&
-    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+    coeffRef(Index index)
-                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+    {
      eigen_internal_assert(index >= 0 && index < size());
-    return internal::evaluator<Derived>(derived()).coeffRef(index);
+      return derived().coeffRef(index);
    }
    /** \returns a reference to the coefficient at given index.
@@ -366,11 +381,15 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
      */
-  EIGEN_DEVICE_FUNC constexpr Scalar& operator[](Index index) {
+    EIGEN_STRONG_INLINE Scalar&
    operator[](Index index)
    {
      #ifndef EIGEN2_SUPPORT
      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
      #endif
      eigen_assert(index >= 0 && index < size());
-    return coeffRef(index);
+      return derived().coeffRef(index);
    }
    /** \returns a reference to the coefficient at given index.
@@ -382,135 +401,226 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
      */
-  EIGEN_DEVICE_FUNC constexpr Scalar& operator()(Index index) {
+    EIGEN_STRONG_INLINE Scalar&
    operator()(Index index)
    {
      eigen_assert(index >= 0 && index < size());
-    return coeffRef(index);
+      return derived().coeffRef(index);
    }
    /** equivalent to operator[](0).  */
-  EIGEN_DEVICE_FUNC constexpr Scalar& x() { return (*this)[0]; }
+    EIGEN_STRONG_INLINE Scalar&
    x() { return (*this)[0]; }
    /** equivalent to operator[](1).  */
-  EIGEN_DEVICE_FUNC constexpr Scalar& y() {
+    EIGEN_STRONG_INLINE Scalar&
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 2, OUT_OF_RANGE_ACCESS);
+    y() { return (*this)[1]; }
    return (*this)[1];
  }
    /** equivalent to operator[](2).  */
-  EIGEN_DEVICE_FUNC constexpr Scalar& z() {
+    EIGEN_STRONG_INLINE Scalar&
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 3, OUT_OF_RANGE_ACCESS);
+    z() { return (*this)[2]; }
    return (*this)[2];
  }
    /** equivalent to operator[](3).  */
-  EIGEN_DEVICE_FUNC constexpr Scalar& w() {
+    EIGEN_STRONG_INLINE Scalar&
-    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 4, OUT_OF_RANGE_ACCESS);
+    w() { return (*this)[3]; }
-    return (*this)[3];
+
    /** \internal
      * Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
      * to ensure that a packet really starts there. This method is only available on expressions having the
      * PacketAccessBit.
      *
      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
      * starting at an address which is a multiple of the packet size.
      */
    template<int StoreMode>
    EIGEN_STRONG_INLINE void writePacket
    (Index row, Index col, const typename internal::packet_traits<Scalar>::type& x)
    {
      eigen_internal_assert(row >= 0 && row < rows()
                        && col >= 0 && col < cols());
      derived().template writePacket<StoreMode>(row,col,x);
    }
    /** \internal */
    template<int StoreMode>
    EIGEN_STRONG_INLINE void writePacketByOuterInner
    (Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& x)
    {
      writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
                            colIndexByOuterInner(outer, inner),
                            x);
    }
    /** \internal
      * Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
      * to ensure that a packet really starts there. This method is only available on expressions having the
      * PacketAccessBit and the LinearAccessBit.
      *
      * The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
      * starting at an address which is a multiple of the packet size.
      */
    template<int StoreMode>
    EIGEN_STRONG_INLINE void writePacket
    (Index index, const typename internal::packet_traits<Scalar>::type& x)
    {
      eigen_internal_assert(index >= 0 && index < size());
      derived().template writePacket<StoreMode>(index,x);
    }
 #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** \internal Copies the coefficient at position (row,col) of other into *this.
      *
      * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
      * with usual assignments.
      *
      * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
      */
    template<typename OtherDerived>
    EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
    {
      eigen_internal_assert(row >= 0 && row < rows()
                        && col >= 0 && col < cols());
      derived().coeffRef(row, col) = other.derived().coeff(row, col);
    }
    /** \internal Copies the coefficient at the given index of other into *this.
      *
      * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
      * with usual assignments.
      *
      * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
      */
    template<typename OtherDerived>
    EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
    {
      eigen_internal_assert(index >= 0 && index < size());
      derived().coeffRef(index) = other.derived().coeff(index);
    }
    template<typename OtherDerived>
    EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
    {
      const Index row = rowIndexByOuterInner(outer,inner);
      const Index col = colIndexByOuterInner(outer,inner);
      // derived() is important here: copyCoeff() may be reimplemented in Derived!
      derived().copyCoeff(row, col, other);
    }
    /** \internal Copies the packet at position (row,col) of other into *this.
      *
      * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
      * with usual assignments.
      *
      * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
      */
    template<typename OtherDerived, int StoreMode, int LoadMode>
    EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
    {
      eigen_internal_assert(row >= 0 && row < rows()
                        && col >= 0 && col < cols());
      derived().template writePacket<StoreMode>(row, col,
        other.derived().template packet<LoadMode>(row, col));
    }
    /** \internal Copies the packet at the given index of other into *this.
      *
      * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
      * with usual assignments.
      *
      * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
      */
    template<typename OtherDerived, int StoreMode, int LoadMode>
    EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other)
    {
      eigen_internal_assert(index >= 0 && index < size());
      derived().template writePacket<StoreMode>(index,
        other.derived().template packet<LoadMode>(index));
    }
    /** \internal */
    template<typename OtherDerived, int StoreMode, int LoadMode>
    EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
    {
      const Index row = rowIndexByOuterInner(outer,inner);
      const Index col = colIndexByOuterInner(outer,inner);
      // derived() is important here: copyCoeff() may be reimplemented in Derived!
      derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other);
    }
 #endif
 };
 /** \brief Base class providing direct read-only coefficient access to matrices and arrays.
  * \ingroup Core_Module
  * \tparam Derived Type of the derived class
- *
+  * \tparam #DirectAccessors Constant indicating direct access
 * \note #DirectAccessors Constant indicating direct access
  *
  * This class defines functions to work with strides which can be used to access entries directly. This class
  * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
  * \c operator() .
  *
- * \sa \blank \ref TopicClassHierarchy
+  * \sa \ref TopicClassHierarchy
  */
 template<typename Derived>
-class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> {
+class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
 {
  public:
    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
    typedef typename internal::traits<Derived>::Index Index;
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef typename NumTraits<Scalar>::Real RealScalar;
  using Base::cols;
  using Base::derived;
    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::derived;
    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
      *
      * \sa outerStride(), rowStride(), colStride()
      */
-  EIGEN_DEVICE_FUNC constexpr Index innerStride() const { return derived().innerStride(); }
+    inline Index innerStride() const
    {
      return derived().innerStride();
    }
    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
      *          in a column-major matrix).
      *
      * \sa innerStride(), rowStride(), colStride()
      */
-  EIGEN_DEVICE_FUNC constexpr Index outerStride() const { return derived().outerStride(); }
+    inline Index outerStride() const
    {
      return derived().outerStride();
    }
    // FIXME shall we remove it ?
-  constexpr Index stride() const { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); }
+    inline Index stride() const
    {
      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
    }
    /** \returns the pointer increment between two consecutive rows.
      *
      * \sa innerStride(), outerStride(), colStride()
      */
-  EIGEN_DEVICE_FUNC constexpr Index rowStride() const { return Derived::IsRowMajor ? outerStride() : innerStride(); }
+    inline Index rowStride() const
-
+    {
  /** \returns the pointer increment between two consecutive columns.
   *
   * \sa innerStride(), outerStride(), rowStride()
   */
  EIGEN_DEVICE_FUNC constexpr Index colStride() const { return Derived::IsRowMajor ? innerStride() : outerStride(); }
 };
 /** \brief Base class providing direct read/write coefficient access to matrices and arrays.
 * \ingroup Core_Module
 * \tparam Derived Type of the derived class
 *
 * \note #DirectWriteAccessors Constant indicating direct access
 *
 * This class defines functions to work with strides which can be used to access entries directly. This class
 * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
 * \c operator().
 *
 * \sa \blank \ref TopicClassHierarchy
 */
 template <typename Derived>
 class DenseCoeffsBase<Derived, DirectWriteAccessors> : public DenseCoeffsBase<Derived, WriteAccessors> {
 public:
  typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
  typedef typename internal::traits<Derived>::Scalar Scalar;
  typedef typename NumTraits<Scalar>::Real RealScalar;
  using Base::cols;
  using Base::derived;
  using Base::rows;
  using Base::size;
  /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
   *
   * \sa outerStride(), rowStride(), colStride()
   */
  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return derived().innerStride(); }
  /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
   *          in a column-major matrix).
   *
   * \sa innerStride(), rowStride(), colStride()
   */
  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return derived().outerStride(); }
  // FIXME shall we remove it ?
  constexpr Index stride() const noexcept { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); }
  /** \returns the pointer increment between two consecutive rows.
   *
   * \sa innerStride(), outerStride(), colStride()
   */
  EIGEN_DEVICE_FUNC constexpr Index rowStride() const noexcept {
      return Derived::IsRowMajor ? outerStride() : innerStride();
    }
@@ -518,66 +628,138 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors> : public DenseCoeffsBase<De
      *
      * \sa innerStride(), outerStride(), rowStride()
      */
-  EIGEN_DEVICE_FUNC constexpr Index colStride() const noexcept {
+    inline Index colStride() const
    {
      return Derived::IsRowMajor ? innerStride() : outerStride();
    }
 };
 /** \brief Base class providing direct read/write coefficient access to matrices and arrays.
  * \ingroup Core_Module
  * \tparam Derived Type of the derived class
  * \tparam #DirectWriteAccessors Constant indicating direct access
  *
  * This class defines functions to work with strides which can be used to access entries directly. This class
  * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
  * \c operator().
  *
  * \sa \ref TopicClassHierarchy
  */
 template<typename Derived>
 class DenseCoeffsBase<Derived, DirectWriteAccessors>
  : public DenseCoeffsBase<Derived, WriteAccessors>
 {
  public:
    typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
    typedef typename internal::traits<Derived>::Index Index;
    typedef typename internal::traits<Derived>::Scalar Scalar;
    typedef typename NumTraits<Scalar>::Real RealScalar;
    using Base::rows;
    using Base::cols;
    using Base::size;
    using Base::derived;
    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
      *
      * \sa outerStride(), rowStride(), colStride()
      */
    inline Index innerStride() const
    {
      return derived().innerStride();
    }
    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
      *          in a column-major matrix).
      *
      * \sa innerStride(), rowStride(), colStride()
      */
    inline Index outerStride() const
    {
      return derived().outerStride();
    }
    // FIXME shall we remove it ?
    inline Index stride() const
    {
      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
    }
    /** \returns the pointer increment between two consecutive rows.
      *
      * \sa innerStride(), outerStride(), colStride()
      */
    inline Index rowStride() const
    {
      return Derived::IsRowMajor ? outerStride() : innerStride();
    }
    /** \returns the pointer increment between two consecutive columns.
      *
      * \sa innerStride(), outerStride(), rowStride()
      */
    inline Index colStride() const
    {
      return Derived::IsRowMajor ? innerStride() : outerStride();
    }
 };
 namespace internal {
-template <int Alignment, typename Derived, bool JustReturnZero>
+template<typename Derived, bool JustReturnZero>
-struct first_aligned_impl {
+struct first_aligned_impl
-  static constexpr Index run(const Derived&) noexcept { return 0; }
+{
  inline static typename Derived::Index run(const Derived&)
  { return 0; }
 };
-template <int Alignment, typename Derived>
+template<typename Derived>
-struct first_aligned_impl<Alignment, Derived, false> {
+struct first_aligned_impl<Derived, false>
-  static inline Index run(const Derived& m) { return internal::first_aligned<Alignment>(m.data(), m.size()); }
+{
  inline static typename Derived::Index run(const Derived& m)
  {
    return first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size());
  }
 };
-/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect
+/** \internal \returns the index of the first element of the array that is well aligned for vectorization.
 * to \a Alignment for vectorization.
 *
 * \tparam Alignment requested alignment in Bytes.
  *
  * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
  * documentation.
  */
 template <int Alignment, typename Derived>
 static inline Index first_aligned(const DenseBase<Derived>& m) {
  enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
  return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
 }
 template<typename Derived>
-static inline Index first_default_aligned(const DenseBase<Derived>& m) {
+inline static typename Derived::Index first_aligned(const Derived& m)
-  typedef typename Derived::Scalar Scalar;
+{
-  typedef typename packet_traits<Scalar>::type DefaultPacketType;
+  return first_aligned_impl
-  return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment), Derived>(m);
+          <Derived, (Derived::Flags & AlignedBit) || !(Derived::Flags & DirectAccessBit)>
          ::run(m);
 }
 template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct inner_stride_at_compile_time {
+struct inner_stride_at_compile_time
 {
  enum { ret = traits<Derived>::InnerStrideAtCompileTime };
 };
 template<typename Derived>
-struct inner_stride_at_compile_time<Derived, false> {
+struct inner_stride_at_compile_time<Derived, false>
 {
  enum { ret = 0 };
 };
 template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct outer_stride_at_compile_time {
+struct outer_stride_at_compile_time
 {
  enum { ret = traits<Derived>::OuterStrideAtCompileTime };
 };
 template<typename Derived>
-struct outer_stride_at_compile_time<Derived, false> {
+struct outer_stride_at_compile_time<Derived, false>
 {
  enum { ret = 0 };
 };
 } // end namespace internal
 }  // end namespace Eigen
 #endif // EIGEN_DENSECOEFFSBASE_H
--- a/Eigen/src/Core/DenseStorage.h
+++ b/Eigen/src/Core/DenseStorage.h
@@ -3,500 +3,86 @@
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_MATRIXSTORAGE_H
 #define EIGEN_MATRIXSTORAGE_H
 #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) \
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
  X;                                                \
  EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
 #else
-#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
 #endif
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
-#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
+struct constructor_without_unaligned_array_assert {};
 #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)
 #else
 #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)                                        \
  eigen_assert((is_constant_evaluated() || (std::uintptr_t(array) % Alignment == 0)) &&     \
               "this assertion is explained here: "                                         \
               "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
               " **** READ THIS WEB PAGE !!! ****");
 #endif
 #if EIGEN_STACK_ALLOCATION_LIMIT
 #define EIGEN_MAKE_STACK_ALLOCATION_ASSERT(X) \
  EIGEN_STATIC_ASSERT(X <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG)
 #else
 #define EIGEN_MAKE_STACK_ALLOCATION_ASSERT(X)
 #endif
 /** \internal
  * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
  * to 16 bytes boundary if the total size is a multiple of 16 bytes.
  */
 template <typename T, int Size, int MatrixOrArrayOptions,
-          int Alignment = (MatrixOrArrayOptions & DontAlign) ? 0 : compute_default_alignment<T, Size>::value>
+          int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
-struct plain_array {
+                        : (((Size*sizeof(T))%16)==0) ? 16
-  EIGEN_ALIGN_TO_BOUNDARY(Alignment) T array[Size];
+                        : 0 >
-#if defined(EIGEN_NO_DEBUG) || defined(EIGEN_TESTING_PLAINOBJECT_CTOR)
+struct plain_array
-  EIGEN_DEVICE_FUNC constexpr plain_array() = default;
+{
-#else
+  T array[Size];
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr plain_array() {
+  plain_array() {}
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)
+  plain_array(constructor_without_unaligned_array_assert) {}
    EIGEN_MAKE_STACK_ALLOCATION_ASSERT(Size * sizeof(T))
  }
 #endif
 };
 #ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
 #else
  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
    eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
              && "this assertion is explained here: " \
              "http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
              " **** READ THIS WEB PAGE !!! ****");
 #endif
 template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 0> {
+struct plain_array<T, Size, MatrixOrArrayOptions, 16>
-  // on some 32-bit platforms, stack-allocated arrays are aligned to 4 bytes, not the preferred alignment of T
+{
-  EIGEN_ALIGN_TO_BOUNDARY(alignof(T)) T array[Size];
+  EIGEN_USER_ALIGN16 T array[Size];
-#if defined(EIGEN_NO_DEBUG) || defined(EIGEN_TESTING_PLAINOBJECT_CTOR)
+  plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
-  EIGEN_DEVICE_FUNC constexpr plain_array() = default;
+  plain_array(constructor_without_unaligned_array_assert) {}
 #else
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr plain_array() { EIGEN_MAKE_STACK_ALLOCATION_ASSERT(Size * sizeof(T)) }
 #endif
 };
-template <typename T, int Size, int Options, int Alignment>
+template <typename T, int MatrixOrArrayOptions, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap_plain_array(plain_array<T, Size, Options, Alignment>& a,
+struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
-                                                                      plain_array<T, Size, Options, Alignment>& b,
+{
-                                                                      Index a_size, Index b_size) {
+  EIGEN_USER_ALIGN16 T array[1];
-  Index common_size = numext::mini(a_size, b_size);
+  plain_array() {}
-  std::swap_ranges(a.array, a.array + common_size, b.array);
+  plain_array(constructor_without_unaligned_array_assert) {}
-  if (a_size > b_size)
+};
    smart_copy(a.array + common_size, a.array + a_size, b.array + common_size);
  else if (b_size > a_size)
    smart_copy(b.array + common_size, b.array + b_size, a.array + common_size);
 }
 template <typename T, int Size, int Rows, int Cols, int Options>
 class DenseStorage_impl {
  plain_array<T, Size, Options> m_data;
 public:
 #ifndef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
 #else
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl() {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
    smart_copy(other.m_data.array, other.m_data.array + Size, m_data.array);
  }
 #endif
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
    numext::swap(m_data, other.m_data);
  }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * Cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
 };
 template <typename T, int Size, int Cols, int Options>
 class DenseStorage_impl<T, Size, Dynamic, Cols, Options> {
  plain_array<T, Size, Options> m_data;
  Index m_rows = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_rows(other.m_rows) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index /*cols*/)
      : m_rows(rows) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    EIGEN_UNUSED_VARIABLE(size);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
    m_rows = other.m_rows;
    return *this;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
    swap_plain_array(m_data, other.m_data, size(), other.size());
    numext::swap(m_rows, other.m_rows);
  }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
 };
 template <typename T, int Size, int Rows, int Options>
 class DenseStorage_impl<T, Size, Rows, Dynamic, Options> {
  plain_array<T, Size, Options> m_data;
  Index m_cols = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_cols(other.m_cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index /*rows*/, Index cols)
      : m_cols(cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    EIGEN_UNUSED_VARIABLE(size);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
    m_cols = other.m_cols;
    return *this;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
    swap_plain_array(m_data, other.m_data, size(), other.size());
    numext::swap(m_cols, other.m_cols);
  }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
 };
 template <typename T, int Size, int Options>
 class DenseStorage_impl<T, Size, Dynamic, Dynamic, Options> {
  plain_array<T, Size, Options> m_data;
  Index m_rows = 0;
  Index m_cols = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_rows(other.m_rows), m_cols(other.m_cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index cols)
      : m_rows(rows), m_cols(cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    EIGEN_UNUSED_VARIABLE(size);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
    m_rows = other.m_rows;
    m_cols = other.m_cols;
    return *this;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
    swap_plain_array(m_data, other.m_data, size(), other.size());
    numext::swap(m_rows, other.m_rows);
    numext::swap(m_cols, other.m_cols);
  }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index cols) {
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index cols) {
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
 };
 // null matrix variants
 template <typename T, int Rows, int Cols, int Options>
 class DenseStorage_impl<T, 0, Rows, Cols, Options> {
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl&) {}
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * Cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
 };
 template <typename T, int Cols, int Options>
 class DenseStorage_impl<T, 0, Dynamic, Cols, Options> {
  Index m_rows = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index rows, Index /*cols*/) : m_rows(rows) {}
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept { numext::swap(m_rows, other.m_rows); }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
 };
 template <typename T, int Rows, int Options>
 class DenseStorage_impl<T, 0, Rows, Dynamic, Options> {
  Index m_cols = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index cols) : m_cols(cols) {}
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept { numext::swap(m_cols, other.m_cols); }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
 };
 template <typename T, int Options>
 class DenseStorage_impl<T, 0, Dynamic, Dynamic, Options> {
  Index m_rows = 0;
  Index m_cols = 0;
 public:
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
    numext::swap(m_rows, other.m_rows);
    numext::swap(m_cols, other.m_cols);
  }
  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index cols) {
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index cols) {
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
 };
 // fixed-size matrix with dynamic memory allocation not currently supported
 template <typename T, int Rows, int Cols, int Options>
 class DenseStorage_impl<T, Dynamic, Rows, Cols, Options> {};
 // dynamic-sized variants
 template <typename T, int Cols, int Options>
 class DenseStorage_impl<T, Dynamic, Dynamic, Cols, Options> {
  static constexpr bool Align = (Options & DontAlign) == 0;
  T* m_data = nullptr;
  Index m_rows = 0;
 public:
  static constexpr int Size = Dynamic;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_rows(other.m_rows) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index /*cols*/)
      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_rows(rows) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
      : m_data(other.m_data), m_rows(other.m_rows) {
    other.m_data = nullptr;
    other.m_rows = 0;
  }
  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    resize(other.size(), other.rows(), other.cols());
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
    this->swap(other);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
    numext::swap(m_data, other.m_data);
    numext::swap(m_rows, other.m_rows);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index rows, Index /*cols*/) {
    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
    m_rows = rows;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index rows, Index /*cols*/) {
    Index oldSize = this->size();
    if (oldSize != size) {
      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
      m_data = conditional_aligned_new_auto<T, Align>(size);
      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    }
    m_rows = rows;
  }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
 };
 template <typename T, int Rows, int Options>
 class DenseStorage_impl<T, Dynamic, Rows, Dynamic, Options> {
  static constexpr bool Align = (Options & DontAlign) == 0;
  T* m_data = nullptr;
  Index m_cols = 0;
 public:
  static constexpr int Size = Dynamic;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_cols(other.m_cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index /*rows*/, Index cols)
      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_cols(cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
      : m_data(other.m_data), m_cols(other.m_cols) {
    other.m_data = nullptr;
    other.m_cols = 0;
  }
  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    resize(other.size(), other.rows(), other.cols());
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
    this->swap(other);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
    numext::swap(m_data, other.m_data);
    numext::swap(m_cols, other.m_cols);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index /*rows*/, Index cols) {
    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index /*rows*/, Index cols) {
    Index oldSize = this->size();
    if (oldSize != size) {
      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
      m_data = conditional_aligned_new_auto<T, Align>(size);
      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    }
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
 };
 template <typename T, int Options>
 class DenseStorage_impl<T, Dynamic, Dynamic, Dynamic, Options> {
  static constexpr bool Align = (Options & DontAlign) == 0;
  T* m_data = nullptr;
  Index m_rows = 0;
  Index m_cols = 0;
 public:
  static constexpr int Size = Dynamic;
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_rows(other.m_rows), m_cols(other.m_cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index cols)
      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_rows(rows), m_cols(cols) {
    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
      : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {
    other.m_data = nullptr;
    other.m_rows = 0;
    other.m_cols = 0;
  }
  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
    resize(other.size(), other.rows(), other.cols());
    smart_copy(other.m_data, other.m_data + other.size(), m_data);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
    this->swap(other);
    return *this;
  }
  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
    numext::swap(m_data, other.m_data);
    numext::swap(m_rows, other.m_rows);
    numext::swap(m_cols, other.m_cols);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index rows, Index cols) {
    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index rows, Index cols) {
    Index oldSize = this->size();
    if (oldSize != size) {
      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
      m_data = conditional_aligned_new_auto<T, Align>(size);
      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
    }
    m_rows = rows;
    m_cols = cols;
  }
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
 };
 template <typename T, int Size, int Rows, int Cols>
 struct use_default_move {
  static constexpr bool DynamicObject = Size == Dynamic;
  static constexpr bool TrivialObject =
      (!NumTraits<T>::RequireInitialization) && (Rows >= 0) && (Cols >= 0) && (Size == Rows * Cols);
  static constexpr bool value = DynamicObject || TrivialObject;
 };
 } // end namespace internal
 /** \internal
  *
- * \class DenseStorage_impl
+  * \class DenseStorage
  * \ingroup Core_Module
  *
  * \brief Stores the data of a matrix
@@ -506,41 +92,213 @@ struct use_default_move {
  *
  * \sa Matrix
  */
-template <typename T, int Size, int Rows, int Cols, int Options,
+template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
          bool Trivial = internal::use_default_move<T, Size, Rows, Cols>::value>
 class DenseStorage : public internal::DenseStorage_impl<T, Size, Rows, Cols, Options> {
  using Base = internal::DenseStorage_impl<T, Size, Rows, Cols, Options>;
 // purely fixed-size matrix
 template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
 {
    internal::plain_array<T,Size,_Options> m_data;
  public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() = default;
+    inline explicit DenseStorage() {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) : Base(size, rows, cols) {}
+      : m_data(internal::constructor_without_unaligned_array_assert()) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
+    inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
-  // if DenseStorage meets the requirements of use_default_move, then use the move construction and move assignment
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
-  // operation defined in DenseStorage_impl, or the compiler-generated version if none is defined
+    inline static DenseIndex rows(void) {return _Rows;}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&&) = default;
+    inline static DenseIndex cols(void) {return _Cols;}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&&) = default;
+    inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
    inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
    inline const T *data() const { return m_data.array; }
    inline T *data() { return m_data.array; }
 };
 template <typename T, int Size, int Rows, int Cols, int Options>
 class DenseStorage<T, Size, Rows, Cols, Options, false>
    : public internal::DenseStorage_impl<T, Size, Rows, Cols, Options> {
  using Base = internal::DenseStorage_impl<T, Size, Rows, Cols, Options>;
 // null matrix
 template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
 {
  public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() = default;
+    inline explicit DenseStorage() {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) : Base(size, rows, cols) {}
+    inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
+    inline void swap(DenseStorage& ) {}
-  // if DenseStorage does not meet the requirements of use_default_move, then defer to the copy construction and copy
+    inline static DenseIndex rows(void) {return _Rows;}
-  // assignment behavior
+    inline static DenseIndex cols(void) {return _Cols;}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&& other)
+    inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
-      : DenseStorage(static_cast<const DenseStorage&>(other)) {}
+    inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&& other) {
+    inline const T *data() const { return 0; }
-    *this = other;
+    inline T *data() { return 0; }
-    return *this;
+};
 // dynamic-size matrix with fixed-size storage
 template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
 {
    internal::plain_array<T,Size,_Options> m_data;
    DenseIndex m_rows;
    DenseIndex m_cols;
  public:
    inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
    inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
    inline void swap(DenseStorage& other)
    { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
    inline DenseIndex rows(void) const {return m_rows;}
    inline DenseIndex cols(void) const {return m_cols;}
    inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
    inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
    inline const T *data() const { return m_data.array; }
    inline T *data() { return m_data.array; }
 };
 // dynamic-size matrix with fixed-size storage and fixed width
 template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
 {
    internal::plain_array<T,Size,_Options> m_data;
    DenseIndex m_rows;
  public:
    inline explicit DenseStorage() : m_rows(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
    inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
    inline DenseIndex rows(void) const {return m_rows;}
    inline DenseIndex cols(void) const {return _Cols;}
    inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
    inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
    inline const T *data() const { return m_data.array; }
    inline T *data() { return m_data.array; }
 };
 // dynamic-size matrix with fixed-size storage and fixed height
 template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
 {
    internal::plain_array<T,Size,_Options> m_data;
    DenseIndex m_cols;
  public:
    inline explicit DenseStorage() : m_cols(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
    inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
    inline DenseIndex rows(void) const {return _Rows;}
    inline DenseIndex cols(void) const {return m_cols;}
    inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
    inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
    inline const T *data() const { return m_data.array; }
    inline T *data() { return m_data.array; }
 };
 // purely dynamic matrix.
 template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
 {
    T *m_data;
    DenseIndex m_rows;
    DenseIndex m_cols;
  public:
    inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
       : m_data(0), m_rows(0), m_cols(0) {}
    inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols) 
    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
    inline void swap(DenseStorage& other)
    { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
    inline DenseIndex rows(void) const {return m_rows;}
    inline DenseIndex cols(void) const {return m_cols;}
    inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
    {
      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
      m_rows = rows;
      m_cols = cols;
    }
    void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
    {
      if(size != m_rows*m_cols)
      {
        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
        if (size)
          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
        else
          m_data = 0;
        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
      }
      m_rows = rows;
      m_cols = cols;
    }
    inline const T *data() const { return m_data; }
    inline T *data() { return m_data; }
 };
-}  // end namespace Eigen
+// matrix with dynamic width and fixed height (so that matrix has dynamic size).
 template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
 {
    T *m_data;
    DenseIndex m_cols;
  public:
    inline explicit DenseStorage() : m_data(0), m_cols(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
    inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
    inline static DenseIndex rows(void) {return _Rows;}
    inline DenseIndex cols(void) const {return m_cols;}
    inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
    {
      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
      m_cols = cols;
    }
    EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
    {
      if(size != _Rows*m_cols)
      {
        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
        if (size)
          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
        else
          m_data = 0;
        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
      }
      m_cols = cols;
    }
    inline const T *data() const { return m_data; }
    inline T *data() { return m_data; }
 };
 // matrix with dynamic height and fixed width (so that matrix has dynamic size).
 template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
 {
    T *m_data;
    DenseIndex m_rows;
  public:
    inline explicit DenseStorage() : m_data(0), m_rows(0) {}
    inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
    inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
    inline DenseIndex rows(void) const {return m_rows;}
    inline static DenseIndex cols(void) {return _Cols;}
    inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
    {
      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
      m_rows = rows;
    }
    EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
    {
      if(size != m_rows*_Cols)
      {
        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
        if (size)
          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
        else
          m_data = 0;
        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
      }
      m_rows = rows;
    }
    inline const T *data() const { return m_data; }
    inline T *data() { return m_data; }
 };
 #endif // EIGEN_MATRIX_H
--- a/Eigen/src/Core/DeviceWrapper.h
+++ b/Eigen/src/Core/DeviceWrapper.h
@@ -1,153 +0,0 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2023 Charlie Schlosser <cs.schlosser@gmail.com>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 #ifndef EIGEN_DEVICEWRAPPER_H
 #define EIGEN_DEVICEWRAPPER_H
 namespace Eigen {
 template <typename Derived, typename Device>
 struct DeviceWrapper {
  using Base = EigenBase<internal::remove_all_t<Derived>>;
  using Scalar = typename Derived::Scalar;
  EIGEN_DEVICE_FUNC DeviceWrapper(Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
  EIGEN_DEVICE_FUNC DeviceWrapper(const Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived>& other) {
    using AssignOp = internal::assign_op<Scalar, typename OtherDerived::Scalar>;
    internal::call_assignment(*this, other.derived(), AssignOp());
    return m_xpr;
  }
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const EigenBase<OtherDerived>& other) {
    using AddAssignOp = internal::add_assign_op<Scalar, typename OtherDerived::Scalar>;
    internal::call_assignment(*this, other.derived(), AddAssignOp());
    return m_xpr;
  }
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const EigenBase<OtherDerived>& other) {
    using SubAssignOp = internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>;
    internal::call_assignment(*this, other.derived(), SubAssignOp());
    return m_xpr;
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& derived() { return m_xpr; }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Device& device() { return m_device; }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NoAlias<DeviceWrapper, EigenBase> noalias() {
    return NoAlias<DeviceWrapper, EigenBase>(*this);
  }
  Derived& m_xpr;
  Device& m_device;
 };
 namespace internal {
 // this is where we differentiate between lazy assignment and specialized kernels (e.g. matrix products)
 template <typename DstXprType, typename SrcXprType, typename Functor, typename Device,
          typename Kind = typename AssignmentKind<typename evaluator_traits<DstXprType>::Shape,
                                                  typename evaluator_traits<SrcXprType>::Shape>::Kind,
          typename EnableIf = void>
 struct AssignmentWithDevice;
 // unless otherwise specified, use the default product implementation
 template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Functor, typename Device,
          typename Weak>
 struct AssignmentWithDevice<DstXprType, Product<Lhs, Rhs, Options>, Functor, Device, Dense2Dense, Weak> {
  using SrcXprType = Product<Lhs, Rhs, Options>;
  using Base = Assignment<DstXprType, SrcXprType, Functor>;
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
                                                        Device&) {
    Base::run(dst, src, func);
  }
 };
 // specialization for coeffcient-wise assignment
 template <typename DstXprType, typename SrcXprType, typename Functor, typename Device, typename Weak>
 struct AssignmentWithDevice<DstXprType, SrcXprType, Functor, Device, Dense2Dense, Weak> {
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
                                                        Device& device) {
 #ifndef EIGEN_NO_DEBUG
    internal::check_for_aliasing(dst, src);
 #endif
    call_dense_assignment_loop(dst, src, func, device);
  }
 };
 // this allows us to use the default evaluation scheme if it is not specialized for the device
 template <typename Kernel, typename Device, int Traversal = Kernel::AssignmentTraits::Traversal,
          int Unrolling = Kernel::AssignmentTraits::Unrolling>
 struct dense_assignment_loop_with_device {
  using Base = dense_assignment_loop<Kernel, Traversal, Unrolling>;
  static EIGEN_DEVICE_FUNC constexpr void run(Kernel& kernel, Device&) { Base::run(kernel); }
 };
 // entry point for a generic expression with device
 template <typename Dst, typename Src, typename Func, typename Device>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias(DeviceWrapper<Dst, Device> dst,
                                                                              const Src& src, const Func& func) {
  enum {
    NeedToTranspose = ((int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) ||
                       (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)) &&
                      int(Dst::SizeAtCompileTime) != 1
  };
  using ActualDstTypeCleaned = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst>;
  using ActualDstType = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&>;
  ActualDstType actualDst(dst.derived());
  // TODO: check whether this is the right place to perform these checks:
  EIGEN_STATIC_ASSERT_LVALUE(Dst)
  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned, Src)
  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename ActualDstTypeCleaned::Scalar, typename Src::Scalar);
  // this provides a mechanism for specializing simple assignments, matrix products, etc
  AssignmentWithDevice<ActualDstTypeCleaned, Src, Func, Device>::run(actualDst, src, func, dst.device());
 }
 // copy and pasted from AssignEvaluator except forward device to kernel
 template <typename DstXprType, typename SrcXprType, typename Functor, typename Device>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src,
                                                                                const Functor& func, Device& device) {
  using DstEvaluatorType = evaluator<DstXprType>;
  using SrcEvaluatorType = evaluator<SrcXprType>;
  SrcEvaluatorType srcEvaluator(src);
  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
  // we need to resize the destination after the source evaluator has been created.
  resize_if_allowed(dst, src, func);
  DstEvaluatorType dstEvaluator(dst);
  using Kernel = generic_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Functor>;
  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
  dense_assignment_loop_with_device<Kernel, Device>::run(kernel, device);
 }
 }  // namespace internal
 template <typename Derived>
 template <typename Device>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> EigenBase<Derived>::device(Device& device) {
  return DeviceWrapper<Derived, Device>(derived(), device);
 }
 template <typename Derived>
 template <typename Device>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> EigenBase<Derived>::device(
    Device& device) const {
  return DeviceWrapper<const Derived, Device>(derived(), device);
 }
 }  // namespace Eigen
 #endif
--- a/Eigen/src/Core/Diagonal.h
+++ b/Eigen/src/Core/Diagonal.h
@@ -2,29 +2,38 @@
 // for linear algebra.
 //
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DIAGONAL_H
 #define EIGEN_DIAGONAL_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 /** \class Diagonal
  * \ingroup Core_Module
  *
  * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
  *
- * \tparam MatrixType the type of the object in which we are taking a sub/main/super diagonal
+  * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
- * \tparam DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
+  * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
  *              A positive value means a superdiagonal, a negative value means a subdiagonal.
- *              You can also use DynamicIndex so the index can be set at runtime.
+  *              You can also use Dynamic so the index can be set at runtime.
  *
  * The matrix is not required to be square.
  *
@@ -37,116 +46,105 @@ namespace Eigen {
 namespace internal {
 template<typename MatrixType, int DiagIndex>
-struct traits<Diagonal<MatrixType, DiagIndex> > : traits<MatrixType> {
+struct traits<Diagonal<MatrixType,DiagIndex> >
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+ : traits<MatrixType>
-  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+{
  typedef typename nested<MatrixType>::type MatrixTypeNested;
  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
  typedef typename MatrixType::StorageKind StorageKind;
  enum {
-    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic)
+    AbsDiagIndex = DiagIndex<0 ? -DiagIndex : DiagIndex, // only used if DiagIndex != Dynamic
-                            ? Dynamic
+    // FIXME these computations are broken in the case where the matrix is rectangular and DiagIndex!=0
-                            : (plain_enum_min(MatrixType::RowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
+    RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
-                                              MatrixType::ColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
+                      : (EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime,
                                        MatrixType::ColsAtCompileTime) - AbsDiagIndex),
    ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime =
+    MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
-        int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
+                         : DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
-        : DiagIndex == DynamicIndex
+                                                                    MatrixType::MaxColsAtCompileTime)
-            ? min_size_prefer_fixed(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime)
+                         : (EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) - AbsDiagIndex),
            : (plain_enum_min(MatrixType::MaxRowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
                              MatrixType::MaxColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
    MaxColsAtCompileTime = 1,
    MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (unsigned int)MatrixTypeNested_::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) &
+    Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
-            ~RowMajorBit,  // FIXME DirectAccessBit should not be handled by expressions
+    CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
    MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
    OuterStrideAtCompileTime = 0
  };
 };
-}  // namespace internal
+}
-template <typename MatrixType, int DiagIndex_>
+template<typename MatrixType, int DiagIndex> class Diagonal
-class Diagonal : public internal::dense_xpr_base<Diagonal<MatrixType, DiagIndex_> >::type {
+   : public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
 {
  public:
-  enum { DiagIndex = DiagIndex_ };
+
    typedef typename internal::dense_xpr_base<Diagonal>::type Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
-  EIGEN_DEVICE_FUNC constexpr explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex)
+    inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
      : m_matrix(matrix), m_index(a_index) {
    eigen_assert(a_index <= m_matrix.cols() && -a_index <= m_matrix.rows());
  }
    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
-  EIGEN_DEVICE_FUNC constexpr inline Index rows() const {
+    inline Index rows() const
-    return m_index.value() < 0 ? numext::mini<Index>(m_matrix.cols(), m_matrix.rows() + m_index.value())
+    { return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
-                               : numext::mini<Index>(m_matrix.rows(), m_matrix.cols() - m_index.value());
+
    inline Index cols() const { return 1; }
    inline Index innerStride() const
    {
      return m_matrix.outerStride() + 1;
    }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return 1; }
+    inline Index outerStride() const
-
+    {
-  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_matrix.outerStride() + 1; }
+      return 0;
  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return 0; }
  typedef std::conditional_t<internal::is_lvalue<MatrixType>::value, Scalar, const Scalar> ScalarWithConstIfNotLvalue;
  EIGEN_DEVICE_FUNC inline ScalarWithConstIfNotLvalue* data() {
    return rows() > 0 ? &(m_matrix.coeffRef(rowOffset(), colOffset())) : nullptr;
  }
  EIGEN_DEVICE_FUNC inline const Scalar* data() const {
    return rows() > 0 ? &(m_matrix.coeffRef(rowOffset(), colOffset())) : nullptr;
    }
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index) {
+    inline Scalar& coeffRef(Index row, Index)
    {
      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
+      return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index) const {
+    inline const Scalar& coeffRef(Index row, Index) const
-    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
+    {
      return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
    }
-  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index row, Index) const {
+    inline CoeffReturnType coeff(Index row, Index) const
    {
      return m_matrix.coeff(row+rowOffset(), row+colOffset());
    }
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index idx) {
+    inline Scalar& coeffRef(Index index)
    {
      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
+      return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
    }
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index idx) const {
+    inline const Scalar& coeffRef(Index index) const
-    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
+    {
      return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
    }
-  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index idx) const {
+    inline CoeffReturnType coeff(Index index) const
-    return m_matrix.coeff(idx + rowOffset(), idx + colOffset());
+    {
      return m_matrix.coeff(index+rowOffset(), index+colOffset());
    }
  EIGEN_DEVICE_FUNC constexpr inline const internal::remove_all_t<typename MatrixType::Nested>& nestedExpression()
      const {
    return m_matrix;
  }
  EIGEN_DEVICE_FUNC constexpr inline Index index() const { return m_index.value(); }
  protected:
-  typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
+    const typename MatrixType::Nested m_matrix;
-  const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
+    const internal::variable_if_dynamic<Index, DiagIndex> m_index;
  private:
    // some compilers may fail to optimize std::max etc in case of compile-time constants...
-  EIGEN_DEVICE_FUNC constexpr Index absDiagIndex() const noexcept {
+    EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
-    return m_index.value() > 0 ? m_index.value() : -m_index.value();
+    EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
-  }
+    EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; }
-  EIGEN_DEVICE_FUNC constexpr Index rowOffset() const noexcept { return m_index.value() > 0 ? 0 : -m_index.value(); }
+    // triger a compile time error is someone try to call packet
-  EIGEN_DEVICE_FUNC constexpr Index colOffset() const noexcept { return m_index.value() > 0 ? m_index.value() : 0; }
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
-  // trigger a compile-time error if someone try to call packet
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
  template <int LoadMode>
  typename MatrixType::PacketReturnType packet(Index) const;
  template <int LoadMode>
  typename MatrixType::PacketReturnType packet(Index, Index) const;
 };
 /** \returns an expression of the main diagonal of the matrix \c *this
@@ -158,14 +156,17 @@ class Diagonal : public internal::dense_xpr_base<Diagonal<MatrixType, DiagIndex_
  *
  * \sa class Diagonal */
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::DiagonalReturnType MatrixBase<Derived>::diagonal() {
+inline typename MatrixBase<Derived>::DiagonalReturnType
-  return DiagonalReturnType(derived());
+MatrixBase<Derived>::diagonal()
 {
  return derived();
 }
 /** This is the const version of diagonal(). */
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr const typename MatrixBase<Derived>::ConstDiagonalReturnType MatrixBase<Derived>::diagonal()
+inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
-    const {
+MatrixBase<Derived>::diagonal() const
 {
  return ConstDiagonalReturnType(derived());
 }
@@ -181,15 +182,18 @@ EIGEN_DEVICE_FUNC constexpr const typename MatrixBase<Derived>::ConstDiagonalRet
  *
  * \sa MatrixBase::diagonal(), class Diagonal */
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, DynamicIndex> MatrixBase<Derived>::diagonal(Index index) {
+inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
-  return Diagonal<Derived, DynamicIndex>(derived(), index);
+MatrixBase<Derived>::diagonal(Index index)
 {
  return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
 }
 /** This is the const version of diagonal(Index). */
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, DynamicIndex> MatrixBase<Derived>::diagonal(
+inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
-    Index index) const {
+MatrixBase<Derived>::diagonal(Index index) const
-  return Diagonal<const Derived, DynamicIndex>(derived(), index);
+{
  return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
 }
 /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -204,18 +208,20 @@ EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, DynamicIndex> MatrixBa
  *
  * \sa MatrixBase::diagonal(), class Diagonal */
 template<typename Derived>
-template <int Index_>
+template<int Index>
-EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, Index_> MatrixBase<Derived>::diagonal() {
+inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
-  return Diagonal<Derived, Index_>(derived());
+MatrixBase<Derived>::diagonal()
 {
  return derived();
 }
 /** This is the const version of diagonal<int>(). */
 template<typename Derived>
-template <int Index_>
+template<int Index>
-EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, Index_> MatrixBase<Derived>::diagonal() const {
+inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
-  return Diagonal<const Derived, Index_>(derived());
+MatrixBase<Derived>::diagonal() const
 {
  return derived();
 }
 }  // end namespace Eigen
 #endif // EIGEN_DIAGONAL_H
--- a/Eigen/src/Core/DiagonalMatrix.h
+++ b/Eigen/src/Core/DiagonalMatrix.h
@@ -4,39 +4,37 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DIAGONALMATRIX_H
 #define EIGEN_DIAGONALMATRIX_H
-// IWYU pragma: private
+#ifndef EIGEN_PARSED_BY_DOXYGEN
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 /** \class DiagonalBase
 * \ingroup Core_Module
 *
 * \brief Base class for diagonal matrices and expressions
 *
 * This is the base class that is inherited by diagonal matrix and related expression
 * types, which internally use a vector for storing the diagonal entries. Diagonal
 * types always represent square matrices.
 *
 * \tparam Derived is the derived type, a DiagonalMatrix or DiagonalWrapper.
 *
 * \sa class DiagonalMatrix, class DiagonalWrapper
 */
 template<typename Derived>
-class DiagonalBase : public EigenBase<Derived> {
+class DiagonalBase : public EigenBase<Derived>
 {
  public:
    typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
    typedef typename DiagonalVectorType::Scalar Scalar;
  typedef typename DiagonalVectorType::RealScalar RealScalar;
    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+    typedef typename internal::traits<Derived>::Index Index;
    enum {
      RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
@@ -44,190 +42,130 @@ class DiagonalBase : public EigenBase<Derived> {
      MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
      MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
      IsVectorAtCompileTime = 0,
-    Flags = NoPreferredStorageOrderBit
+      Flags = 0
    };
-  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime>
+    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
      DenseMatrixType;
    typedef DenseMatrixType DenseType;
-  typedef DiagonalMatrix<Scalar, DiagonalVectorType::SizeAtCompileTime, DiagonalVectorType::MaxSizeAtCompileTime>
+    typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
      PlainObject;
-  /** \returns a reference to the derived object. */
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
  /** \returns a const reference to the derived object. */
  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
-  /**
+    DenseMatrixType toDenseMatrix() const { return derived(); }
-   * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
+    template<typename DenseDerived>
-   * not an expression.
+    void evalTo(MatrixBase<DenseDerived> &other) const;
-   * \returns A dense matrix, with its diagonal entries set from the derived object. */
+    template<typename DenseDerived>
-  EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
+    void addTo(MatrixBase<DenseDerived> &other) const
    { other.diagonal() += diagonal(); }
    template<typename DenseDerived>
    void subTo(MatrixBase<DenseDerived> &other) const
    { other.diagonal() -= diagonal(); }
-  /** \returns a reference to the derived object's vector of diagonal coefficients. */
+    inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
-  EIGEN_DEVICE_FUNC inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
+    inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
  /** \returns a const reference to the derived object's vector of diagonal coefficients. */
  EIGEN_DEVICE_FUNC inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
-  /** \returns the value of the coefficient as if \c *this was a dense matrix. */
+    inline Index rows() const { return diagonal().size(); }
-  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const {
+    inline Index cols() const { return diagonal().size(); }
    eigen_assert(row >= 0 && col >= 0 && row < rows() && col <= cols());
    return row == col ? diagonal().coeff(row) : Scalar(0);
  }
  /** \returns the number of rows. */
  EIGEN_DEVICE_FUNC constexpr Index rows() const { return diagonal().size(); }
  /** \returns the number of columns. */
  EIGEN_DEVICE_FUNC constexpr Index cols() const { return diagonal().size(); }
  /** \returns the diagonal matrix product of \c *this by the dense matrix, \a matrix */
    template<typename MatrixDerived>
-  EIGEN_DEVICE_FUNC const Product<Derived, MatrixDerived, LazyProduct> operator*(
+    const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
-      const MatrixBase<MatrixDerived>& matrix) const {
+    operator*(const MatrixBase<MatrixDerived> &matrix) const;
-    return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+
    inline const DiagonalWrapper<CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
    inverse() const
    {
      return diagonal().cwiseInverse();
    }
    #ifdef EIGEN2_SUPPORT
    template<typename OtherDerived>
-  using DiagonalProductReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+    bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
-      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, product)>;
+    {
-
+      return diagonal().isApprox(other.diagonal(), precision);
-  /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a other */
+    }
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC const DiagonalProductReturnType<OtherDerived> operator*(
+    bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
-      const DiagonalBase<OtherDerived>& other) const {
+    {
-    return diagonal().cwiseProduct(other.diagonal()).asDiagonal();
+      return toDenseMatrix().isApprox(other, precision);
  }
  using DiagonalInverseReturnType =
      DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType>>;
  /** \returns the inverse \c *this. Computed as the coefficient-wise inverse of the diagonal. */
  EIGEN_DEVICE_FUNC inline const DiagonalInverseReturnType inverse() const {
    return diagonal().cwiseInverse().asDiagonal();
  }
  using DiagonalScaleReturnType =
      DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType, Scalar, product)>;
  /** \returns the product of \c *this by the scalar \a scalar */
  EIGEN_DEVICE_FUNC inline const DiagonalScaleReturnType operator*(const Scalar& scalar) const {
    return (diagonal() * scalar).asDiagonal();
  }
  using ScaleDiagonalReturnType =
      DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, DiagonalVectorType, product)>;
  /** \returns the product of a scalar and the diagonal matrix \a other */
  EIGEN_DEVICE_FUNC friend inline const ScaleDiagonalReturnType operator*(const Scalar& scalar,
                                                                          const DiagonalBase& other) {
    return (scalar * other.diagonal()).asDiagonal();
  }
  template <typename OtherDerived>
  using DiagonalSumReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, sum)>;
  /** \returns the sum of \c *this and the diagonal matrix \a other */
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC inline const DiagonalSumReturnType<OtherDerived> operator+(
      const DiagonalBase<OtherDerived>& other) const {
    return (diagonal() + other.diagonal()).asDiagonal();
  }
  template <typename OtherDerived>
  using DiagonalDifferenceReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, difference)>;
  /** \returns the difference of \c *this and the diagonal matrix \a other */
  template <typename OtherDerived>
  EIGEN_DEVICE_FUNC inline const DiagonalDifferenceReturnType<OtherDerived> operator-(
      const DiagonalBase<OtherDerived>& other) const {
    return (diagonal() - other.diagonal()).asDiagonal();
    }
    #endif
 };
 template<typename Derived>
 template<typename DenseDerived>
 void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
 {
  other.setZero();
  other.diagonal() = diagonal();
 }
 #endif
 /** \class DiagonalMatrix
  * \ingroup Core_Module
  *
  * \brief Represents a diagonal matrix with its storage
  *
- * \tparam Scalar_ the type of coefficients
+  * \param _Scalar the type of coefficients
- * \tparam SizeAtCompileTime the dimension of the matrix, or Dynamic
+  * \param SizeAtCompileTime the dimension of the matrix, or Dynamic
- * \tparam MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
+  * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
  *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
  *
- * \sa class DiagonalBase, class DiagonalWrapper
+  * \sa class DiagonalWrapper
  */
 namespace internal {
-template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-struct traits<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>>
+struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
-    : traits<Matrix<Scalar_, SizeAtCompileTime, SizeAtCompileTime, 0, MaxSizeAtCompileTime, MaxSizeAtCompileTime>> {
+ : traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-  typedef Matrix<Scalar_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> DiagonalVectorType;
+{
-  typedef DiagonalShape StorageKind;
+  typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
-  enum { Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit };
+  typedef Dense StorageKind;
  typedef DenseIndex Index;
  enum {
    Flags = LvalueBit
  };
-}  // namespace internal
+};
-template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+}
-class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>> {
+template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
 class DiagonalMatrix
  : public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
 {
  public:
    #ifndef EIGEN_PARSED_BY_DOXYGEN
    typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
    typedef const DiagonalMatrix& Nested;
-  typedef Scalar_ Scalar;
+    typedef _Scalar Scalar;
    typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
-  typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
+    typedef typename internal::traits<DiagonalMatrix>::Index Index;
    #endif
  protected:
    DiagonalVectorType m_diagonal;
  public:
    /** const version of diagonal(). */
-  EIGEN_DEVICE_FUNC constexpr inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
+    inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
    /** \returns a reference to the stored vector of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC constexpr inline DiagonalVectorType& diagonal() { return m_diagonal; }
+    inline DiagonalVectorType& diagonal() { return m_diagonal; }
    /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix() {}
+    inline DiagonalMatrix() {}
    /** Constructs a diagonal matrix with given dimension  */
-  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
+    inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
    /** 2D constructor. */
-  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x, y) {}
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
    /** 3D constructor. */
-  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z)
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
      : m_diagonal(x, y, z) {}
  /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients.
   *
   * \warning To construct a diagonal matrix of fixed size, the number of values passed to this
   * constructor must match the fixed dimension of \c *this.
   *
   * \sa DiagonalMatrix(const Scalar&, const Scalar&)
   * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
   */
  template <typename... ArgTypes>
  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,
                                                                 const ArgTypes&... args)
      : m_diagonal(a0, a1, a2, args...) {}
  /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
   * lists \cpp11
   */
  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE DiagonalMatrix(
      const std::initializer_list<std::initializer_list<Scalar>>& list)
      : m_diagonal(list) {}
  /** \brief Constructs a DiagonalMatrix from an r-value diagonal vector type */
  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(DiagonalVectorType&& diag) : m_diagonal(std::move(diag)) {}
    /** Copy constructor. */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other)
+    inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
      : m_diagonal(other.diagonal()) {}
    #ifndef EIGEN_PARSED_BY_DOXYGEN
    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
@@ -236,12 +174,13 @@ class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompile
    /** generic constructor from expression of the diagonal coefficients */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other)
+    explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
-      : m_diagonal(other) {}
+    {}
    /** Copy operator. */
    template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other) {
+    DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
    {
      m_diagonal = other.diagonal();
      return *this;
    }
@@ -250,41 +189,23 @@ class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompile
    /** This is a special case of the templated operator=. Its purpose is to
      * prevent a default operator= from hiding the templated operator=.
      */
-  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalMatrix& other) {
+    DiagonalMatrix& operator=(const DiagonalMatrix& other)
    {
      m_diagonal = other.diagonal();
      return *this;
    }
    #endif
  typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, DiagonalVectorType>>
      InitializeReturnType;
  typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_zero_op<Scalar>, DiagonalVectorType>>
      ZeroInitializeReturnType;
  /** Initializes a diagonal matrix of size SizeAtCompileTime with coefficients set to zero */
  EIGEN_DEVICE_FUNC static const ZeroInitializeReturnType Zero() { return DiagonalVectorType::Zero().asDiagonal(); }
  /** Initializes a diagonal matrix of size dim with coefficients set to zero */
  EIGEN_DEVICE_FUNC static const ZeroInitializeReturnType Zero(Index size) {
    return DiagonalVectorType::Zero(size).asDiagonal();
  }
  /** Initializes a identity matrix of size SizeAtCompileTime */
  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity() { return DiagonalVectorType::Ones().asDiagonal(); }
  /** Initializes a identity matrix of size dim */
  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity(Index size) {
    return DiagonalVectorType::Ones(size).asDiagonal();
  }
    /** Resizes to given size. */
-  EIGEN_DEVICE_FUNC inline void resize(Index size) { m_diagonal.resize(size); }
+    inline void resize(Index size) { m_diagonal.resize(size); }
    /** Sets all coefficients to zero. */
-  EIGEN_DEVICE_FUNC inline void setZero() { m_diagonal.setZero(); }
+    inline void setZero() { m_diagonal.setZero(); }
    /** Resizes and sets all coefficients to zero. */
-  EIGEN_DEVICE_FUNC inline void setZero(Index size) { m_diagonal.setZero(size); }
+    inline void setZero(Index size) { m_diagonal.setZero(size); }
    /** Sets this matrix to be the identity matrix of the current size. */
-  EIGEN_DEVICE_FUNC inline void setIdentity() { m_diagonal.setOnes(); }
+    inline void setIdentity() { m_diagonal.setOnes(); }
    /** Sets this matrix to be the identity matrix of the given size. */
-  EIGEN_DEVICE_FUNC inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
+    inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
 };
 /** \class DiagonalWrapper
@@ -292,7 +213,7 @@ class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompile
  *
  * \brief Expression of a diagonal matrix
  *
- * \tparam DiagonalVectorType_ the type of the vector of diagonal coefficients
+  * \param _DiagonalVectorType the type of the vector of diagonal coefficients
  *
  * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
@@ -302,40 +223,41 @@ class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompile
  */
 namespace internal {
-template <typename DiagonalVectorType_>
+template<typename _DiagonalVectorType>
-struct traits<DiagonalWrapper<DiagonalVectorType_>> {
+struct traits<DiagonalWrapper<_DiagonalVectorType> >
-  typedef DiagonalVectorType_ DiagonalVectorType;
+{
  typedef _DiagonalVectorType DiagonalVectorType;
  typedef typename DiagonalVectorType::Scalar Scalar;
-  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
+  typedef typename DiagonalVectorType::Index Index;
-  typedef DiagonalShape StorageKind;
+  typedef typename DiagonalVectorType::StorageKind StorageKind;
  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
  enum {
    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+    Flags =  traits<DiagonalVectorType>::Flags & LvalueBit
  };
 };
-}  // namespace internal
+}
-template <typename DiagonalVectorType_>
+template<typename _DiagonalVectorType>
-class DiagonalWrapper : public DiagonalBase<DiagonalWrapper<DiagonalVectorType_>>, internal::no_assignment_operator {
+class DiagonalWrapper
  : public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
 {
  public:
    #ifndef EIGEN_PARSED_BY_DOXYGEN
-  typedef DiagonalVectorType_ DiagonalVectorType;
+    typedef _DiagonalVectorType DiagonalVectorType;
    typedef DiagonalWrapper Nested;
    #endif
    /** Constructor from expression of diagonal coefficients to wrap. */
-  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal)
+    inline DiagonalWrapper(const DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
      : m_diagonal(a_diagonal) {}
    /** \returns a const reference to the wrapped expression of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC constexpr const DiagonalVectorType& diagonal() const { return m_diagonal; }
+    const DiagonalVectorType& diagonal() const { return m_diagonal; }
  protected:
-  typename DiagonalVectorType::Nested m_diagonal;
+    const typename DiagonalVectorType::Nested m_diagonal;
 };
 /** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
@@ -348,8 +270,10 @@ class DiagonalWrapper : public DiagonalBase<DiagonalWrapper<DiagonalVectorType_>
  * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
  **/
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr const DiagonalWrapper<const Derived> MatrixBase<Derived>::asDiagonal() const {
+inline const DiagonalWrapper<const Derived>
-  return DiagonalWrapper<const Derived>(derived());
+MatrixBase<Derived>::asDiagonal() const
 {
  return derived();
 }
 /** \returns true if *this is approximately equal to a diagonal matrix,
@@ -361,113 +285,22 @@ EIGEN_DEVICE_FUNC constexpr const DiagonalWrapper<const Derived> MatrixBase<Deri
  * \sa asDiagonal()
  */
 template<typename Derived>
-bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const {
+bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
 {
  if(cols() != rows()) return false;
  RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
-  for (Index j = 0; j < cols(); ++j) {
+  for(Index j = 0; j < cols(); ++j)
-    RealScalar absOnDiagonal = numext::abs(coeff(j, j));
+  {
    RealScalar absOnDiagonal = internal::abs(coeff(j,j));
    if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
  }
  for(Index j = 0; j < cols(); ++j)
-    for (Index i = 0; i < j; ++i) {
+    for(Index i = 0; i < j; ++i)
    {
      if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
      if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
    }
  return true;
 }
 /** \returns DiagonalWrapper.
 *
 * Example: \include MatrixBase_diagonalView.cpp
 * Output: \verbinclude MatrixBase_diagonalView.out
 *
 * \sa diagonalView()
 */
 /** This is the non-const version of diagonalView() with DiagIndex_ . */
 template <typename Derived>
 template <int DiagIndex_>
 EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DiagIndex_>> MatrixBase<Derived>::diagonalView() {
  typedef Diagonal<Derived, DiagIndex_> DiagType;
  typedef DiagonalWrapper<DiagType> ReturnType;
  DiagType diag(this->derived());
  return ReturnType(diag);
 }
 /** This is the const version of diagonalView() with DiagIndex_ . */
 template <typename Derived>
 template <int DiagIndex_>
 EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DiagIndex_>> MatrixBase<Derived>::diagonalView()
    const {
  typedef Diagonal<const Derived, DiagIndex_> DiagType;
  typedef DiagonalWrapper<DiagType> ReturnType;
  DiagType diag(this->derived());
  return ReturnType(diag);
 }
 /** This is the non-const version of diagonalView() with dynamic index. */
 template <typename Derived>
 EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DynamicIndex>> MatrixBase<Derived>::diagonalView(
    Index index) {
  typedef Diagonal<Derived, DynamicIndex> DiagType;
  typedef DiagonalWrapper<DiagType> ReturnType;
  DiagType diag(this->derived(), index);
  return ReturnType(diag);
 }
 /** This is the const version of diagonalView() with dynamic index. */
 template <typename Derived>
 EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DynamicIndex>> MatrixBase<Derived>::diagonalView(
    Index index) const {
  typedef Diagonal<const Derived, DynamicIndex> DiagType;
  typedef DiagonalWrapper<DiagType> ReturnType;
  DiagType diag(this->derived(), index);
  return ReturnType(diag);
 }
 namespace internal {
 template <>
 struct storage_kind_to_shape<DiagonalShape> {
  typedef DiagonalShape Shape;
 };
 struct Diagonal2Dense {};
 template <>
 struct AssignmentKind<DenseShape, DiagonalShape> {
  typedef Diagonal2Dense Kind;
 };
 // Diagonal matrix to Dense assignment
 template <typename DstXprType, typename SrcXprType, typename Functor>
 struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense> {
  static EIGEN_DEVICE_FUNC void run(
      DstXprType& dst, const SrcXprType& src,
      const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
    Index dstRows = src.rows();
    Index dstCols = src.cols();
    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
    dst.setZero();
    dst.diagonal() = src.diagonal();
  }
  static EIGEN_DEVICE_FUNC void run(
      DstXprType& dst, const SrcXprType& src,
      const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
    dst.diagonal() += src.diagonal();
  }
  static EIGEN_DEVICE_FUNC void run(
      DstXprType& dst, const SrcXprType& src,
      const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
    dst.diagonal() -= src.diagonal();
  }
 };
 }  // namespace internal
 }  // end namespace Eigen
 #endif // EIGEN_DIAGONALMATRIX_H
--- a/Eigen/src/Core/DiagonalProduct.h
+++ b/Eigen/src/Core/DiagonalProduct.h
@@ -4,27 +4,132 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DIAGONALPRODUCT_H
 #define EIGEN_DIAGONALPRODUCT_H
-// IWYU pragma: private
+namespace internal {
-#include "./InternalHeaderCheck.h"
+template<typename MatrixType, typename DiagonalType, int ProductOrder>
 struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
 : traits<MatrixType>
 {
  typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
  enum {
    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-namespace Eigen {
+    _StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
    _PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
                    ||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
    _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
    // FIXME currently we need same types, but in the future the next rule should be the one
    //_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
    _Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
    Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
    CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
  };
 };
 }
 template<typename MatrixType, typename DiagonalType, int ProductOrder>
 class DiagonalProduct : internal::no_assignment_operator,
                        public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
 {
  public:
    typedef MatrixBase<DiagonalProduct> Base;
    EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
    inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
      : m_matrix(matrix), m_diagonal(diagonal)
    {
      eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
    }
    inline Index rows() const { return m_matrix.rows(); }
    inline Index cols() const { return m_matrix.cols(); }
    const Scalar coeff(Index row, Index col) const
    {
      return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
    {
      enum {
        StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
      };
      const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
      return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
        ((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
       ||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
    }
  protected:
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
    {
      return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
                     internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
    }
    template<int LoadMode>
    EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
    {
      enum {
        InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
        DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
      };
      return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
                     m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
    }
    const typename MatrixType::Nested m_matrix;
    const typename DiagonalType::Nested m_diagonal;
 };
 /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
  */
 template<typename Derived>
 template<typename DiagonalDerived>
-EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct> MatrixBase<Derived>::operator*(
+inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
-    const DiagonalBase<DiagonalDerived> &a_diagonal) const {
+MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
-  return Product<Derived, DiagonalDerived, LazyProduct>(derived(), a_diagonal.derived());
+{
  return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
 }
 /** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
  */
 template<typename DiagonalDerived>
 template<typename MatrixDerived>
 inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
 DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
 {
  return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
 }
 }  // end namespace Eigen
 #endif // EIGEN_DIAGONALPRODUCT_H
--- a/Eigen/src/Core/Dot.h
+++ b/Eigen/src/Core/Dot.h
@@ -3,37 +3,64 @@
 //
 // Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_DOT_H
 #define EIGEN_DOT_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
 namespace Eigen {
 namespace internal {
-template <typename Derived, typename Scalar = typename traits<Derived>::Scalar>
+// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
-struct squared_norm_impl {
+// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
-  using Real = typename NumTraits<Scalar>::Real;
+// looking at the static assertions. Thus this is a trick to get better compile errors.
-  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Real run(const Derived& a) {
+template<typename T, typename U,
-    return a.realView().cwiseAbs2().sum();
+// the NeedToTranspose condition here is taken straight from Assign.h
         bool NeedToTranspose = T::IsVectorAtCompileTime
                && U::IsVectorAtCompileTime
                && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
                         // revert to || as soon as not needed anymore.
                    (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
 >
 struct dot_nocheck
 {
  typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
  static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
  {
    return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
  }
 };
-template <typename Derived>
+template<typename T, typename U>
-struct squared_norm_impl<Derived, bool> {
+struct dot_nocheck<T, U, true>
-  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool run(const Derived& a) { return a.any(); }
+{
  typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
  static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
  {
    return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
  }
 };
 } // end namespace internal
-/** \fn MatrixBase::dot
+/** \returns the dot product of *this with other.
 * \returns the dot product of *this with other.
  *
  * \only_for_vectors
  *
@@ -45,117 +72,100 @@ struct squared_norm_impl<Derived, bool> {
  */
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
-    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
+MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
-                                  typename internal::traits<OtherDerived>::Scalar>::ReturnType
+{
-    MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return internal::dot_impl<Derived, OtherDerived>::run(derived(), other.derived());
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
  typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
  EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
  eigen_assert(size() == other.size());
  return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
 }
 #ifdef EIGEN2_SUPPORT
 /** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
  * (conjugating the second variable). Of course this only makes a difference in the complex case.
  *
  * This method is only available in EIGEN2_SUPPORT mode.
  *
  * \only_for_vectors
  *
  * \sa dot()
  */
 template<typename Derived>
 template<typename OtherDerived>
 typename internal::traits<Derived>::Scalar
 MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
 {
  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
  eigen_assert(size() == other.size());
  return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
 }
 #endif
 //---------- implementation of L2 norm and related functions ----------
-/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
+/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
  * In both cases, it consists in the sum of the square of all the matrix entries.
- * For vectors, this is also equal to the dot product of \c *this with itself.
+  * For vectors, this is also equals to the dot product of \c *this with itself.
  *
- * \sa dot(), norm(), lpNorm()
+  * \sa dot(), norm()
  */
 template<typename Derived>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
-MatrixBase<Derived>::squaredNorm() const {
+{
-  return internal::squared_norm_impl<Derived>::run(derived());
+  return internal::real((*this).cwiseAbs2().sum());
 }
 /** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
- * For vectors, this is also equal to the square root of the dot product of \c *this with itself.
+  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
  *
- * \sa lpNorm(), dot(), squaredNorm()
+  * \sa dot(), squaredNorm()
  */
 template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
-MatrixBase<Derived>::norm() const {
+{
-  return numext::sqrt(squaredNorm());
+  return internal::sqrt(squaredNorm());
 }
-/** \returns an expression of the quotient of \c *this by its own norm.
+/** \returns an expression of the quotient of *this by its own norm.
 *
 * \warning If the input vector is too small (i.e., this->norm()==0),
 *          then this function returns a copy of the input.
  *
  * \only_for_vectors
  *
  * \sa norm(), normalize()
  */
 template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject MatrixBase<Derived>::normalized()
+inline const typename MatrixBase<Derived>::PlainObject
-    const {
+MatrixBase<Derived>::normalized() const
-  typedef typename internal::nested_eval<Derived, 2>::type Nested_;
+{
-  Nested_ n(derived());
+  typedef typename internal::nested<Derived>::type Nested;
-  RealScalar z = n.squaredNorm();
+  typedef typename internal::remove_reference<Nested>::type _Nested;
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  _Nested n(derived());
-  if (z > RealScalar(0))
+  return n / n.norm();
    return n / numext::sqrt(z);
  else
    return n;
 }
 /** Normalizes the vector, i.e. divides it by its own norm.
  *
  * \only_for_vectors
  *
 * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
 *
  * \sa norm(), normalized()
  */
 template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize() {
+inline void MatrixBase<Derived>::normalize()
-  RealScalar z = squaredNorm();
+{
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  *this /= norm();
  if (z > RealScalar(0)) derived() /= numext::sqrt(z);
 }
 /** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
 *
 * \only_for_vectors
 *
 * This method is analogue to the normalized() method, but it reduces the risk of
 * underflow and overflow when computing the norm.
 *
 * \warning If the input vector is too small (i.e., this->norm()==0),
 *          then this function returns a copy of the input.
 *
 * \sa stableNorm(), stableNormalize(), normalized()
 */
 template <typename Derived>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
 MatrixBase<Derived>::stableNormalized() const {
  typedef typename internal::nested_eval<Derived, 3>::type Nested_;
  Nested_ n(derived());
  RealScalar w = n.cwiseAbs().maxCoeff();
  RealScalar z = (n / w).squaredNorm();
  if (z > RealScalar(0))
    return n / (numext::sqrt(z) * w);
  else
    return n;
 }
 /** Normalizes the vector while avoid underflow and overflow
 *
 * \only_for_vectors
 *
 * This method is analogue to the normalize() method, but it reduces the risk of
 * underflow and overflow when computing the norm.
 *
 * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
 *
 * \sa stableNorm(), stableNormalized(), normalize()
 */
 template <typename Derived>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize() {
  RealScalar w = cwiseAbs().maxCoeff();
  RealScalar z = (derived() / w).squaredNorm();
  if (z > RealScalar(0)) derived() /= numext::sqrt(z) * w;
 }
 //---------- implementation of other norms ----------
@@ -163,64 +173,55 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize(
 namespace internal {
 template<typename Derived, int p>
-struct lpNorm_selector {
+struct lpNorm_selector
 {
  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
+  inline static RealScalar run(const MatrixBase<Derived>& m)
-    EIGEN_USING_STD(pow)
+  {
    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
  }
 };
 template<typename Derived>
-struct lpNorm_selector<Derived, 1> {
+struct lpNorm_selector<Derived, 1>
-  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
+{
-      const MatrixBase<Derived>& m) {
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
  {
    return m.cwiseAbs().sum();
  }
 };
 template<typename Derived>
-struct lpNorm_selector<Derived, 2> {
+struct lpNorm_selector<Derived, 2>
-  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
+{
-      const MatrixBase<Derived>& m) {
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
  {
    return m.norm();
  }
 };
 template<typename Derived>
-struct lpNorm_selector<Derived, Infinity> {
+struct lpNorm_selector<Derived, Infinity>
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
+{
-  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
-    if (Derived::SizeAtCompileTime == 0 || (Derived::SizeAtCompileTime == Dynamic && m.size() == 0))
+  {
      return RealScalar(0);
    return m.cwiseAbs().maxCoeff();
  }
 };
 } // end namespace internal
-/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the
+/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
- * p-th powers of the absolute values of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity,
+  *          of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
- * this function returns the \f$ \ell^\infty \f$ norm, that is the maximum of the absolute values of the coefficients of
+  *          norm, that is the maximum of the absolute values of the coefficients of *this.
 * \c *this.
 *
 * In all cases, if \c *this is empty, then the value 0 is returned.
 *
 * \note For matrices, this function does not compute the <a
 * href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its
 * coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm
 * matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
  *
  * \sa norm()
  */
 template<typename Derived>
 template<int p>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::lpNorm() const
-#else
+{
 EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
 #endif
 MatrixBase<Derived>::lpNorm() const {
  return internal::lpNorm_selector<Derived, p>::run(*this);
 }
@@ -234,10 +235,12 @@ MatrixBase<Derived>::lpNorm() const {
  */
 template<typename Derived>
 template<typename OtherDerived>
-bool MatrixBase<Derived>::isOrthogonal(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const {
+bool MatrixBase<Derived>::isOrthogonal
-  typename internal::nested_eval<Derived, 2>::type nested(derived());
+(const MatrixBase<OtherDerived>& other, RealScalar prec) const
-  typename internal::nested_eval<OtherDerived, 2>::type otherNested(other.derived());
+{
-  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
+  typename internal::nested<Derived,2>::type nested(derived());
  typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
  return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
 }
 /** \returns true if *this is approximately an unitary matrix,
@@ -252,16 +255,18 @@ bool MatrixBase<Derived>::isOrthogonal(const MatrixBase<OtherDerived>& other, co
  * Output: \verbinclude MatrixBase_isUnitary.out
  */
 template<typename Derived>
-bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const {
+bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
-  typename internal::nested_eval<Derived, 1>::type self(derived());
+{
-  for (Index i = 0; i < cols(); ++i) {
+  typename Derived::Nested nested(derived());
-    if (!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec)) return false;
+  for(Index i = 0; i < cols(); ++i)
  {
    if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
      return false;
    for(Index j = 0; j < i; ++j)
-      if (!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec)) return false;
+      if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
        return false;
  }
  return true;
 }
 }  // end namespace Eigen
 #endif // EIGEN_DOT_H
--- a/Eigen/src/Core/EigenBase.h
+++ b/Eigen/src/Core/EigenBase.h
@@ -4,22 +4,30 @@
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
+// Eigen is free software; you can redistribute it and/or
-// Public License v. 2.0. If a copy of the MPL was not distributed
+// modify it under the terms of the GNU Lesser General Public
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.
 #ifndef EIGEN_EIGENBASE_H
 #define EIGEN_EIGENBASE_H
 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"
-namespace Eigen {
+/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
 /** \class EigenBase
 * \ingroup Core_Module
 *
 * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
  *
  * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
  *
@@ -27,51 +35,40 @@ namespace Eigen {
  *
  * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
  *
- * \sa \blank \ref TopicClassHierarchy
+  * \sa \ref TopicClassHierarchy
  */
-template <typename Derived>
+template<typename Derived> struct EigenBase
-struct EigenBase {
+{
 //   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
  /** \brief The interface type of indices
   * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
   * \sa StorageIndex, \ref TopicPreprocessorDirectives.
   * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
   * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation
   * attribute.
   */
  typedef Eigen::Index Index;
  // FIXME is it needed?
  typedef typename internal::traits<Derived>::StorageKind StorageKind;
  typedef typename internal::traits<Derived>::Index Index;
  /** \returns a reference to the derived object */
-  EIGEN_DEVICE_FUNC constexpr Derived& derived() { return *static_cast<Derived*>(this); }
+  Derived& derived() { return *static_cast<Derived*>(this); }
  /** \returns a const reference to the derived object */
-  EIGEN_DEVICE_FUNC constexpr const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-  EIGEN_DEVICE_FUNC inline constexpr Derived& const_cast_derived() const {
+  inline Derived& const_cast_derived() const
-    return *static_cast<Derived*>(const_cast<EigenBase*>(this));
+  { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
-  }
+  inline const Derived& const_derived() const
-  EIGEN_DEVICE_FUNC constexpr inline const Derived& const_derived() const { return *static_cast<const Derived*>(this); }
+  { return *static_cast<const Derived*>(this); }
  /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
-  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return derived().rows(); }
+  inline Index rows() const { return derived().rows(); }
  /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
-  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return derived().cols(); }
+  inline Index cols() const { return derived().cols(); }
  /** \returns the number of coefficients, which is rows()*cols().
    * \sa rows(), cols(), SizeAtCompileTime. */
-  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return rows() * cols(); }
+  inline Index size() const { return rows() * cols(); }
  /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
-  template <typename Dest>
+  template<typename Dest> inline void evalTo(Dest& dst) const
-  EIGEN_DEVICE_FUNC constexpr inline void evalTo(Dest& dst) const {
+  { derived().evalTo(dst); }
    derived().evalTo(dst);
  }
  /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
-  template <typename Dest>
+  template<typename Dest> inline void addTo(Dest& dst) const
-  EIGEN_DEVICE_FUNC constexpr inline void addTo(Dest& dst) const {
+  {
    // This is the default implementation,
    // derived class can reimplement it in a more optimized way.
    typename Dest::PlainObject res(rows(),cols());
@@ -80,8 +77,8 @@ struct EigenBase {
  }
  /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
-  template <typename Dest>
+  template<typename Dest> inline void subTo(Dest& dst) const
-  EIGEN_DEVICE_FUNC constexpr inline void subTo(Dest& dst) const {
+  {
    // This is the default implementation,
    // derived class can reimplement it in a more optimized way.
    typename Dest::PlainObject res(rows(),cols());
@@ -90,25 +87,21 @@ struct EigenBase {
  }
  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
-  template <typename Dest>
+  template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
-  EIGEN_DEVICE_FUNC constexpr inline void applyThisOnTheRight(Dest& dst) const {
+  {
    // This is the default implementation,
    // derived class can reimplement it in a more optimized way.
    dst = dst * this->derived();
  }
  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
-  template <typename Dest>
+  template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const
-  EIGEN_DEVICE_FUNC constexpr inline void applyThisOnTheLeft(Dest& dst) const {
+  {
    // This is the default implementation,
    // derived class can reimplement it in a more optimized way.
    dst = this->derived() * dst;
  }
  template <typename Device>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> device(Device& device);
  template <typename Device>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> device(Device& device) const;
 };
 /***************************************************************************
@@ -125,25 +118,55 @@ struct EigenBase {
  */
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived>& other) {
+Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
-  call_assignment(derived(), other.derived());
+{
  other.derived().evalTo(derived());
  return derived();
 }
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived>& other) {
+Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+{
  other.derived().addTo(derived());
  return derived();
 }
 template<typename Derived>
 template<typename OtherDerived>
-EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived>& other) {
+Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+{
  other.derived().subTo(derived());
  return derived();
 }
-}  // end namespace Eigen
+/** replaces \c *this by \c *this * \a other.
  *
  * \returns a reference to \c *this
  */
 template<typename Derived>
 template<typename OtherDerived>
 inline Derived&
 MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
 {
  other.derived().applyThisOnTheRight(derived());
  return derived();
 }
 /** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
 template<typename Derived>
 template<typename OtherDerived>
 inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
 {
  other.derived().applyThisOnTheRight(derived());
 }
 /** replaces \c *this by \c *this * \a other. */
 template<typename Derived>
 template<typename OtherDerived>
 inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
 {
  other.derived().applyThisOnTheLeft(derived());
 }
 #endif // EIGEN_EIGENBASE_H
--- a/Show More
+++ b/Show More