oops, placement new should take a void*

doing QVector<Vector3d>...

CMakeLists.txt

@@ -1,67 +1,95 @@
-PROJECT(Eigen)
-SET(EIGEN_VERSION_NUMBER "2.0-beta1")
+project(Eigen)
+set(EIGEN_VERSION_NUMBER "2.0-beta5")
 
 #if the svnversion program is absent, this will leave the SVN_REVISION string empty,
 #but won't stop CMake.
-EXECUTE_PROCESS(COMMAND svnversion -n ${CMAKE_SOURCE_DIR}
-                OUTPUT_VARIABLE EIGEN_SVN_REVISION)
+execute_process(COMMAND svnversion -n ${CMAKE_SOURCE_DIR}
+                OUTPUT_VARIABLE EIGEN_SVNVERSION_OUTPUT)
 
-IF(EIGEN_SVN_REVISION)
-SET(EIGEN_VERSION "${EIGEN_VERSION_NUMBER} (SVN revision ${EIGEN_SVN_REVISION})")
-ELSE(EIGEN_SVN_REVISION)
-SET(EIGEN_VERSION "${EIGEN_VERSION_NUMBER}")
-ENDIF(EIGEN_SVN_REVISION)
+#we only want EIGEN_SVN_REVISION if it is an actual revision number, not a string like "exported"
+string(REGEX MATCH "^[0-9]+.*" EIGEN_SVN_REVISION "${EIGEN_SVNVERSION_OUTPUT}")
 
-SET(EIGEN_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
-SET(EIGEN_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
+if(EIGEN_SVN_REVISION)
+  set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER} (SVN revision ${EIGEN_SVN_REVISION})")
+else(EIGEN_SVN_REVISION)
+  set(EIGEN_VERSION "${EIGEN_VERSION_NUMBER}")
+endif(EIGEN_SVN_REVISION)
 
-CMAKE_MINIMUM_REQUIRED(VERSION 2.4)
+cmake_minimum_required(VERSION 2.6.2)
 
 set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
 
-OPTION(BUILD_TESTS "Build tests" OFF)
-OPTION(BUILD_DEMOS "Build demos" OFF)
-OPTION(BUILD_LIB "Build the binary shared library" OFF)
-OPTION(BUILD_BTL "Build benchmark suite" OFF)
+option(EIGEN_BUILD_TESTS "Build tests" OFF)
+option(EIGEN_BUILD_DEMOS "Build demos" OFF)
+if(NOT WIN32)
+  option(EIGEN_BUILD_LIB "Build the binary shared library" OFF)
+endif(NOT WIN32)
+option(EIGEN_BUILD_BTL "Build benchmark suite" OFF)
 
-IF(BUILD_LIB)
-  OPTION(TEST_LIB "Build the unit tests using the library (disable -pedantic)" OFF)
-ENDIF(BUILD_LIB)
+if(EIGEN_BUILD_LIB)
+  option(EIGEN_TEST_LIB "Build the unit tests using the library (disable -pedantic)" OFF)
+endif(EIGEN_BUILD_LIB)
 
-SET(CMAKE_INCLUDE_CURRENT_DIR ON)
+set(CMAKE_INCLUDE_CURRENT_DIR ON)
 
-IF(CMAKE_COMPILER_IS_GNUCXX)
-  IF(CMAKE_SYSTEM_NAME MATCHES Linux)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -fno-exceptions -fno-check-new -fno-common -fstrict-aliasing")
-    IF(NOT TEST_LIB)
-      SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
-    ENDIF(NOT TEST_LIB)
-    IF(TEST_SSE2)
-      SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse2")
-      MESSAGE("Enabling SSE2 in tests/examples")
-    ENDIF(TEST_SSE2)
-    IF(TEST_SSE3)
-      SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse3")
-      MESSAGE("Enabling SSE3 in tests/examples")
-    ENDIF(TEST_SSE3)
-    IF(TEST_SSSE3)
-      SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mssse3")
-      MESSAGE("Enabling SSSE3 in tests/examples")
-    ENDIF(TEST_SSSE3)
-    IF(TEST_ALTIVEC)
-      SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
-      MESSAGE("Enabling AltiVec in tests/examples")
-    ENDIF(TEST_ALTIVEC)
-  ENDIF(CMAKE_SYSTEM_NAME MATCHES Linux)
-ENDIF(CMAKE_COMPILER_IS_GNUCXX)
+if(CMAKE_COMPILER_IS_GNUCXX)
+  if(CMAKE_SYSTEM_NAME MATCHES Linux)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wnon-virtual-dtor -Wno-long-long -ansi -Wundef -Wcast-align -Wchar-subscripts -Wall -W -Wpointer-arith -Wwrite-strings -Wformat-security -Wextra -fno-exceptions -fno-check-new -fno-common -fstrict-aliasing")
+    if(NOT EIGEN_TEST_LIB)
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic")
+    endif(NOT EIGEN_TEST_LIB)
 
-INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+    option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
+    if(EIGEN_TEST_SSE2)
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse2")
+      message("Enabling SSE2 in tests/examples")
+    endif(EIGEN_TEST_SSE2)
 
-ADD_SUBDIRECTORY(Eigen)
-ADD_SUBDIRECTORY(test)
-ADD_SUBDIRECTORY(doc)
-ADD_SUBDIRECTORY(demos)
+    option(EIGEN_TEST_SSE3 "Enable/Disable SSE3 in tests/examples" OFF)
+    if(EIGEN_TEST_SSE3)
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse3")
+      message("Enabling SSE3 in tests/examples")
+    endif(EIGEN_TEST_SSE3)
 
-IF(BUILD_BTL)
-  ADD_SUBDIRECTORY(bench/btl)
-ENDIF(BUILD_BTL)
+    option(EIGEN_TEST_SSSE3 "Enable/Disable SSSE3 in tests/examples" OFF)
+    if(EIGEN_TEST_SSSE3)
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mssse3")
+      message("Enabling SSSE3 in tests/examples")
+    endif(EIGEN_TEST_SSSE3)
+
+    option(EIGEN_TEST_ALTIVEC "Enable/Disable altivec in tests/examples" OFF)
+    if(EIGEN_TEST_ALTIVEC)
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -maltivec -mabi=altivec")
+      message("Enabling AltiVec in tests/examples")
+    endif(EIGEN_TEST_ALTIVEC)
+
+  endif(CMAKE_SYSTEM_NAME MATCHES Linux)
+endif(CMAKE_COMPILER_IS_GNUCXX)
+
+if(MSVC)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ")
+
+  option(EIGEN_TEST_SSE2 "Enable/Disable SSE2 in tests/examples" OFF)
+  if(EIGEN_TEST_SSE2)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:SSE2")
+    message("Enabling SSE2 in tests/examples")
+  endif(EIGEN_TEST_SSE2)
+endif(MSVC)
+
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+
+add_subdirectory(Eigen)
+
+if(EIGEN_BUILD_TESTS)
+  add_subdirectory(test)
+endif(EIGEN_BUILD_TESTS)
+
+add_subdirectory(doc)
+
+if(EIGEN_BUILD_DEMOS)
+  add_subdirectory(demos)
+endif(EIGEN_BUILD_DEMOS)
+
+if(EIGEN_BUILD_BTL)
+  add_subdirectory(bench/btl)
+endif(EIGEN_BUILD_BTL)

Doxyfile

@@ -5,7 +5,7 @@
 #---------------------------------------------------------------------------
 DOXYFILE_ENCODING      = UTF-8
 PROJECT_NAME           = Eigen
-PROJECT_NUMBER         = 2.0-alpha7
+PROJECT_NUMBER         = 2.0
 OUTPUT_DIRECTORY       = ./
 CREATE_SUBDIRS         = NO
 OUTPUT_LANGUAGE        = English

Eigen/Array

@@ -3,6 +3,8 @@
 
 #include "Core"
 
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 namespace Eigen {
 
 /** \defgroup Array Array module
@@ -28,7 +30,10 @@ namespace Eigen {
 #include "src/Array/Select.h"
 #include "src/Array/PartialRedux.h"
 #include "src/Array/Random.h"
+#include "src/Array/Norms.h"
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_ARRAY_MODULE_H

Eigen/CMakeLists.txt

@@ -1,34 +1,34 @@
-SET(Eigen_HEADERS Core LU Cholesky QR Geometry Sparse Array SVD Regression)
+set(Eigen_HEADERS Core LU Cholesky QR Geometry Sparse Array SVD Regression LeastSquares)
 
-IF(BUILD_LIB)
-    SET(Eigen_SRCS
+if(EIGEN_BUILD_LIB)
+    set(Eigen_SRCS
       src/Core/CoreInstantiations.cpp
       src/Cholesky/CholeskyInstantiations.cpp
       src/QR/QrInstantiations.cpp
     )
 
-    ADD_LIBRARY(Eigen2 SHARED ${Eigen_SRCS})
+    add_library(Eigen2 SHARED ${Eigen_SRCS})
 
-    INSTALL(TARGETS Eigen2
+    install(TARGETS Eigen2
             RUNTIME DESTINATION bin
             LIBRARY DESTINATION lib
             ARCHIVE DESTINATION lib)
-ENDIF(BUILD_LIB)
+endif(EIGEN_BUILD_LIB)
 
-IF(CMAKE_COMPILER_IS_GNUCXX)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g1 -O2")
-    SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -g1 -O2")
-ENDIF(CMAKE_COMPILER_IS_GNUCXX)
+if(CMAKE_COMPILER_IS_GNUCXX)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g1 -O2")
+    set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -g1 -O2")
+endif(CMAKE_COMPILER_IS_GNUCXX)
 
-SET(INCLUDE_INSTALL_DIR
+set(INCLUDE_INSTALL_DIR
     "${CMAKE_INSTALL_PREFIX}/include/eigen2"
     CACHE PATH
     "The directory where we install the header files"
     FORCE)
 
-INSTALL(FILES
+install(FILES
   ${Eigen_HEADERS}
   DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen
   )
 
-ADD_SUBDIRECTORY(src)
+add_subdirectory(src)

Eigen/Cholesky

@@ -3,6 +3,8 @@
 
 #include "Core"
 
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 // Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
 #if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS>=2)
   #ifndef EIGEN_HIDE_HEAVY_CODE
@@ -17,8 +19,8 @@ namespace Eigen {
 /** \defgroup Cholesky_Module Cholesky module
   * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
   * Those decompositions are accessible via the following MatrixBase methods:
-  *  - MatrixBase::cholesky(),
-  *  - MatrixBase::choleskyNoSqrt()
+  *  - MatrixBase::llt(),
+  *  - MatrixBase::ldlt()
   *
   * \code
   * #include <Eigen/Cholesky>
@@ -27,6 +29,8 @@ namespace Eigen {
 
 #include "src/Array/CwiseOperators.h"
 #include "src/Array/Functors.h"
+#include "src/Cholesky/LLT.h"
+#include "src/Cholesky/LDLT.h"
 #include "src/Cholesky/Cholesky.h"
 #include "src/Cholesky/CholeskyWithoutSquareRoot.h"
 
@@ -55,4 +59,6 @@ namespace Eigen {
 } // namespace Eigen
 #endif
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_CHOLESKY_MODULE_H

Eigen/Core

@@ -1,18 +1,32 @@
 #ifndef EIGEN_CORE_H
 #define EIGEN_CORE_H
 
+// first thing Eigen does: prevent MSVC from committing suicide
+#include "src/Core/util/DisableMSVCWarnings.h"
 #ifdef _MSC_VER
-#pragma warning( disable : 4181 4244 )
+  #if (_MSC_VER >= 1500) // 2008 or later
+    // Remember that usage of defined() in a #define is undefined by the standard
+    #ifdef _M_IX86_FP
+      #if _M_IX86_FP >= 2
+        #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
+      #endif
+    #endif   
+  #endif
 #endif
 
 #ifdef __GNUC__
-#define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__>=x && __GNUC_MINOR__>=y) || __GNUC__>x)
+  #define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__>=x && __GNUC_MINOR__>=y) || __GNUC__>x)
 #else
-#define EIGEN_GNUC_AT_LEAST(x,y) 0
+  #define EIGEN_GNUC_AT_LEAST(x,y) 0
+#endif
+
+// 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_BUT_NOT_OLD_GCC
 #endif
 
 #ifndef EIGEN_DONT_VECTORIZE
-  #if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
+  #if defined (EIGEN_SSE2_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
     #define EIGEN_VECTORIZE
     #define EIGEN_VECTORIZE_SSE
     #include <emmintrin.h>
@@ -23,11 +37,11 @@
     #ifdef __SSSE3__
       #include <tmmintrin.h>
     #endif
-  #elif (defined __ALTIVEC__)
+  #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>
+    // We need to #undef all these ugly tokens defined in <altivec.h>
     // => use __vector instead of vector
     #undef bool
     #undef vector
@@ -44,6 +58,18 @@
 #include <cstring>
 #include <string>
 
+#if defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER) && defined(EIGEN_VECTORIZE)
+  #include <malloc.h> // for _aligned_malloc
+#endif
+
+#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_NO_EXCEPTIONS)
+  #define EIGEN_EXCEPTIONS
+#endif
+
+#ifdef EIGEN_EXCEPTIONS
+  #include <new>
+#endif
+
 namespace Eigen {
 
 /** \defgroup Core_Module Core module
@@ -69,9 +95,9 @@ namespace Eigen {
 #include "src/Core/GenericPacketMath.h"
 
 #if defined EIGEN_VECTORIZE_SSE
-#include "src/Core/arch/SSE/PacketMath.h"
+  #include "src/Core/arch/SSE/PacketMath.h"
 #elif defined EIGEN_VECTORIZE_ALTIVEC
-#include "src/Core/arch/AltiVec/PacketMath.h"
+  #include "src/Core/arch/AltiVec/PacketMath.h"
 #endif
 
 #ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
@@ -81,10 +107,12 @@ namespace Eigen {
 #include "src/Core/Functors.h"
 #include "src/Core/MatrixBase.h"
 #include "src/Core/Coeffs.h"
+
 #ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
                                 // at least confirmed with Doxygen 1.5.5 and 1.5.6
-#include "src/Core/Assign.h"
+  #include "src/Core/Assign.h"
 #endif
+
 #include "src/Core/MatrixStorage.h"
 #include "src/Core/NestByValue.h"
 #include "src/Core/Flagged.h"
@@ -116,4 +144,6 @@ namespace Eigen {
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_CORE_H

Eigen/Geometry

@@ -1,7 +1,12 @@
 #ifndef EIGEN_GEOMETRY_MODULE_H
 #define EIGEN_GEOMETRY_MODULE_H
 
+#include "Core"
+
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 #include "Array"
+#include <limits>
 
 #ifndef M_PI
 #define M_PI 3.14159265358979323846
@@ -28,12 +33,16 @@ namespace Eigen {
 #include "src/Geometry/Rotation2D.h"
 #include "src/Geometry/Quaternion.h"
 #include "src/Geometry/AngleAxis.h"
+#include "src/Geometry/EulerAngles.h"
 #include "src/Geometry/Transform.h"
 #include "src/Geometry/Translation.h"
 #include "src/Geometry/Scaling.h"
 #include "src/Geometry/Hyperplane.h"
 #include "src/Geometry/ParametrizedLine.h"
+#include "src/Geometry/AlignedBox.h"
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_GEOMETRY_MODULE_H

Eigen/LU

@@ -3,6 +3,8 @@
 
 #include "Core"
 
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 namespace Eigen {
 
 /** \defgroup LU_Module LU module
@@ -22,4 +24,6 @@ namespace Eigen {
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_LU_MODULE_H

Eigen/LeastSquares

@@ -0,0 +1,28 @@
+#ifndef EIGEN_REGRESSION_MODULE_H
+#define EIGEN_REGRESSION_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableMSVCWarnings.h"
+
+#include "LU"
+#include "QR"
+#include "Geometry"
+
+namespace Eigen {
+
+/** \defgroup Regression_Module Regression module
+  * This module provides linear regression and related features.
+  *
+  * \code
+  * #include <Eigen/Regression>
+  * \endcode
+  */
+
+#include "src/Regression/Regression.h"
+
+} // namespace Eigen
+
+#include "src/Core/util/EnableMSVCWarnings.h"
+
+#endif // EIGEN_REGRESSION_MODULE_H

Eigen/QR

@@ -2,6 +2,9 @@
 #define EIGEN_QR_MODULE_H
 
 #include "Core"
+
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 #include "Cholesky"
 
 // Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
@@ -62,4 +65,6 @@ namespace Eigen {
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_QR_MODULE_H

Eigen/Regression

@@ -1,22 +1,5 @@
-#ifndef EIGEN_REGRESSION_MODULE_H
-#define EIGEN_REGRESSION_MODULE_H
+#ifdef __GNUC__
+#warning "The Eigen/Regression header file has been renamed to Eigen/LeastSquares. The old name is deprecated, please update your code."
+#endif
 
-#include "LU"
-#include "QR"
-#include "Geometry"
-
-namespace Eigen {
-
-/** \defgroup Regression_Module Regression module
-  * This module provides linear regression and related features.
-  *
-  * \code
-  * #include <Eigen/Regression>
-  * \endcode
-  */
-
-#include "src/Regression/Regression.h"
-
-} // namespace Eigen
-
-#endif // EIGEN_REGRESSION_MODULE_H
+#include "LeastSquares"

Eigen/SVD

@@ -3,6 +3,8 @@
 
 #include "Core"
 
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 namespace Eigen {
 
 /** \defgroup SVD_Module SVD module
@@ -19,4 +21,6 @@ namespace Eigen {
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_SVD_MODULE_H

Eigen/Sparse

@@ -2,26 +2,107 @@
 #define EIGEN_SPARSE_MODULE_H
 
 #include "Core"
+
+#include "src/Core/util/DisableMSVCWarnings.h"
+
 #include <vector>
 #include <map>
 #include <cstdlib>
 #include <cstring>
 #include <algorithm>
 
+#ifdef EIGEN_GOOGLEHASH_SUPPORT
+  #include <google/dense_hash_map>
+#endif
+
+#ifdef EIGEN_CHOLMOD_SUPPORT
+  extern "C" {
+    #include "cholmod.h"
+  }
+#endif
+
+#ifdef EIGEN_TAUCS_SUPPORT
+
+  // taucs.h declares a lot of mess
+  #define isnan
+  #define finite
+  #define isinf
+  extern "C" {
+    #include "taucs.h"
+  }
+  #undef isnan
+  #undef finite
+  #undef isinf
+
+  #ifdef min
+    #undef min
+  #endif
+  #ifdef max
+    #undef max
+  #endif
+
+#endif
+
+#ifdef EIGEN_SUPERLU_SUPPORT
+  typedef int int_t;
+  #include "superlu/slu_Cnames.h"
+  #include "superlu/supermatrix.h"
+  #include "superlu/slu_util.h"
+
+  namespace SuperLU_S {
+  #include "superlu/slu_sdefs.h"
+  }
+  namespace SuperLU_D {
+  #include "superlu/slu_ddefs.h"
+  }
+  namespace SuperLU_C {
+  #include "superlu/slu_cdefs.h"
+  }
+  namespace SuperLU_Z {
+  #include "superlu/slu_zdefs.h"
+  }
+  namespace Eigen { struct SluMatrix; }
+#endif
+
+#ifdef EIGEN_UMFPACK_SUPPORT
+  #include "umfpack.h"
+#endif
+
 namespace Eigen {
 
 #include "src/Sparse/SparseUtil.h"
 #include "src/Sparse/SparseMatrixBase.h"
 #include "src/Sparse/SparseArray.h"
+#include "src/Sparse/AmbiVector.h"
+#include "src/Sparse/RandomSetter.h"
 #include "src/Sparse/SparseBlock.h"
 #include "src/Sparse/SparseMatrix.h"
-#include "src/Sparse/HashMatrix.h"
-#include "src/Sparse/LinkedVectorMatrix.h"
+#include "src/Sparse/SparseVector.h"
 #include "src/Sparse/CoreIterators.h"
-#include "src/Sparse/SparseSetter.h"
 #include "src/Sparse/SparseProduct.h"
 #include "src/Sparse/TriangularSolver.h"
+#include "src/Sparse/SparseLLT.h"
+#include "src/Sparse/SparseLDLT.h"
+#include "src/Sparse/SparseLU.h"
+
+#ifdef EIGEN_CHOLMOD_SUPPORT
+# include "src/Sparse/CholmodSupport.h"
+#endif
+
+#ifdef EIGEN_TAUCS_SUPPORT
+# include "src/Sparse/TaucsSupport.h"
+#endif
+
+#ifdef EIGEN_SUPERLU_SUPPORT
+# include "src/Sparse/SuperLUSupport.h"
+#endif
+
+#ifdef EIGEN_UMFPACK_SUPPORT
+# include "src/Sparse/UmfPackSupport.h"
+#endif
 
 } // namespace Eigen
 
+#include "src/Core/util/EnableMSVCWarnings.h"
+
 #endif // EIGEN_SPARSE_MODULE_H

Eigen/src/Array/AllAndAny.h

@@ -99,8 +99,8 @@ inline bool MatrixBase<Derived>::all(void) const
      >::run(derived());
   else
   {
-    for(int j = 0; j < cols(); j++)
-      for(int i = 0; i < rows(); i++)
+    for(int j = 0; j < cols(); ++j)
+      for(int i = 0; i < rows(); ++i)
         if (!coeff(i, j)) return false;
     return true;
   }
@@ -123,8 +123,8 @@ inline bool MatrixBase<Derived>::any(void) const
            >::run(derived());
   else
   {
-    for(int j = 0; j < cols(); j++)
-      for(int i = 0; i < rows(); i++)
+    for(int j = 0; j < cols(); ++j)
+      for(int i = 0; i < rows(); ++i)
         if (coeff(i, j)) return true;
     return false;
   }

Eigen/src/Array/Norms.h

@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// 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_ARRAY_NORMS_H
+#define EIGEN_ARRAY_NORMS_H
+
+template<typename Derived, int p>
+struct ei_lpNorm_selector
+{
+  typedef typename NumTraits<typename ei_traits<Derived>::Scalar>::Real RealScalar;
+  inline static RealScalar run(const MatrixBase<Derived>& m)
+  {
+    return ei_pow(m.cwise().abs().cwise().pow(p).sum(), RealScalar(1)/p);
+  }
+};
+
+template<typename Derived>
+struct ei_lpNorm_selector<Derived, 1>
+{
+  inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
+    return m.cwise().abs().sum();
+  }
+};
+
+template<typename Derived>
+struct ei_lpNorm_selector<Derived, 2>
+{
+  inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
+    return m.norm();
+  }
+};
+
+template<typename Derived>
+struct ei_lpNorm_selector<Derived, Infinity>
+{
+  inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
+    return m.cwise().abs().maxCoeff();
+  }
+};
+
+/** \array_module
+  * 
+  * \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
+  *          of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^p\infty \f$
+  *          norm, that is the maximum of the absolute values of the coefficients of *this.
+  *
+  * \sa norm()
+  */
+template<typename Derived>
+template<int p>
+inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::lpNorm() const
+{
+  return ei_lpNorm_selector<Derived, p>::run(*this);
+}
+
+#endif // EIGEN_ARRAY_NORMS_H

Eigen/src/Array/PartialRedux.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -107,7 +107,7 @@ class PartialReduxExpr : ei_no_assignment_operator,
     { return mat.MEMBER(); }                                        \
   }
 
-EIGEN_MEMBER_FUNCTOR(norm2, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
 EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
 EIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost);
 EIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost);
@@ -172,6 +172,8 @@ template<typename ExpressionType, int Direction> class PartialRedux
                               > Type;
     };
 
+    typedef typename ExpressionType::PlainMatrixType CrossReturnType;
+
     inline PartialRedux(const ExpressionType& matrix) : m_matrix(matrix) {}
 
     /** \internal */
@@ -204,11 +206,11 @@ template<typename ExpressionType, int Direction> class PartialRedux
     /** \returns a row (or column) vector expression of the squared norm
       * of each column (or row) of the referenced expression.
       *
-      * Example: \include PartialRedux_norm2.cpp
-      * Output: \verbinclude PartialRedux_norm2.out
+      * Example: \include PartialRedux_squaredNorm.cpp
+      * Output: \verbinclude PartialRedux_squaredNorm.out
       *
-      * \sa MatrixBase::norm2() */
-    const typename ReturnType<ei_member_norm2>::Type norm2() const
+      * \sa MatrixBase::squaredNorm() */
+    const typename ReturnType<ei_member_squaredNorm>::Type squaredNorm() const
     { return _expression(); }
 
     /** \returns a row (or column) vector expression of the norm
@@ -245,6 +247,32 @@ template<typename ExpressionType, int Direction> class PartialRedux
     const typename ReturnType<ei_member_any>::Type any() const
     { return _expression(); }
 
+    /** \returns a 3x3 matrix expression of the cross product
+      * of each column or row of the referenced expression with the \a other vector.
+      *
+      * \geometry_module
+      *
+      * \sa MatrixBase::cross() */
+    template<typename OtherDerived>
+    const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(CrossReturnType,3,3)
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
+      EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+      if(Direction==Vertical)
+        return (CrossReturnType()
+                                 << _expression().col(0).cross(other),
+                                    _expression().col(1).cross(other),
+                                    _expression().col(2).cross(other)).finished();
+      else
+        return (CrossReturnType() 
+                                 << _expression().row(0).cross(other),
+                                    _expression().row(1).cross(other),
+                                    _expression().row(2).cross(other)).finished();
+    }
+
   protected:
     ExpressionTypeNested m_matrix;
 };

Eigen/src/Cholesky/Cholesky.h

@@ -29,22 +29,7 @@
   *
   * \class Cholesky
   *
-  * \brief Standard Cholesky decomposition of a matrix and associated features
-  *
-  * \param MatrixType the type of the matrix of which we are computing the Cholesky decomposition
-  *
-  * This class performs a standard Cholesky decomposition of a symmetric, positive definite
-  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
-  *
-  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
-  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
-  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
-  * situations like generalised eigen problems with hermitian matrices.
-  *
-  * 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.
-  *
-  * \sa MatrixBase::cholesky(), class CholeskyWithoutSquareRoot
+  * \deprecated this class has been renamed LLT
   */
 template<typename MatrixType> class Cholesky
 {
@@ -59,20 +44,27 @@ template<typename MatrixType> class Cholesky
     };
 
   public:
-  
+
     Cholesky(const MatrixType& matrix)
       : m_matrix(matrix.rows(), matrix.cols())
     {
       compute(matrix);
     }
 
-    inline Part<MatrixType, Lower> matrixL(void) const { return m_matrix; }
+		/** \deprecated */
+    inline Part<MatrixType, LowerTriangular> matrixL(void) const { return m_matrix; }
 
-    /** \returns true if the matrix is positive definite */
+    /** \deprecated */
     inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
 
     template<typename Derived>
-    typename Derived::Eval solve(const MatrixBase<Derived> &b) const;
+    EIGEN_DEPRECATED typename MatrixBase<Derived>::PlainMatrixType_ColMajor solve(const MatrixBase<Derived> &b) const;
+
+    template<typename RhsDerived, typename ResDerived>
+    bool solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const;
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
 
     void compute(const MatrixType& matrix);
 
@@ -85,8 +77,7 @@ template<typename MatrixType> class Cholesky
     bool m_isPositiveDefinite;
 };
 
-/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
-  */
+/** \deprecated */
 template<typename MatrixType>
 void Cholesky<MatrixType>::compute(const MatrixType& a)
 {
@@ -102,7 +93,7 @@ void Cholesky<MatrixType>::compute(const MatrixType& a)
   m_matrix.col(0).end(size-1) = a.row(0).end(size-1).adjoint() / ei_real(m_matrix.coeff(0,0));
   for (int j = 1; j < size; ++j)
   {
-    Scalar tmp = ei_real(a.coeff(j,j)) - m_matrix.row(j).start(j).norm2();
+    Scalar tmp = ei_real(a.coeff(j,j)) - m_matrix.row(j).start(j).squaredNorm();
     x = ei_real(tmp);
     if (x < eps || (!ei_isMuchSmallerThan(ei_imag(tmp), RealScalar(1))))
     {
@@ -125,34 +116,50 @@ void Cholesky<MatrixType>::compute(const MatrixType& a)
   }
 }
 
-/** \returns the solution of \f$ A x = b \f$ using the current decomposition of A.
-  * In other words, it returns \f$ A^{-1} b \f$ computing
-  * \f$ {L^{*}}^{-1} L^{-1} b \f$ from right to left.
-  * \param b the column vector \f$ b \f$, which can also be a matrix.
-  *
-  * Example: \include Cholesky_solve.cpp
-  * Output: \verbinclude Cholesky_solve.out
-  *
-  * \sa MatrixBase::cholesky(), CholeskyWithoutSquareRoot::solve()
-  */
+/** \deprecated */
 template<typename MatrixType>
 template<typename Derived>
-typename Derived::Eval Cholesky<MatrixType>::solve(const MatrixBase<Derived> &b) const
+typename MatrixBase<Derived>::PlainMatrixType_ColMajor Cholesky<MatrixType>::solve(const MatrixBase<Derived> &b) const
 {
   const int size = m_matrix.rows();
   ei_assert(size==b.rows());
+  typename MatrixBase<Derived>::PlainMatrixType_ColMajor x(b);
+  solveInPlace(x);
+  return x;
+}
 
-  return m_matrix.adjoint().template part<Upper>().solveTriangular(matrixL().solveTriangular(b));
+/** \deprecated */
+template<typename MatrixType>
+template<typename RhsDerived, typename ResDerived>
+bool Cholesky<MatrixType>::solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows() && "Cholesky::solve(): invalid number of rows of the right hand side matrix b");
+  return solveInPlace((*result) = b);
+}
+
+/** \deprecated */
+template<typename MatrixType>
+template<typename Derived>
+bool Cholesky<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==bAndX.rows());
+  if (!m_isPositiveDefinite)
+    return false;
+  matrixL().solveTriangularInPlace(bAndX);
+  m_matrix.adjoint().template part<UpperTriangular>().solveTriangularInPlace(bAndX);
+  return true;
 }
 
 /** \cholesky_module
-  * \returns the Cholesky decomposition of \c *this
+  * \deprecated has been renamed llt()
   */
 template<typename Derived>
-inline const Cholesky<typename MatrixBase<Derived>::EvalType>
+inline const Cholesky<typename MatrixBase<Derived>::PlainMatrixType>
 MatrixBase<Derived>::cholesky() const
 {
-  return Cholesky<typename ei_eval<Derived>::type>(derived());
+  return Cholesky<PlainMatrixType>(derived());
 }
 
 #endif // EIGEN_CHOLESKY_H

Eigen/src/Cholesky/CholeskyWithoutSquareRoot.h

@@ -25,25 +25,11 @@
 #ifndef EIGEN_CHOLESKY_WITHOUT_SQUARE_ROOT_H
 #define EIGEN_CHOLESKY_WITHOUT_SQUARE_ROOT_H
 
-/** \ingroup Cholesky_Module
+/** \deprecated \ingroup Cholesky_Module
   *
   * \class CholeskyWithoutSquareRoot
   *
-  * \brief Robust Cholesky decomposition of a matrix and associated features
-  *
-  * \param MatrixType the type of the matrix of which we are computing the Cholesky decomposition
-  *
-  * This class performs a Cholesky decomposition without square root of a symmetric, positive definite
-  * matrix A such that A = L D L^* = U^* D U, where L is lower triangular with a unit diagonal
-  * and D is a diagonal matrix.
-  *
-  * Compared to a standard Cholesky decomposition, avoiding the square roots allows for faster and more
-  * stable computation.
-  *
-  * 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.
-  *
-  * \sa MatrixBase::choleskyNoSqrt(), class Cholesky
+  * \deprecated this class has been renamed LDLT
   */
 template<typename MatrixType> class CholeskyWithoutSquareRoot
 {
@@ -60,7 +46,7 @@ template<typename MatrixType> class CholeskyWithoutSquareRoot
     }
 
     /** \returns the lower triangular matrix L */
-    inline Part<MatrixType, UnitLower> matrixL(void) const { return m_matrix; }
+    inline Part<MatrixType, UnitLowerTriangular> matrixL(void) const { return m_matrix; }
 
     /** \returns the coefficients of the diagonal matrix D */
     inline DiagonalCoeffs<MatrixType> vectorD(void) const { return m_matrix.diagonal(); }
@@ -69,7 +55,13 @@ template<typename MatrixType> class CholeskyWithoutSquareRoot
     inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
 
     template<typename Derived>
-    typename Derived::Eval solve(const MatrixBase<Derived> &b) const;
+    EIGEN_DEPRECATED typename Derived::Eval solve(const MatrixBase<Derived> &b) const;
+
+    template<typename RhsDerived, typename ResDerived>
+    bool solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const;
+
+		template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
 
     void compute(const MatrixType& matrix);
 
@@ -85,8 +77,7 @@ template<typename MatrixType> class CholeskyWithoutSquareRoot
     bool m_isPositiveDefinite;
 };
 
-/** Compute / recompute the Cholesky decomposition A = L D L^* = U^* D U of \a matrix
-  */
+/** \deprecated */
 template<typename MatrixType>
 void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
 {
@@ -101,7 +92,7 @@ void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
     m_matrix = a;
     return;
   }
-  
+
   // Let's preallocate a temporay vector to evaluate the matrix-vector product into it.
   // Unlike the standard Cholesky decomposition, here we cannot evaluate it to the destination
   // matrix because it a sub-row which is not compatible suitable for efficient packet evaluation.
@@ -138,15 +129,7 @@ void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
   }
 }
 
-/** \returns the solution of \f$ A x = b \f$ using the current decomposition of A.
-  * In other words, it returns \f$ A^{-1} b \f$ computing
-  * \f$ {L^{*}}^{-1} D^{-1} L^{-1} b \f$ from right to left.
-  * \param b the column vector \f$ b \f$, which can also be a matrix.
-  *
-  * See Cholesky::solve() for a example.
-  * 
-  * \sa MatrixBase::choleskyNoSqrt()
-  */
+/** \deprecated */
 template<typename MatrixType>
 template<typename Derived>
 typename Derived::Eval CholeskyWithoutSquareRoot<MatrixType>::solve(const MatrixBase<Derived> &b) const
@@ -154,18 +137,45 @@ typename Derived::Eval CholeskyWithoutSquareRoot<MatrixType>::solve(const Matrix
   const int size = m_matrix.rows();
   ei_assert(size==b.rows());
 
-  return m_matrix.adjoint().template part<UnitUpper>()
+  return m_matrix.adjoint().template part<UnitUpperTriangular>()
     .solveTriangular(
       (  m_matrix.cwise().inverse().template part<Diagonal>()
        * matrixL().solveTriangular(b))
      );
 }
 
+/** \deprecated */
+template<typename MatrixType>
+template<typename RhsDerived, typename ResDerived>
+bool CholeskyWithoutSquareRoot<MatrixType>
+::solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows() && "Cholesky::solve(): invalid number of rows of the right hand side matrix b");
+  *result = b;
+  return solveInPlace(*result);
+}
+
+/** \deprecated */
+template<typename MatrixType>
+template<typename Derived>
+bool CholeskyWithoutSquareRoot<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==bAndX.rows());
+  if (!m_isPositiveDefinite)
+    return false;
+  matrixL().solveTriangularInPlace(bAndX);
+  bAndX = (m_matrix.cwise().inverse().template part<Diagonal>() * bAndX).lazy();
+  m_matrix.adjoint().template part<UnitUpperTriangular>().solveTriangularInPlace(bAndX);
+  return true;
+}
+
 /** \cholesky_module
-  * \returns the Cholesky decomposition without square root of \c *this
+  * \deprecated has been renamed ldlt()
   */
 template<typename Derived>
-inline const CholeskyWithoutSquareRoot<typename MatrixBase<Derived>::EvalType>
+inline const CholeskyWithoutSquareRoot<typename MatrixBase<Derived>::PlainMatrixType>
 MatrixBase<Derived>::choleskyNoSqrt() const
 {
   return derived();

Eigen/src/Cholesky/LDLT.h

@@ -0,0 +1,198 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_LDLT_H
+#define EIGEN_LDLT_H
+
+/** \ingroup cholesky_Module
+  *
+  * \class LDLT
+  *
+  * \brief Robust Cholesky decomposition of a matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LDL^T Cholesky decomposition
+  *
+  * This class performs a Cholesky decomposition without square root of a symmetric, positive definite
+  * matrix A such that A = L D L^* = U^* D U, where L is lower triangular with a unit diagonal
+  * and D is a diagonal matrix.
+  *
+  * Compared to a standard Cholesky decomposition, avoiding the square roots allows for faster and more
+  * stable computation.
+  *
+  * 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.
+  *
+  * \sa MatrixBase::ldlt(), class LLT
+  */
+template<typename MatrixType> class LDLT
+{
+  public:
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
+
+    LDLT(const MatrixType& matrix)
+      : m_matrix(matrix.rows(), matrix.cols())
+    {
+      compute(matrix);
+    }
+
+    /** \returns the lower triangular matrix L */
+    inline Part<MatrixType, UnitLowerTriangular> matrixL(void) const { return m_matrix; }
+
+    /** \returns the coefficients of the diagonal matrix D */
+    inline DiagonalCoeffs<MatrixType> vectorD(void) const { return m_matrix.diagonal(); }
+
+    /** \returns true if the matrix is positive definite */
+    inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
+
+    template<typename RhsDerived, typename ResDerived>
+    bool solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const;
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
+
+    void compute(const MatrixType& matrix);
+
+  protected:
+    /** \internal
+      * Used to compute and store the cholesky decomposition A = L D L^* = U^* D U.
+      * The strict upper part is used during the decomposition, the strict lower
+      * part correspond to the coefficients of L (its diagonal is equal to 1 and
+      * is not stored), and the diagonal entries correspond to D.
+      */
+    MatrixType m_matrix;
+
+    bool m_isPositiveDefinite;
+};
+
+/** Compute / recompute the LLT decomposition A = L D L^* = U^* D U of \a matrix
+  */
+template<typename MatrixType>
+void LDLT<MatrixType>::compute(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
+  const int size = a.rows();
+  m_matrix.resize(size, size);
+  m_isPositiveDefinite = true;
+  const RealScalar eps = ei_sqrt(precision<Scalar>());
+
+  if (size<=1)
+  {
+    m_matrix = a;
+    return;
+  }
+
+  // Let's preallocate a temporay vector to evaluate the matrix-vector product into it.
+  // Unlike the standard LLT decomposition, here we cannot evaluate it to the destination
+  // matrix because it a sub-row which is not compatible suitable for efficient packet evaluation.
+  // (at least if we assume the matrix is col-major)
+  Matrix<Scalar,MatrixType::RowsAtCompileTime,1> _temporary(size);
+
+  // Note that, in this algorithm the rows of the strict upper part of m_matrix is used to store
+  // column vector, thus the strange .conjugate() and .transpose()...
+
+  m_matrix.row(0) = a.row(0).conjugate();
+  m_matrix.col(0).end(size-1) = m_matrix.row(0).end(size-1) / m_matrix.coeff(0,0);
+  for (int j = 1; j < size; ++j)
+  {
+    RealScalar tmp = ei_real(a.coeff(j,j) - (m_matrix.row(j).start(j) * m_matrix.col(j).start(j).conjugate()).coeff(0,0));
+    m_matrix.coeffRef(j,j) = tmp;
+
+    if (tmp < eps)
+    {
+      m_isPositiveDefinite = false;
+      return;
+    }
+
+    int endSize = size-j-1;
+    if (endSize>0)
+    {
+      _temporary.end(endSize) = ( m_matrix.block(j+1,0, endSize, j)
+                                  * m_matrix.col(j).start(j).conjugate() ).lazy();
+
+      m_matrix.row(j).end(endSize) = a.row(j).end(endSize).conjugate()
+                                   - _temporary.end(endSize).transpose();
+
+      m_matrix.col(j).end(endSize) = m_matrix.row(j).end(endSize) / tmp;
+    }
+  }
+}
+
+/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.
+  * The result is stored in \a result
+  *
+  * \returns true in case of success, false otherwise.
+  *
+  * In other words, it computes \f$ b = A^{-1} b \f$ with
+  * \f$ {L^{*}}^{-1} D^{-1} L^{-1} b \f$ from right to left.
+  *
+  * \sa LDLT::solveInPlace(), MatrixBase::ldlt()
+  */
+template<typename MatrixType>
+template<typename RhsDerived, typename ResDerived>
+bool LDLT<MatrixType>
+::solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows() && "LLT::solve(): invalid number of rows of the right hand side matrix b");
+  *result = b;
+  return solveInPlace(*result);
+}
+
+/** 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.
+  *
+  * \sa LDLT::solve(), MatrixBase::ldlt()
+  */
+template<typename MatrixType>
+template<typename Derived>
+bool LDLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==bAndX.rows());
+  if (!m_isPositiveDefinite)
+    return false;
+  matrixL().solveTriangularInPlace(bAndX);
+  bAndX = (m_matrix.cwise().inverse().template part<Diagonal>() * bAndX).lazy();
+  m_matrix.adjoint().template part<UnitUpperTriangular>().solveTriangularInPlace(bAndX);
+  return true;
+}
+
+/** \cholesky_module
+  * \returns the Cholesky decomposition without square root of \c *this
+  */
+template<typename Derived>
+inline const LDLT<typename MatrixBase<Derived>::PlainMatrixType>
+MatrixBase<Derived>::ldlt() const
+{
+  return derived();
+}
+
+#endif // EIGEN_LDLT_H

Eigen/src/Cholesky/LLT.h

@@ -0,0 +1,186 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_LLT_H
+#define EIGEN_LLT_H
+
+/** \ingroup cholesky_Module
+  *
+  * \class LLT
+  *
+  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
+  *
+  * 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.
+  *
+  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
+  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+  * situations like generalised eigen problems with hermitian matrices.
+  *
+  * 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.
+  *
+  * \sa MatrixBase::llt(), class LDLT
+  */
+template<typename MatrixType> class LLT
+{
+  private:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
+
+    enum {
+      PacketSize = ei_packet_traits<Scalar>::size,
+      AlignmentMask = int(PacketSize)-1
+    };
+
+  public:
+
+    LLT(const MatrixType& matrix)
+      : m_matrix(matrix.rows(), matrix.cols())
+    {
+      compute(matrix);
+    }
+
+    /** \returns the lower triangular matrix L */
+    inline Part<MatrixType, LowerTriangular> matrixL(void) const { return m_matrix; }
+
+    /** \returns true if the matrix is positive definite */
+    inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
+
+    template<typename RhsDerived, typename ResDerived>
+    bool solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const;
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
+
+    void compute(const MatrixType& matrix);
+
+  protected:
+    /** \internal
+      * Used to compute and store L
+      * The strict upper part is not used and even not initialized.
+      */
+    MatrixType m_matrix;
+    bool m_isPositiveDefinite;
+};
+
+/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
+  */
+template<typename MatrixType>
+void LLT<MatrixType>::compute(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
+  const int size = a.rows();
+  m_matrix.resize(size, size);
+  const RealScalar eps = ei_sqrt(precision<Scalar>());
+
+  RealScalar x;
+  x = ei_real(a.coeff(0,0));
+  m_isPositiveDefinite = x > eps && ei_isMuchSmallerThan(ei_imag(a.coeff(0,0)), RealScalar(1));
+  m_matrix.coeffRef(0,0) = ei_sqrt(x);
+  m_matrix.col(0).end(size-1) = a.row(0).end(size-1).adjoint() / ei_real(m_matrix.coeff(0,0));
+  for (int j = 1; j < size; ++j)
+  {
+    Scalar tmp = ei_real(a.coeff(j,j)) - m_matrix.row(j).start(j).squaredNorm();
+    x = ei_real(tmp);
+    if (x < eps || (!ei_isMuchSmallerThan(ei_imag(tmp), RealScalar(1))))
+    {
+      m_isPositiveDefinite = false;
+      return;
+    }
+    m_matrix.coeffRef(j,j) = x = ei_sqrt(x);
+
+    int endSize = size-j-1;
+    if (endSize>0) {
+      // Note that when all matrix columns have good alignment, then the following
+      // product is guaranteed to be optimal with respect to alignment.
+      m_matrix.col(j).end(endSize) =
+        (m_matrix.block(j+1, 0, endSize, j) * m_matrix.row(j).start(j).adjoint()).lazy();
+
+      // FIXME could use a.col instead of a.row
+      m_matrix.col(j).end(endSize) = (a.row(j).end(endSize).adjoint()
+        - m_matrix.col(j).end(endSize) ) / x;
+    }
+  }
+}
+
+/** Computes the solution x of \f$ A x = b \f$ using the current decomposition of A.
+  * The result is stored in \a result
+  *
+  * \returns true in case of success, false otherwise.
+  *
+  * In other words, it computes \f$ b = A^{-1} b \f$ with
+  * \f$ {L^{*}}^{-1} L^{-1} b \f$ from right to left.
+  *
+  * Example: \include LLT_solve.cpp
+  * Output: \verbinclude LLT_solve.out
+  *
+  * \sa LLT::solveInPlace(), MatrixBase::llt()
+  */
+template<typename MatrixType>
+template<typename RhsDerived, typename ResDerived>
+bool LLT<MatrixType>::solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows() && "LLT::solve(): invalid number of rows of the right hand side matrix b");
+  return solveInPlace((*result) = b);
+}
+
+/** 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.
+  *
+  * \sa LLT::solve(), MatrixBase::llt()
+  */
+template<typename MatrixType>
+template<typename Derived>
+bool LLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==bAndX.rows());
+  if (!m_isPositiveDefinite)
+    return false;
+  matrixL().solveTriangularInPlace(bAndX);
+  m_matrix.adjoint().template part<UpperTriangular>().solveTriangularInPlace(bAndX);
+  return true;
+}
+
+/** \cholesky_module
+  * \returns the LLT decomposition of \c *this
+  */
+template<typename Derived>
+inline const LLT<typename MatrixBase<Derived>::PlainMatrixType>
+MatrixBase<Derived>::llt() const
+{
+  return LLT<PlainMatrixType>(derived());
+}
+
+#endif // EIGEN_LLT_H

Eigen/src/Core/Assign.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -112,7 +112,7 @@ struct ei_assign_novec_CompleteUnrolling
         : Index / Derived1::RowsAtCompileTime
   };
 
-  inline static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     dst.copyCoeff(row, col, src);
     ei_assign_novec_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
@@ -122,13 +122,13 @@ struct ei_assign_novec_CompleteUnrolling
 template<typename Derived1, typename Derived2, int Stop>
 struct ei_assign_novec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
 {
-  inline static void run(Derived1 &, const Derived2 &) {}
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
 };
 
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct ei_assign_novec_InnerUnrolling
 {
-  inline static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
   {
     const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
     const int row = rowMajor ? row_or_col : Index;
@@ -141,7 +141,7 @@ struct ei_assign_novec_InnerUnrolling
 template<typename Derived1, typename Derived2, int Stop>
 struct ei_assign_novec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
 {
-  inline static void run(Derived1 &, const Derived2 &, int) {}
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
 };
 
 /**************************
@@ -161,7 +161,7 @@ struct ei_assign_innervec_CompleteUnrolling
     SrcAlignment = ei_assign_traits<Derived1,Derived2>::SrcAlignment
   };
 
-  inline static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     dst.template copyPacket<Derived2, Aligned, SrcAlignment>(row, col, src);
     ei_assign_innervec_CompleteUnrolling<Derived1, Derived2,
@@ -172,13 +172,13 @@ struct ei_assign_innervec_CompleteUnrolling
 template<typename Derived1, typename Derived2, int Stop>
 struct ei_assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
 {
-  inline static void run(Derived1 &, const Derived2 &) {}
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
 };
 
 template<typename Derived1, typename Derived2, int Index, int Stop>
 struct ei_assign_innervec_InnerUnrolling
 {
-  inline static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int row_or_col)
   {
     const int row = int(Derived1::Flags)&RowMajorBit ? row_or_col : Index;
     const int col = int(Derived1::Flags)&RowMajorBit ? Index : row_or_col;
@@ -191,7 +191,7 @@ struct ei_assign_innervec_InnerUnrolling
 template<typename Derived1, typename Derived2, int Stop>
 struct ei_assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
 {
-  inline static void run(Derived1 &, const Derived2 &, int) {}
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
 };
 
 /***************************************************************************
@@ -210,12 +210,12 @@ struct ei_assign_impl;
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, NoVectorization, NoUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  inline static void run(Derived1 &dst, const Derived2 &src)
   {
     const int innerSize = dst.innerSize();
     const int outerSize = dst.outerSize();
-    for(int j = 0; j < outerSize; j++)
-      for(int i = 0; i < innerSize; i++)
+    for(int j = 0; j < outerSize; ++j)
+      for(int i = 0; i < innerSize; ++i)
       {
         if(int(Derived1::Flags)&RowMajorBit)
           dst.copyCoeff(j, i, src);
@@ -228,7 +228,7 @@ struct ei_assign_impl<Derived1, Derived2, NoVectorization, NoUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, NoVectorization, CompleteUnrolling>
 {
-  inline static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     ei_assign_novec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
       ::run(dst, src);
@@ -238,12 +238,12 @@ struct ei_assign_impl<Derived1, Derived2, NoVectorization, CompleteUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, NoVectorization, InnerUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
     const int innerSize = rowMajor ? Derived1::ColsAtCompileTime : Derived1::RowsAtCompileTime;
     const int outerSize = dst.outerSize();
-    for(int j = 0; j < outerSize; j++)
+    for(int j = 0; j < outerSize; ++j)
       ei_assign_novec_InnerUnrolling<Derived1, Derived2, 0, innerSize>
         ::run(dst, src, j);
   }
@@ -256,12 +256,12 @@ struct ei_assign_impl<Derived1, Derived2, NoVectorization, InnerUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, InnerVectorization, NoUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  inline static void run(Derived1 &dst, const Derived2 &src)
   {
     const int innerSize = dst.innerSize();
     const int outerSize = dst.outerSize();
     const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
-    for(int j = 0; j < outerSize; j++)
+    for(int j = 0; j < outerSize; ++j)
       for(int i = 0; i < innerSize; i+=packetSize)
       {
         if(int(Derived1::Flags)&RowMajorBit)
@@ -275,7 +275,7 @@ struct ei_assign_impl<Derived1, Derived2, InnerVectorization, NoUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, InnerVectorization, CompleteUnrolling>
 {
-  inline static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     ei_assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
       ::run(dst, src);
@@ -285,12 +285,12 @@ struct ei_assign_impl<Derived1, Derived2, InnerVectorization, CompleteUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, InnerVectorization, InnerUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     const bool rowMajor = int(Derived1::Flags)&RowMajorBit;
     const int innerSize = rowMajor ? Derived1::ColsAtCompileTime : Derived1::RowsAtCompileTime;
     const int outerSize = dst.outerSize();
-    for(int j = 0; j < outerSize; j++)
+    for(int j = 0; j < outerSize; ++j)
       ei_assign_innervec_InnerUnrolling<Derived1, Derived2, 0, innerSize>
         ::run(dst, src, j);
   }
@@ -303,7 +303,7 @@ struct ei_assign_impl<Derived1, Derived2, InnerVectorization, InnerUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  inline static void run(Derived1 &dst, const Derived2 &src)
   {
     const int size = dst.size();
     const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
@@ -311,7 +311,7 @@ struct ei_assign_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
                            : ei_alignmentOffset(&dst.coeffRef(0), size);
     const int alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
 
-    for(int index = 0; index < alignedStart; index++)
+    for(int index = 0; index < alignedStart; ++index)
       dst.copyCoeff(index, src);
 
     for(int index = alignedStart; index < alignedEnd; index += packetSize)
@@ -319,7 +319,7 @@ struct ei_assign_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
       dst.template copyPacket<Derived2, Aligned, ei_assign_traits<Derived1,Derived2>::SrcAlignment>(index, src);
     }
 
-    for(int index = alignedEnd; index < size; index++)
+    for(int index = alignedEnd; index < size; ++index)
       dst.copyCoeff(index, src);
   }
 };
@@ -327,7 +327,7 @@ struct ei_assign_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, LinearVectorization, CompleteUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
   {
     const int size = Derived1::SizeAtCompileTime;
     const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
@@ -345,7 +345,7 @@ struct ei_assign_impl<Derived1, Derived2, LinearVectorization, CompleteUnrolling
 template<typename Derived1, typename Derived2>
 struct ei_assign_impl<Derived1, Derived2, SliceVectorization, NoUnrolling>
 {
-  static void run(Derived1 &dst, const Derived2 &src)
+  inline static void run(Derived1 &dst, const Derived2 &src)
   {
     const int packetSize = ei_packet_traits<typename Derived1::Scalar>::size;
     const int packetAlignedMask = packetSize - 1;
@@ -355,12 +355,12 @@ struct ei_assign_impl<Derived1, Derived2, SliceVectorization, NoUnrolling>
     int alignedStart = ei_assign_traits<Derived1,Derived2>::DstIsAligned ? 0
                      : ei_alignmentOffset(&dst.coeffRef(0), innerSize);
 
-    for(int i = 0; i < outerSize; i++)
+    for(int i = 0; i < outerSize; ++i)
     {
       const int alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
 
       // do the non-vectorizable part of the assignment
-      for (int index = 0; index<alignedStart ; index++)
+      for (int index = 0; index<alignedStart ; ++index)
       {
         if(Derived1::Flags&RowMajorBit)
           dst.copyCoeff(i, index, src);
@@ -378,7 +378,7 @@ struct ei_assign_impl<Derived1, Derived2, SliceVectorization, NoUnrolling>
       }
 
       // do the non-vectorizable part of the assignment
-      for (int index = alignedEnd; index<innerSize ; index++)
+      for (int index = alignedEnd; index<innerSize ; ++index)
       {
         if(Derived1::Flags&RowMajorBit)
           dst.copyCoeff(i, index, src);
@@ -397,17 +397,17 @@ struct ei_assign_impl<Derived1, Derived2, SliceVectorization, NoUnrolling>
 
 template<typename Derived>
 template<typename OtherDerived>
-inline Derived& MatrixBase<Derived>
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>
   ::lazyAssign(const MatrixBase<OtherDerived>& other)
 {
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived);
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
   ei_assert(rows() == other.rows() && cols() == other.cols());
   ei_assign_impl<Derived, OtherDerived>::run(derived(),other.derived());
   return derived();
 }
 
 template<typename Derived, typename OtherDerived,
-         bool EvalBeforeAssigning = int(OtherDerived::Flags) & EvalBeforeAssigningBit,
+         bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
          bool NeedToTranspose = Derived::IsVectorAtCompileTime
                 && OtherDerived::IsVectorAtCompileTime
                 && int(Derived::RowsAtCompileTime) == int(OtherDerived::ColsAtCompileTime)
@@ -417,26 +417,28 @@ struct ei_assign_selector;
 
 template<typename Derived, typename OtherDerived>
 struct ei_assign_selector<Derived,OtherDerived,false,false> {
-  static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
 };
 template<typename Derived, typename OtherDerived>
 struct ei_assign_selector<Derived,OtherDerived,true,false> {
-  static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
 };
 template<typename Derived, typename OtherDerived>
 struct ei_assign_selector<Derived,OtherDerived,false,true> {
-  static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
 };
 template<typename Derived, typename OtherDerived>
 struct ei_assign_selector<Derived,OtherDerived,true,true> {
-  static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
 };
 
 template<typename Derived>
 template<typename OtherDerived>
-inline Derived& MatrixBase<Derived>
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>
   ::operator=(const MatrixBase<OtherDerived>& other)
 {
+  EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
   return ei_assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
 }
 

Eigen/src/Core/Block.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -122,7 +122,7 @@ template<typename MatrixType, int BlockRows, int BlockCols, int PacketAccess, in
       : m_matrix(matrix), m_startRow(startRow), m_startCol(startCol),
         m_blockRows(matrix.rows()), m_blockCols(matrix.cols())
     {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && RowsAtCompileTime!=Dynamic,this_method_is_only_for_fixed_size);
+      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && RowsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
       ei_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= matrix.rows()
           && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= matrix.cols());
     }
@@ -318,41 +318,41 @@ inline const typename BlockReturnType<Derived>::Type MatrixBase<Derived>
   return typename BlockReturnType<Derived>::Type(derived(), startRow, startCol, blockRows, blockCols);
 }
 
-/** \returns a dynamic-size expression of a block in *this.
+/** \returns a dynamic-size expression of a segment (i.e. a vector block) in *this.
   *
   * \only_for_vectors
   *
-  * \addexample BlockIntInt \label How to reference a sub-vector (dynamic size)
+  * \addexample SegmentIntInt \label How to reference a sub-vector (dynamic size)
   *
-  * \param start the first coefficient in the block
-  * \param size the number of coefficients in the block
+  * \param start the first coefficient in the segment
+  * \param size the number of coefficients in the segment
   *
-  * Example: \include MatrixBase_block_int_int.cpp
-  * Output: \verbinclude MatrixBase_block_int_int.out
+  * Example: \include MatrixBase_segment_int_int.cpp
+  * Output: \verbinclude MatrixBase_segment_int_int.out
   *
   * \note Even though the returned expression has dynamic size, in the case
   * when it is applied to a fixed-size vector, it inherits a fixed maximal size,
   * which means that evaluating it does not cause a dynamic memory allocation.
   *
-  * \sa class Block, block(int)
+  * \sa class Block, segment(int)
   */
 template<typename Derived>
 inline typename BlockReturnType<Derived>::SubVectorType MatrixBase<Derived>
-  ::block(int start, int size)
+  ::segment(int start, int size)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return typename BlockReturnType<Derived>::SubVectorType(derived(), RowsAtCompileTime == 1 ? 0 : start,
                                    ColsAtCompileTime == 1 ? 0 : start,
                                    RowsAtCompileTime == 1 ? 1 : size,
                                    ColsAtCompileTime == 1 ? 1 : size);
 }
 
-/** This is the const version of block(int,int).*/
+/** This is the const version of segment(int,int).*/
 template<typename Derived>
 inline const typename BlockReturnType<Derived>::SubVectorType
-MatrixBase<Derived>::block(int start, int size) const
+MatrixBase<Derived>::segment(int start, int size) const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return typename BlockReturnType<Derived>::SubVectorType(derived(), RowsAtCompileTime == 1 ? 0 : start,
                                    ColsAtCompileTime == 1 ? 0 : start,
                                    RowsAtCompileTime == 1 ? 1 : size,
@@ -380,7 +380,7 @@ template<typename Derived>
 inline typename BlockReturnType<Derived,Dynamic>::SubVectorType
 MatrixBase<Derived>::start(int size)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,
                RowsAtCompileTime == 1 ? 1 : Dynamic,
                ColsAtCompileTime == 1 ? 1 : Dynamic>
@@ -394,7 +394,7 @@ template<typename Derived>
 inline const typename BlockReturnType<Derived,Dynamic>::SubVectorType
 MatrixBase<Derived>::start(int size) const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,
                RowsAtCompileTime == 1 ? 1 : Dynamic,
                ColsAtCompileTime == 1 ? 1 : Dynamic>
@@ -424,7 +424,7 @@ template<typename Derived>
 inline typename BlockReturnType<Derived,Dynamic>::SubVectorType
 MatrixBase<Derived>::end(int size)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,
                RowsAtCompileTime == 1 ? 1 : Dynamic,
                ColsAtCompileTime == 1 ? 1 : Dynamic>
@@ -440,7 +440,7 @@ template<typename Derived>
 inline const typename BlockReturnType<Derived,Dynamic>::SubVectorType
 MatrixBase<Derived>::end(int size) const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,
                RowsAtCompileTime == 1 ? 1 : Dynamic,
                ColsAtCompileTime == 1 ? 1 : Dynamic>
@@ -451,7 +451,7 @@ MatrixBase<Derived>::end(int size) const
                ColsAtCompileTime == 1 ? 1 : size);
 }
 
-/** \returns a fixed-size expression of a sub-vector of \c *this
+/** \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
   *
   * \only_for_vectors
   *
@@ -459,30 +459,30 @@ MatrixBase<Derived>::end(int size) const
   * 
   * \param start the index of the first element of the sub-vector
   *
-  * Example: \include MatrixBase_template_int.cpp
-  * Output: \verbinclude MatrixBase_template_int.out
+  * Example: \include MatrixBase_template_int_segment.cpp
+  * Output: \verbinclude MatrixBase_template_int_segment.out
   *
   * \sa class Block
   */
 template<typename Derived>
 template<int Size>
 inline typename BlockReturnType<Derived,Size>::SubVectorType
-MatrixBase<Derived>::block(int start)
+MatrixBase<Derived>::segment(int start)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,  (RowsAtCompileTime == 1 ? 1 : Size),
                          (ColsAtCompileTime == 1 ? 1 : Size)>
               (derived(), RowsAtCompileTime == 1 ? 0 : start,
                           ColsAtCompileTime == 1 ? 0 : start);
 }
 
-/** This is the const version of block<int>(int).*/
+/** This is the const version of segment<int>(int).*/
 template<typename Derived>
 template<int Size>
 inline const typename BlockReturnType<Derived,Size>::SubVectorType
-MatrixBase<Derived>::block(int start) const
+MatrixBase<Derived>::segment(int start) const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived,  (RowsAtCompileTime == 1 ? 1 : Size),
                          (ColsAtCompileTime == 1 ? 1 : Size)>
               (derived(), RowsAtCompileTime == 1 ? 0 : start,
@@ -507,7 +507,7 @@ template<int Size>
 inline typename BlockReturnType<Derived,Size>::SubVectorType
 MatrixBase<Derived>::start()
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
                         (ColsAtCompileTime == 1 ? 1 : Size)>(derived(), 0, 0);
 }
@@ -518,7 +518,7 @@ template<int Size>
 inline const typename BlockReturnType<Derived,Size>::SubVectorType
 MatrixBase<Derived>::start() const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived, (RowsAtCompileTime == 1 ? 1 : Size),
                         (ColsAtCompileTime == 1 ? 1 : Size)>(derived(), 0, 0);
 }
@@ -539,7 +539,7 @@ template<int Size>
 inline typename BlockReturnType<Derived,Size>::SubVectorType
 MatrixBase<Derived>::end()
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived, RowsAtCompileTime == 1 ? 1 : Size,
                         ColsAtCompileTime == 1 ? 1 : Size>
            (derived(),
@@ -553,7 +553,7 @@ template<int Size>
 inline const typename BlockReturnType<Derived,Size>::SubVectorType
 MatrixBase<Derived>::end() const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return Block<Derived, RowsAtCompileTime == 1 ? 1 : Size,
                         ColsAtCompileTime == 1 ? 1 : Size>
            (derived(),

Eigen/src/Core/CacheFriendlyProduct.h

@@ -95,9 +95,9 @@ static void ei_cache_friendly_product(
   const bool needRhsCopy = (PacketSize>1) && ((rhsStride%PacketSize!=0) || (size_t(rhs)%16!=0));
   Scalar* EIGEN_RESTRICT block = 0;
   const int allocBlockSize = l2BlockRows*size;
-  block = ei_alloc_stack(Scalar, allocBlockSize);
+  block = ei_aligned_stack_alloc(Scalar, allocBlockSize);
   Scalar* EIGEN_RESTRICT rhsCopy
-    = ei_alloc_stack(Scalar, l2BlockSizeAligned*l2BlockSizeAligned);
+    = ei_aligned_stack_alloc(Scalar, l2BlockSizeAligned*l2BlockSizeAligned);
 
   // loops on each L2 cache friendly blocks of the result
   for(int l2i=0; l2i<rows; l2i+=l2BlockRows)
@@ -338,8 +338,8 @@ static void ei_cache_friendly_product(
     }
   }
 
-  ei_free_stack(block, Scalar, allocBlockSize);
-  ei_free_stack(rhsCopy, Scalar, l2BlockSizeAligned*l2BlockSizeAligned);
+  ei_aligned_stack_free(block, Scalar, allocBlockSize);
+  ei_aligned_stack_free(rhsCopy, Scalar, l2BlockSizeAligned*l2BlockSizeAligned);
 }
 
 #endif // EIGEN_EXTERN_INSTANTIATIONS
@@ -433,7 +433,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
     {
       /* explicit vectorization */
       // process initial unaligned coeffs
-      for (int j=0; j<alignedStart; j++)
+      for (int j=0; j<alignedStart; ++j)
         res[j] += ei_pfirst(ptmp0)*lhs0[j] + ei_pfirst(ptmp1)*lhs1[j] + ei_pfirst(ptmp2)*lhs2[j] + ei_pfirst(ptmp3)*lhs3[j];
 
       if (alignedSize>alignedStart)
@@ -493,8 +493,8 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
     } // end explicit vectorization
 
     /* process remaining coeffs (or all if there is no explicit vectorization) */
-    for (int j=alignedSize; j<size; j++)
-      res[j] += ei_pfirst(ptmp0)*lhs0[j] + ei_pfirst(ptmp1)*lhs1[j] + ei_pfirst(ptmp2)*lhs2[j] + ei_pfirst(ptmp3)*lhs3[j];
+    for (int j=alignedSize; j<size; ++j)
+	  res[j] += ei_pfirst(ptmp0)*lhs0[j] + ei_pfirst(ptmp1)*lhs1[j] + ei_pfirst(ptmp2)*lhs2[j] + ei_pfirst(ptmp3)*lhs3[j];
   }
 
   // process remaining first and last columns (at most columnsAtOnce-1)
@@ -502,7 +502,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
   int start = columnBound;
   do
   {
-    for (int i=start; i<end; i++)
+    for (int i=start; i<end; ++i)
     {
       Packet ptmp0 = ei_pset1(rhs[i]);
       const Scalar* lhs0 = lhs + i*lhsStride;
@@ -511,7 +511,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
       {
         /* explicit vectorization */
         // process first unaligned result's coeffs
-        for (int j=0; j<alignedStart; j++)
+        for (int j=0; j<alignedStart; ++j)
           res[j] += ei_pfirst(ptmp0) * lhs0[j];
 
         // process aligned result's coeffs
@@ -524,7 +524,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
       }
 
       // process remaining scalars (or all if no explicit vectorization)
-      for (int j=alignedSize; j<size; j++)
+      for (int j=alignedSize; j<size; ++j)
         res[j] += ei_pfirst(ptmp0) * lhs0[j];
     }
     if (skipColumns)
@@ -624,7 +624,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_rowmajor_times_vector(
       
       // process initial unaligned coeffs
       // FIXME this loop get vectorized by the compiler !
-      for (int j=0; j<alignedStart; j++)
+      for (int j=0; j<alignedStart; ++j)
       {
         Scalar b = rhs[j];
         tmp0 += b*lhs0[j]; tmp1 += b*lhs1[j]; tmp2 += b*lhs2[j]; tmp3 += b*lhs3[j];
@@ -695,7 +695,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_rowmajor_times_vector(
 
     // process remaining coeffs (or all if no explicit vectorization)
     // FIXME this loop get vectorized by the compiler !
-    for (int j=alignedSize; j<size; j++)
+    for (int j=alignedSize; j<size; ++j)
     {
       Scalar b = rhs[j];
       tmp0 += b*lhs0[j]; tmp1 += b*lhs1[j]; tmp2 += b*lhs2[j]; tmp3 += b*lhs3[j];
@@ -708,14 +708,14 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_rowmajor_times_vector(
   int start = rowBound;
   do
   {
-    for (int i=start; i<end; i++)
+    for (int i=start; i<end; ++i)
     {
       Scalar tmp0 = Scalar(0);
       Packet ptmp0 = ei_pset1(tmp0);
       const Scalar* lhs0 = lhs + i*lhsStride;
       // process first unaligned result's coeffs
       // FIXME this loop get vectorized by the compiler !
-      for (int j=0; j<alignedStart; j++)
+      for (int j=0; j<alignedStart; ++j)
         tmp0 += rhs[j] * lhs0[j];
 
       if (alignedSize>alignedStart)
@@ -732,7 +732,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_rowmajor_times_vector(
 
       // process remaining scalars
       // FIXME this loop get vectorized by the compiler !
-      for (int j=alignedSize; j<size; j++)
+      for (int j=alignedSize; j<size; ++j)
         tmp0 += rhs[j] * lhs0[j];
       res[i] += tmp0;
     }

Eigen/src/Core/Coeffs.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -40,7 +40,7 @@
   * \sa operator()(int,int) const, coeffRef(int,int), coeff(int) const
   */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::coeff(int row, int col) const
 {
   ei_internal_assert(row >= 0 && row < rows()
@@ -53,7 +53,7 @@ inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   * \sa operator()(int,int), operator[](int) const
   */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::operator()(int row, int col) const
 {
   ei_assert(row >= 0 && row < rows()
@@ -76,7 +76,7 @@ inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   * \sa operator()(int,int), coeff(int, int) const, coeffRef(int)
   */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::coeffRef(int row, int col)
 {
   ei_internal_assert(row >= 0 && row < rows()
@@ -89,7 +89,7 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   * \sa operator()(int,int) const, operator[](int)
   */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::operator()(int row, int col)
 {
   ei_assert(row >= 0 && row < rows()
@@ -112,7 +112,7 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   * \sa operator[](int) const, coeffRef(int), coeff(int,int) const
   */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::coeff(int index) const
 {
   ei_internal_assert(index >= 0 && index < size());
@@ -127,7 +127,7 @@ inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   * z() const, w() const
   */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::operator[](int index) const
 {
   ei_assert(index >= 0 && index < size());
@@ -144,7 +144,7 @@ inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   * z() const, w() const
   */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::operator()(int index) const
 {
   ei_assert(index >= 0 && index < size());
@@ -166,7 +166,7 @@ inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   * \sa operator[](int), coeff(int) const, coeffRef(int,int)
   */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::coeffRef(int index)
 {
   ei_internal_assert(index >= 0 && index < size());
@@ -180,7 +180,7 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   * \sa operator[](int) const, operator()(int,int), x(), y(), z(), w()
   */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::operator[](int index)
 {
   ei_assert(index >= 0 && index < size());
@@ -196,7 +196,7 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   * \sa operator[](int) const, operator()(int,int), x(), y(), z(), w()
   */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::operator()(int index)
 {
   ei_assert(index >= 0 && index < size());
@@ -205,42 +205,42 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
 
 /** equivalent to operator[](0).  */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::x() const { return (*this)[0]; }
 
 /** equivalent to operator[](1).  */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::y() const { return (*this)[1]; }
 
 /** equivalent to operator[](2).  */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::z() const { return (*this)[2]; }
 
 /** equivalent to operator[](3).  */
 template<typename Derived>
-inline const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
+EIGEN_STRONG_INLINE const typename ei_traits<Derived>::Scalar MatrixBase<Derived>
   ::w() const { return (*this)[3]; }
 
 /** equivalent to operator[](0).  */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::x() { return (*this)[0]; }
 
 /** equivalent to operator[](1).  */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::y() { return (*this)[1]; }
 
 /** equivalent to operator[](2).  */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::z() { return (*this)[2]; }
 
 /** equivalent to operator[](3).  */
 template<typename Derived>
-inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
+EIGEN_STRONG_INLINE typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   ::w() { return (*this)[3]; }
 
 /** \returns the packet of coefficients starting at the given row and column. It is your responsibility
@@ -253,7 +253,7 @@ inline typename ei_traits<Derived>::Scalar& MatrixBase<Derived>
   */
 template<typename Derived>
 template<int LoadMode>
-inline typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
+EIGEN_STRONG_INLINE typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
 MatrixBase<Derived>::packet(int row, int col) const
 {
   ei_internal_assert(row >= 0 && row < rows()
@@ -271,7 +271,7 @@ MatrixBase<Derived>::packet(int row, int col) const
   */
 template<typename Derived>
 template<int StoreMode>
-inline void MatrixBase<Derived>::writePacket
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::writePacket
 (int row, int col, const typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type& x)
 {
   ei_internal_assert(row >= 0 && row < rows()
@@ -289,7 +289,7 @@ inline void MatrixBase<Derived>::writePacket
   */
 template<typename Derived>
 template<int LoadMode>
-inline typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
+EIGEN_STRONG_INLINE typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type
 MatrixBase<Derived>::packet(int index) const
 {
   ei_internal_assert(index >= 0 && index < size());
@@ -306,33 +306,54 @@ MatrixBase<Derived>::packet(int index) const
   */
 template<typename Derived>
 template<int StoreMode>
-inline void MatrixBase<Derived>::writePacket
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::writePacket
 (int index, const typename ei_packet_traits<typename ei_traits<Derived>::Scalar>::type& x)
 {
   ei_internal_assert(index >= 0 && index < size());
   derived().template writePacket<StoreMode>(index,x);
 }
 
+/** \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 Derived>
 template<typename OtherDerived>
-inline void MatrixBase<Derived>::copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other)
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other)
 {
   ei_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 Derived>
 template<typename OtherDerived>
-inline void MatrixBase<Derived>::copyCoeff(int index, const MatrixBase<OtherDerived>& other)
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyCoeff(int index, const MatrixBase<OtherDerived>& other)
 {
   ei_internal_assert(index >= 0 && index < size());
   derived().coeffRef(index) = other.derived().coeff(index);
 }
 
+/** \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 Derived>
 template<typename OtherDerived, int StoreMode, int LoadMode>
-inline void MatrixBase<Derived>::copyPacket(int row, int col, const MatrixBase<OtherDerived>& other)
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyPacket(int row, int col, const MatrixBase<OtherDerived>& other)
 {
   ei_internal_assert(row >= 0 && row < rows()
                      && col >= 0 && col < cols());
@@ -340,9 +361,16 @@ inline void MatrixBase<Derived>::copyPacket(int row, int col, const MatrixBase<O
     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 Derived>
 template<typename OtherDerived, int StoreMode, int LoadMode>
-inline void MatrixBase<Derived>::copyPacket(int index, const MatrixBase<OtherDerived>& other)
+EIGEN_STRONG_INLINE void MatrixBase<Derived>::copyPacket(int index, const MatrixBase<OtherDerived>& other)
 {
   ei_internal_assert(index >= 0 && index < size());
   derived().template writePacket<StoreMode>(index,

Eigen/src/Core/CommaInitializer.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -27,13 +27,13 @@
 #define EIGEN_COMMAINITIALIZER_H
 
 /** \class CommaInitializer
-  * 
+  *
   * \brief Helper class used by the comma initializer operator
   *
   * This class is internally used to implement the comma initializer feature. It is
   * the return type of MatrixBase::operator<<, and most of the time this is the only
   * way it is used.
-  * 
+  *
   * \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
   */
 template<typename MatrixType>
@@ -128,7 +128,7 @@ struct CommaInitializer
   *
   * Example: \include MatrixBase_set.cpp
   * Output: \verbinclude MatrixBase_set.out
-  * 
+  *
   * \sa CommaInitializer::finished(), class CommaInitializer
   */
 template<typename Derived>

Eigen/src/Core/Cwise.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -31,6 +31,18 @@
 #define EIGEN_CWISE_BINOP_RETURN_TYPE(OP) \
     CwiseBinaryOp<OP<typename ei_traits<ExpressionType>::Scalar>, ExpressionType, OtherDerived>
 
+#define EIGEN_CWISE_PRODUCT_RETURN_TYPE \
+    CwiseBinaryOp< \
+      ei_scalar_product_op< \
+        typename ei_scalar_product_traits< \
+          typename ei_traits<ExpressionType>::Scalar, \
+          typename ei_traits<OtherDerived>::Scalar \
+        >::ReturnType \
+      >, \
+      ExpressionType, \
+      OtherDerived \
+    >
+
 /** \internal
   * convenient macro to defined the return type of a cwise unary operation */
 #define EIGEN_CWISE_UNOP_RETURN_TYPE(OP) \
@@ -74,7 +86,7 @@ template<typename ExpressionType> class Cwise
     inline const ExpressionType& _expression() const { return m_matrix; }
 
     template<typename OtherDerived>
-    const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_product_op)
+    const EIGEN_CWISE_PRODUCT_RETURN_TYPE
     operator*(const MatrixBase<OtherDerived> &other) const;
 
     template<typename OtherDerived>

Eigen/src/Core/CwiseBinaryOp.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -46,6 +46,8 @@
 template<typename BinaryOp, typename Lhs, typename Rhs>
 struct ei_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 ei_result_of<
                      BinaryOp(
                        typename Lhs::Scalar,
@@ -86,33 +88,46 @@ class CwiseBinaryOp : ei_no_assignment_operator,
 
     class InnerIterator;
 
-    inline CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
+    EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
       : m_lhs(lhs), m_rhs(rhs), m_functor(func)
     {
+      // 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.
+      EIGEN_STATIC_ASSERT((ei_functor_allows_mixing_real_and_complex<BinaryOp>::ret
+                           ? int(ei_is_same_type<typename Lhs::RealScalar, typename Rhs::RealScalar>::ret)
+                           : int(ei_is_same_type<typename Lhs::Scalar, typename Rhs::Scalar>::ret)),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+      // require the sizes to match
+      EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
       ei_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
     }
 
-    inline int rows() const { return m_lhs.rows(); }
-    inline int cols() const { return m_lhs.cols(); }
+    EIGEN_STRONG_INLINE int rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE int cols() const { return m_lhs.cols(); }
 
-    inline const Scalar coeff(int row, int col) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int row, int col) const
     {
       return m_functor(m_lhs.coeff(row, col), m_rhs.coeff(row, col));
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int row, int col) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
     {
       return m_functor.packetOp(m_lhs.template packet<LoadMode>(row, col), m_rhs.template packet<LoadMode>(row, col));
     }
 
-    inline const Scalar coeff(int index) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int index) const
     {
       return m_functor(m_lhs.coeff(index), m_rhs.coeff(index));
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int index) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int index) const
     {
       return m_functor.packetOp(m_lhs.template packet<LoadMode>(index), m_rhs.template packet<LoadMode>(index));
     }
@@ -131,7 +146,7 @@ class CwiseBinaryOp : ei_no_assignment_operator,
   */
 template<typename Derived>
 template<typename OtherDerived>
-inline const CwiseBinaryOp<ei_scalar_difference_op<typename ei_traits<Derived>::Scalar>,
+EIGEN_STRONG_INLINE const CwiseBinaryOp<ei_scalar_difference_op<typename ei_traits<Derived>::Scalar>,
                                  Derived, OtherDerived>
 MatrixBase<Derived>::operator-(const MatrixBase<OtherDerived> &other) const
 {
@@ -145,7 +160,7 @@ MatrixBase<Derived>::operator-(const MatrixBase<OtherDerived> &other) const
   */
 template<typename Derived>
 template<typename OtherDerived>
-inline Derived &
+EIGEN_STRONG_INLINE Derived &
 MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
 {
   return *this = *this - other;
@@ -161,7 +176,7 @@ MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
   */
 template<typename Derived>
 template<typename OtherDerived>
-inline const CwiseBinaryOp<ei_scalar_sum_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<ei_scalar_sum_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
 MatrixBase<Derived>::operator+(const MatrixBase<OtherDerived> &other) const
 {
   return CwiseBinaryOp<ei_scalar_sum_op<Scalar>, Derived, OtherDerived>(derived(), other.derived());
@@ -173,7 +188,7 @@ MatrixBase<Derived>::operator+(const MatrixBase<OtherDerived> &other) const
   */
 template<typename Derived>
 template<typename OtherDerived>
-inline Derived &
+EIGEN_STRONG_INLINE Derived &
 MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
 {
   return *this = *this + other;
@@ -188,10 +203,10 @@ MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
   */
 template<typename ExpressionType>
 template<typename OtherDerived>
-inline const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_product_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE
 Cwise<ExpressionType>::operator*(const MatrixBase<OtherDerived> &other) const
 {
-  return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_product_op)(_expression(), other.derived());
+  return EIGEN_CWISE_PRODUCT_RETURN_TYPE(_expression(), other.derived());
 }
 
 /** \returns an expression of the coefficient-wise quotient of *this and \a other
@@ -203,7 +218,7 @@ Cwise<ExpressionType>::operator*(const MatrixBase<OtherDerived> &other) const
   */
 template<typename ExpressionType>
 template<typename OtherDerived>
-inline const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)
 Cwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const
 {
   return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_quotient_op)(_expression(), other.derived());
@@ -218,7 +233,7 @@ Cwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const
   */
 template<typename ExpressionType>
 template<typename OtherDerived>
-inline const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)
 Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
 {
   return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_min_op)(_expression(), other.derived());
@@ -233,7 +248,7 @@ Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
   */
 template<typename ExpressionType>
 template<typename OtherDerived>
-inline const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)
 Cwise<ExpressionType>::max(const MatrixBase<OtherDerived> &other) const
 {
   return EIGEN_CWISE_BINOP_RETURN_TYPE(ei_scalar_max_op)(_expression(), other.derived());
@@ -254,7 +269,7 @@ Cwise<ExpressionType>::max(const MatrixBase<OtherDerived> &other) const
   */
 template<typename Derived>
 template<typename CustomBinaryOp, typename OtherDerived>
-inline const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
 MatrixBase<Derived>::binaryExpr(const MatrixBase<OtherDerived> &other, const CustomBinaryOp& func) const
 {
   return CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>(derived(), other.derived(), func);

Eigen/src/Core/CwiseNullaryOp.h

@@ -75,21 +75,21 @@ class CwiseNullaryOp : ei_no_assignment_operator,
           && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
     }
 
-    int rows() const { return m_rows.value(); }
-    int cols() const { return m_cols.value(); }
+    EIGEN_STRONG_INLINE int rows() const { return m_rows.value(); }
+    EIGEN_STRONG_INLINE int cols() const { return m_cols.value(); }
 
-    const Scalar coeff(int rows, int cols) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int rows, int cols) const
     {
       return m_functor(rows, cols);
     }
 
     template<int LoadMode>
-    PacketScalar packet(int, int) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int, int) const
     {
       return m_functor.packetOp();
     }
 
-    const Scalar coeff(int index) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int index) const
     {
       if(RowsAtCompileTime == 1)
         return m_functor(0, index);
@@ -98,7 +98,7 @@ class CwiseNullaryOp : ei_no_assignment_operator,
     }
 
     template<int LoadMode>
-    PacketScalar packet(int) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int) const
     {
       return m_functor.packetOp();
     }
@@ -125,7 +125,7 @@ class CwiseNullaryOp : ei_no_assignment_operator,
   */
 template<typename Derived>
 template<typename CustomNullaryOp>
-const CwiseNullaryOp<CustomNullaryOp, Derived>
+EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
 MatrixBase<Derived>::NullaryExpr(int rows, int cols, const CustomNullaryOp& func)
 {
   return CwiseNullaryOp<CustomNullaryOp, Derived>(rows, cols, func);
@@ -148,7 +148,7 @@ MatrixBase<Derived>::NullaryExpr(int rows, int cols, const CustomNullaryOp& func
   */
 template<typename Derived>
 template<typename CustomNullaryOp>
-const CwiseNullaryOp<CustomNullaryOp, Derived>
+EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
 MatrixBase<Derived>::NullaryExpr(int size, const CustomNullaryOp& func)
 {
   ei_assert(IsVectorAtCompileTime);
@@ -167,7 +167,7 @@ MatrixBase<Derived>::NullaryExpr(int size, const CustomNullaryOp& func)
   */
 template<typename Derived>
 template<typename CustomNullaryOp>
-const CwiseNullaryOp<CustomNullaryOp, Derived>
+EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
 MatrixBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
 {
   return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func);
@@ -187,7 +187,7 @@ MatrixBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
   * \sa class CwiseNullaryOp
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Constant(int rows, int cols, const Scalar& value)
 {
   return NullaryExpr(rows, cols, ei_scalar_constant_op<Scalar>(value));
@@ -209,7 +209,7 @@ MatrixBase<Derived>::Constant(int rows, int cols, const Scalar& value)
   * \sa class CwiseNullaryOp
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Constant(int size, const Scalar& value)
 {
   return NullaryExpr(size, ei_scalar_constant_op<Scalar>(value));
@@ -225,7 +225,7 @@ MatrixBase<Derived>::Constant(int size, const Scalar& value)
   * \sa class CwiseNullaryOp
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Constant(const Scalar& value)
 {
   EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@@ -236,8 +236,8 @@ template<typename Derived>
 bool MatrixBase<Derived>::isApproxToConstant
 (const Scalar& value, RealScalar prec) const
 {
-  for(int j = 0; j < cols(); j++)
-    for(int i = 0; i < rows(); i++)
+  for(int j = 0; j < cols(); ++j)
+    for(int i = 0; i < rows(); ++i)
       if(!ei_isApprox(coeff(i, j), value, prec))
         return false;
   return true;
@@ -248,7 +248,7 @@ bool MatrixBase<Derived>::isApproxToConstant
   * \sa class CwiseNullaryOp, Zero(), Ones()
   */
 template<typename Derived>
-Derived& MatrixBase<Derived>::setConstant(const Scalar& value)
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setConstant(const Scalar& value)
 {
   return derived() = Constant(rows(), cols(), value);
 }
@@ -272,7 +272,7 @@ Derived& MatrixBase<Derived>::setConstant(const Scalar& value)
   * \sa Zero(), Zero(int)
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Zero(int rows, int cols)
 {
   return Constant(rows, cols, Scalar(0));
@@ -295,7 +295,7 @@ MatrixBase<Derived>::Zero(int rows, int cols)
   * \sa Zero(), Zero(int,int)
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Zero(int size)
 {
   return Constant(size, Scalar(0));
@@ -312,7 +312,7 @@ MatrixBase<Derived>::Zero(int size)
   * \sa Zero(int), Zero(int,int)
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Zero()
 {
   return Constant(Scalar(0));
@@ -327,11 +327,10 @@ MatrixBase<Derived>::Zero()
   * \sa class CwiseNullaryOp, Zero()
   */
 template<typename Derived>
-bool MatrixBase<Derived>::isZero
-(RealScalar prec) const
+bool MatrixBase<Derived>::isZero(RealScalar prec) const
 {
-  for(int j = 0; j < cols(); j++)
-    for(int i = 0; i < rows(); i++)
+  for(int j = 0; j < cols(); ++j)
+    for(int i = 0; i < rows(); ++i)
       if(!ei_isMuchSmallerThan(coeff(i, j), static_cast<Scalar>(1), prec))
         return false;
   return true;
@@ -345,7 +344,7 @@ bool MatrixBase<Derived>::isZero
   * \sa class CwiseNullaryOp, Zero()
   */
 template<typename Derived>
-Derived& MatrixBase<Derived>::setZero()
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setZero()
 {
   return setConstant(Scalar(0));
 }
@@ -369,7 +368,7 @@ Derived& MatrixBase<Derived>::setZero()
   * \sa Ones(), Ones(int), isOnes(), class Ones
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Ones(int rows, int cols)
 {
   return Constant(rows, cols, Scalar(1));
@@ -392,7 +391,7 @@ MatrixBase<Derived>::Ones(int rows, int cols)
   * \sa Ones(), Ones(int,int), isOnes(), class Ones
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Ones(int size)
 {
   return Constant(size, Scalar(1));
@@ -409,7 +408,7 @@ MatrixBase<Derived>::Ones(int size)
   * \sa Ones(int), Ones(int,int), isOnes(), class Ones
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::ConstantReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ConstantReturnType
 MatrixBase<Derived>::Ones()
 {
   return Constant(Scalar(1));
@@ -438,7 +437,7 @@ bool MatrixBase<Derived>::isOnes
   * \sa class CwiseNullaryOp, Ones()
   */
 template<typename Derived>
-Derived& MatrixBase<Derived>::setOnes()
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setOnes()
 {
   return setConstant(Scalar(1));
 }
@@ -462,7 +461,7 @@ Derived& MatrixBase<Derived>::setOnes()
   * \sa Identity(), setIdentity(), isIdentity()
   */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::IdentityReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
 MatrixBase<Derived>::Identity(int rows, int cols)
 {
   return NullaryExpr(rows, cols, ei_scalar_identity_op<Scalar>());
@@ -479,7 +478,7 @@ MatrixBase<Derived>::Identity(int rows, int cols)
   * \sa Identity(int,int), setIdentity(), isIdentity()
   */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::IdentityReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
 MatrixBase<Derived>::Identity()
 {
   EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@@ -499,9 +498,9 @@ template<typename Derived>
 bool MatrixBase<Derived>::isIdentity
 (RealScalar prec) const
 {
-  for(int j = 0; j < cols(); j++)
+  for(int j = 0; j < cols(); ++j)
   {
-    for(int i = 0; i < rows(); i++)
+    for(int i = 0; i < rows(); ++i)
     {
       if(i == j)
       {
@@ -521,7 +520,7 @@ bool MatrixBase<Derived>::isIdentity
 template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
 struct ei_setIdentity_impl
 {
-  static inline Derived& run(Derived& m)
+  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
   {
     return m = Derived::Identity(m.rows(), m.cols());
   }
@@ -530,11 +529,11 @@ struct ei_setIdentity_impl
 template<typename Derived>
 struct ei_setIdentity_impl<Derived, true>
 {
-  static inline Derived& run(Derived& m)
+  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
   {
     m.setZero();
     const int size = std::min(m.rows(), m.cols());
-    for(int i = 0; i < size; i++) m.coeffRef(i,i) = typename Derived::Scalar(1);
+    for(int i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
     return m;
   }
 };
@@ -547,7 +546,7 @@ struct ei_setIdentity_impl<Derived, true>
   * \sa class CwiseNullaryOp, Identity(), Identity(int,int), isIdentity()
   */
 template<typename Derived>
-inline Derived& MatrixBase<Derived>::setIdentity()
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
 {
   return ei_setIdentity_impl<Derived>::run(derived());
 }
@@ -559,9 +558,9 @@ inline Derived& MatrixBase<Derived>::setIdentity()
   * \sa MatrixBase::Unit(int), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int size, int i)
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int size, int i)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return BasisReturnType(SquareMatrixType::Identity(size,size), i);
 }
 
@@ -574,9 +573,9 @@ const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(in
   * \sa MatrixBase::Unit(int,int), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int i)
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(int i)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return BasisReturnType(SquareMatrixType::Identity(),i);
 }
 
@@ -587,7 +586,7 @@ const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(in
   * \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
 { return Derived::Unit(0); }
 
 /** \returns an expression of the Y axis unit vector (0,1{,0}^*)
@@ -597,7 +596,7 @@ const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
   * \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
 { return Derived::Unit(1); }
 
 /** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
@@ -607,7 +606,7 @@ const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
   * \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
 { return Derived::Unit(2); }
 
 /** \returns an expression of the W axis unit vector (0,0,0,1)
@@ -617,7 +616,7 @@ const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
   * \sa MatrixBase::Unit(int,int), MatrixBase::Unit(int), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
   */
 template<typename Derived>
-const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
 { return Derived::Unit(3); }
 
 #endif // EIGEN_CWISE_NULLARY_OP_H

Eigen/src/Core/CwiseUnaryOp.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -74,27 +74,27 @@ class CwiseUnaryOp : ei_no_assignment_operator,
     inline CwiseUnaryOp(const MatrixType& mat, const UnaryOp& func = UnaryOp())
       : m_matrix(mat), m_functor(func) {}
 
-    inline int rows() const { return m_matrix.rows(); }
-    inline int cols() const { return m_matrix.cols(); }
+    EIGEN_STRONG_INLINE int rows() const { return m_matrix.rows(); }
+    EIGEN_STRONG_INLINE int cols() const { return m_matrix.cols(); }
 
-    inline const Scalar coeff(int row, int col) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int row, int col) const
     {
       return m_functor(m_matrix.coeff(row, col));
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int row, int col) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
     {
       return m_functor.packetOp(m_matrix.template packet<LoadMode>(row, col));
     }
 
-    inline const Scalar coeff(int index) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int index) const
     {
       return m_functor(m_matrix.coeff(index));
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int index) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int index) const
     {
       return m_functor.packetOp(m_matrix.template packet<LoadMode>(index));
     }
@@ -119,7 +119,7 @@ class CwiseUnaryOp : ei_no_assignment_operator,
   */
 template<typename Derived>
 template<typename CustomUnaryOp>
-inline const CwiseUnaryOp<CustomUnaryOp, Derived>
+EIGEN_STRONG_INLINE const CwiseUnaryOp<CustomUnaryOp, Derived>
 MatrixBase<Derived>::unaryExpr(const CustomUnaryOp& func) const
 {
   return CwiseUnaryOp<CustomUnaryOp, Derived>(derived(), func);
@@ -128,7 +128,7 @@ MatrixBase<Derived>::unaryExpr(const CustomUnaryOp& func) const
 /** \returns an expression of the opposite of \c *this
   */
 template<typename Derived>
-inline const CwiseUnaryOp<ei_scalar_opposite_op<typename ei_traits<Derived>::Scalar>,Derived>
+EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_opposite_op<typename ei_traits<Derived>::Scalar>,Derived>
 MatrixBase<Derived>::operator-() const
 {
   return derived();
@@ -142,7 +142,7 @@ MatrixBase<Derived>::operator-() const
   * \sa abs2()
   */
 template<typename ExpressionType>
-inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs_op)
 Cwise<ExpressionType>::abs() const
 {
   return _expression();
@@ -156,7 +156,7 @@ Cwise<ExpressionType>::abs() const
   * \sa abs(), square()
   */
 template<typename ExpressionType>
-inline const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs2_op)
+EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(ei_scalar_abs2_op)
 Cwise<ExpressionType>::abs2() const
 {
   return _expression();
@@ -166,7 +166,7 @@ Cwise<ExpressionType>::abs2() const
   *
   * \sa adjoint() */
 template<typename Derived>
-inline typename MatrixBase<Derived>::ConjugateReturnType
+EIGEN_STRONG_INLINE typename MatrixBase<Derived>::ConjugateReturnType
 MatrixBase<Derived>::conjugate() const
 {
   return ConjugateReturnType(derived());
@@ -174,13 +174,17 @@ MatrixBase<Derived>::conjugate() const
 
 /** \returns an expression of the real part of \c *this.
   *
-  * \sa adjoint() */
+  * \sa imag() */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::RealReturnType
-MatrixBase<Derived>::real() const
-{
-  return derived();
-}
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::RealReturnType
+MatrixBase<Derived>::real() const { return derived(); }
+
+/** \returns an expression of the imaginary part of \c *this.
+  *
+  * \sa real() */
+template<typename Derived>
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ImagReturnType
+MatrixBase<Derived>::imag() const { return derived(); }
 
 /** \returns an expression of *this with the \a Scalar type casted to
   * \a NewScalar.
@@ -191,7 +195,7 @@ MatrixBase<Derived>::real() const
   */
 template<typename Derived>
 template<typename NewType>
-inline const CwiseUnaryOp<ei_scalar_cast_op<typename ei_traits<Derived>::Scalar, NewType>, Derived>
+EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_cast_op<typename ei_traits<Derived>::Scalar, NewType>, Derived>
 MatrixBase<Derived>::cast() const
 {
   return derived();
@@ -199,7 +203,7 @@ MatrixBase<Derived>::cast() const
 
 /** \relates MatrixBase */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::ScalarMultipleReturnType
+EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::ScalarMultipleReturnType
 MatrixBase<Derived>::operator*(const Scalar& scalar) const
 {
   return CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, Derived>
@@ -208,7 +212,7 @@ MatrixBase<Derived>::operator*(const Scalar& scalar) const
 
 /** \relates MatrixBase */
 template<typename Derived>
-inline const CwiseUnaryOp<ei_scalar_quotient1_op<typename ei_traits<Derived>::Scalar>, Derived>
+EIGEN_STRONG_INLINE const CwiseUnaryOp<ei_scalar_quotient1_op<typename ei_traits<Derived>::Scalar>, Derived>
 MatrixBase<Derived>::operator/(const Scalar& scalar) const
 {
   return CwiseUnaryOp<ei_scalar_quotient1_op<Scalar>, Derived>
@@ -216,14 +220,14 @@ MatrixBase<Derived>::operator/(const Scalar& scalar) const
 }
 
 template<typename Derived>
-inline Derived&
+EIGEN_STRONG_INLINE Derived&
 MatrixBase<Derived>::operator*=(const Scalar& other)
 {
   return *this = *this * other;
 }
 
 template<typename Derived>
-inline Derived&
+EIGEN_STRONG_INLINE Derived&
 MatrixBase<Derived>::operator/=(const Scalar& other)
 {
   return *this = *this / other;

Eigen/src/Core/DiagonalCoeffs.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/DiagonalMatrix.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -62,10 +62,19 @@ class DiagonalMatrix : ei_no_assignment_operator,
 
     EIGEN_GENERIC_PUBLIC_INTERFACE(DiagonalMatrix)
 
+    // needed to evaluate a DiagonalMatrix<Xpr> to a DiagonalMatrix<NestByValue<Vector> >
+    template<typename OtherCoeffsVectorType>
+    inline DiagonalMatrix(const DiagonalMatrix<OtherCoeffsVectorType>& other) : m_coeffs(other.diagonal())
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(CoeffsVectorType);
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherCoeffsVectorType);
+      ei_assert(m_coeffs.size() > 0);
+    }
+
     inline DiagonalMatrix(const CoeffsVectorType& coeffs) : m_coeffs(coeffs)
     {
-      ei_assert(CoeffsVectorType::IsVectorAtCompileTime
-          && coeffs.size() > 0);
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(CoeffsVectorType);
+      ei_assert(coeffs.size() > 0);
     }
 
     inline int rows() const { return m_coeffs.size(); }
@@ -76,6 +85,8 @@ class DiagonalMatrix : ei_no_assignment_operator,
       return row == col ? m_coeffs.coeff(row) : static_cast<Scalar>(0);
     }
 
+    inline const CoeffsVectorType& diagonal() const { return m_coeffs; }
+
   protected:
     const typename CoeffsVectorType::Nested m_coeffs;
 };
@@ -112,13 +123,13 @@ bool MatrixBase<Derived>::isDiagonal
 {
   if(cols() != rows()) return false;
   RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
-  for(int j = 0; j < cols(); j++)
+  for(int j = 0; j < cols(); ++j)
   {
     RealScalar absOnDiagonal = ei_abs(coeff(j,j));
     if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
   }
-  for(int j = 0; j < cols(); j++)
-    for(int i = 0; i < j; i++)
+  for(int j = 0; j < cols(); ++j)
+    for(int i = 0; i < j; ++i)
     {
       if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
       if(!ei_isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;

Eigen/src/Core/DiagonalProduct.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -26,12 +26,31 @@
 #ifndef EIGEN_DIAGONALPRODUCT_H
 #define EIGEN_DIAGONALPRODUCT_H
 
+/** \internal Specialization of ei_nested for DiagonalMatrix.
+ *  Unlike ei_nested, if the argument is a DiagonalMatrix and if it must be evaluated,
+ *  then it evaluated to a DiagonalMatrix having its own argument evaluated.
+ */
+template<typename T, int N> struct ei_nested_diagonal : ei_nested<T,N> {};
+template<typename T, int N> struct ei_nested_diagonal<DiagonalMatrix<T>,N >
+ : ei_nested<DiagonalMatrix<T>, N, DiagonalMatrix<NestByValue<typename ei_plain_matrix_type<T>::type> > >
+{};
+
+// specialization of ProductReturnType
+template<typename Lhs, typename Rhs>
+struct ProductReturnType<Lhs,Rhs,DiagonalProduct>
+{
+  typedef typename ei_nested_diagonal<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
+  typedef typename ei_nested_diagonal<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
+
+  typedef Product<LhsNested, RhsNested, DiagonalProduct> Type;
+};
+
 template<typename LhsNested, typename RhsNested>
 struct ei_traits<Product<LhsNested, RhsNested, DiagonalProduct> >
 {
   // clean the nested types:
-  typedef typename ei_unconst<typename ei_unref<LhsNested>::type>::type _LhsNested;
-  typedef typename ei_unconst<typename ei_unref<RhsNested>::type>::type _RhsNested;
+  typedef typename ei_cleantype<LhsNested>::type _LhsNested;
+  typedef typename ei_cleantype<RhsNested>::type _RhsNested;
   typedef typename _LhsNested::Scalar Scalar;
 
   enum {

Eigen/src/Core/Dot.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -153,9 +153,10 @@ struct ei_dot_impl<Derived1, Derived2, NoVectorization, NoUnrolling>
   typedef typename Derived1::Scalar Scalar;
   static Scalar run(const Derived1& v1, const Derived2& v2)
   {
+    ei_assert(v1.size()>0 && "you are using a non initialized vector");
     Scalar res;
     res = v1.coeff(0) * ei_conj(v2.coeff(0));
-    for(int i = 1; i < v1.size(); i++)
+    for(int i = 1; i < v1.size(); ++i)
       res += v1.coeff(i) * ei_conj(v2.coeff(i));
     return res;
   }
@@ -210,7 +211,7 @@ struct ei_dot_impl<Derived1, Derived2, LinearVectorization, NoUnrolling>
     }
 
     // do the remainder of the vector
-    for(int index = alignedSize; index < size; index++)
+    for(int index = alignedSize; index < size; ++index)
     {
       res += v1.coeff(index) * v2.coeff(index);
     }
@@ -247,10 +248,10 @@ struct ei_dot_impl<Derived1, Derived2, LinearVectorization, CompleteUnrolling>
   * \only_for_vectors
   *
   * \note If the scalar type is complex numbers, then this function returns the hermitian
-  * (sesquilinear) dot product, linear in the first variable and anti-linear in the
+  * (sesquilinear) dot product, linear in the first variable and conjugate-linear in the
   * second variable.
   *
-  * \sa norm2(), norm()
+  * \sa squaredNorm(), norm()
   */
 template<typename Derived>
 template<typename OtherDerived>
@@ -262,14 +263,33 @@ MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
   typedef typename ei_unref<Nested>::type _Nested;
   typedef typename ei_unref<OtherNested>::type _OtherNested;
 
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(_Nested);
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(_OtherNested);
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(_Nested,_OtherNested);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(_Nested)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(_OtherNested)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(_Nested,_OtherNested)
+  EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
   ei_assert(size() == other.size());
 
   return ei_dot_impl<_Nested, _OtherNested>::run(derived(), other.derived());
 }
 
+/** \returns the squared norm of *this, i.e. the dot product of *this with itself.
+  *
+  * \note This is \em not the \em l2 norm, but its square.
+  *
+  * \deprecated Use squaredNorm() instead. This norm2() function is kept only for compatibility and will be removed in Eigen 2.0.
+  *
+  * \only_for_vectors
+  *
+  * \sa dot(), norm()
+  */
+template<typename Derived>
+EIGEN_DEPRECATED inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm2() const
+{
+  return ei_real(dot(*this));
+}
+
 /** \returns the squared norm of *this, i.e. the dot product of *this with itself.
   *
   * \only_for_vectors
@@ -277,21 +297,21 @@ MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
   * \sa dot(), norm()
   */
 template<typename Derived>
-inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm2() const
+inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
 {
   return ei_real(dot(*this));
 }
 
-/** \returns the norm of *this, i.e. the square root of the dot product of *this with itself.
+/** \returns the \em l2 norm of *this, i.e. the square root of the dot product of *this with itself.
   *
   * \only_for_vectors
   *
-  * \sa dot(), norm2()
+  * \sa dot(), squaredNorm()
   */
 template<typename Derived>
 inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
 {
-  return ei_sqrt(norm2());
+  return ei_sqrt(squaredNorm());
 }
 
 /** \returns an expression of the quotient of *this by its own norm.
@@ -301,7 +321,7 @@ inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<
   * \sa norm(), normalize()
   */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::EvalType
+inline const typename MatrixBase<Derived>::PlainMatrixType
 MatrixBase<Derived>::normalized() const
 {
   typedef typename ei_nested<Derived>::type Nested;
@@ -335,7 +355,7 @@ bool MatrixBase<Derived>::isOrthogonal
 {
   typename ei_nested<Derived,2>::type nested(derived());
   typename ei_nested<OtherDerived,2>::type otherNested(other.derived());
-  return ei_abs2(nested.dot(otherNested)) <= prec * prec * nested.norm2() * otherNested.norm2();
+  return ei_abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
 }
 
 /** \returns true if *this is approximately an unitary matrix,
@@ -353,11 +373,11 @@ template<typename Derived>
 bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
 {
   typename Derived::Nested nested(derived());
-  for(int i = 0; i < cols(); i++)
+  for(int i = 0; i < cols(); ++i)
   {
-    if(!ei_isApprox(nested.col(i).norm2(), static_cast<Scalar>(1), prec))
+    if(!ei_isApprox(nested.col(i).squaredNorm(), static_cast<Scalar>(1), prec))
       return false;
-    for(int j = 0; j < i; j++)
+    for(int j = 0; j < i; ++j)
       if(!ei_isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
         return false;
   }

Eigen/src/Core/Flagged.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/Functors.h

@@ -33,9 +33,9 @@
   * \sa class CwiseBinaryOp, MatrixBase::operator+, class PartialRedux, MatrixBase::sum()
   */
 template<typename Scalar> struct ei_scalar_sum_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_padd(a,b); }
 };
 template<typename Scalar>
@@ -52,9 +52,9 @@ struct ei_functor_traits<ei_scalar_sum_op<Scalar> > {
   * \sa class CwiseBinaryOp, Cwise::operator*(), class PartialRedux, MatrixBase::redux()
   */
 template<typename Scalar> struct ei_scalar_product_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return a * b; }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a * b; }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_pmul(a,b); }
 };
 template<typename Scalar>
@@ -71,9 +71,9 @@ struct ei_functor_traits<ei_scalar_product_op<Scalar> > {
   * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class PartialRedux, MatrixBase::minCoeff()
   */
 template<typename Scalar> struct ei_scalar_min_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return std::min(a, b); }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return std::min(a, b); }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_pmin(a,b); }
 };
 template<typename Scalar>
@@ -90,9 +90,9 @@ struct ei_functor_traits<ei_scalar_min_op<Scalar> > {
   * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class PartialRedux, MatrixBase::maxCoeff()
   */
 template<typename Scalar> struct ei_scalar_max_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return std::max(a, b); }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return std::max(a, b); }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_pmax(a,b); }
 };
 template<typename Scalar>
@@ -112,9 +112,9 @@ struct ei_functor_traits<ei_scalar_max_op<Scalar> > {
   * \sa class CwiseBinaryOp, MatrixBase::operator-
   */
 template<typename Scalar> struct ei_scalar_difference_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_psub(a,b); }
 };
 template<typename Scalar>
@@ -131,9 +131,9 @@ struct ei_functor_traits<ei_scalar_difference_op<Scalar> > {
   * \sa class CwiseBinaryOp, Cwise::operator/()
   */
 template<typename Scalar> struct ei_scalar_quotient_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a, const PacketScalar& b) const
   { return ei_pdiv(a,b); }
 };
 template<typename Scalar>
@@ -155,7 +155,7 @@ struct ei_functor_traits<ei_scalar_quotient_op<Scalar> > {
   * \sa class CwiseUnaryOp, MatrixBase::operator-
   */
 template<typename Scalar> struct ei_scalar_opposite_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a) const { return -a; }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_opposite_op<Scalar> >
@@ -168,7 +168,7 @@ struct ei_functor_traits<ei_scalar_opposite_op<Scalar> >
   */
 template<typename Scalar> struct ei_scalar_abs_op EIGEN_EMPTY_STRUCT {
   typedef typename NumTraits<Scalar>::Real result_type;
-  inline const result_type operator() (const Scalar& a) const { return ei_abs(a); }
+  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return ei_abs(a); }
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_abs_op<Scalar> >
@@ -186,9 +186,9 @@ struct ei_functor_traits<ei_scalar_abs_op<Scalar> >
   */
 template<typename Scalar> struct ei_scalar_abs2_op EIGEN_EMPTY_STRUCT {
   typedef typename NumTraits<Scalar>::Real result_type;
-  inline const result_type operator() (const Scalar& a) const { return ei_abs2(a); }
+  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return ei_abs2(a); }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a) const
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a) const
   { return ei_pmul(a,a); }
 };
 template<typename Scalar>
@@ -201,9 +201,9 @@ struct ei_functor_traits<ei_scalar_abs2_op<Scalar> >
   * \sa class CwiseUnaryOp, MatrixBase::conjugate()
   */
 template<typename Scalar> struct ei_scalar_conjugate_op EIGEN_EMPTY_STRUCT {
-  inline const Scalar operator() (const Scalar& a) const { return ei_conj(a); }
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return ei_conj(a); }
   template<typename PacketScalar>
-  inline const PacketScalar packetOp(const PacketScalar& a) const { return a; }
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a) const { return a; }
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_conjugate_op<Scalar> >
@@ -222,7 +222,7 @@ struct ei_functor_traits<ei_scalar_conjugate_op<Scalar> >
 template<typename Scalar, typename NewType>
 struct ei_scalar_cast_op EIGEN_EMPTY_STRUCT {
   typedef NewType result_type;
-  inline const NewType operator() (const Scalar& a) const { return static_cast<NewType>(a); }
+  EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return static_cast<NewType>(a); }
 };
 template<typename Scalar, typename NewType>
 struct ei_functor_traits<ei_scalar_cast_op<Scalar,NewType> >
@@ -236,11 +236,25 @@ struct ei_functor_traits<ei_scalar_cast_op<Scalar,NewType> >
 template<typename Scalar>
 struct ei_scalar_real_op EIGEN_EMPTY_STRUCT {
   typedef typename NumTraits<Scalar>::Real result_type;
-  inline result_type operator() (const Scalar& a) const { return ei_real(a); }
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return ei_real(a); }
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_real_op<Scalar> >
-{ enum { Cost =  0, PacketAccess = false }; };
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the imaginary part of a complex
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::imag()
+  */
+template<typename Scalar>
+struct ei_scalar_imag_op EIGEN_EMPTY_STRUCT {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return ei_imag(a); }
+};
+template<typename Scalar>
+struct ei_functor_traits<ei_scalar_imag_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
 
 /** \internal
   * \brief Template functor to multiply a scalar by a fixed other one
@@ -259,10 +273,10 @@ template<typename Scalar>
 struct ei_scalar_multiple_op {
   typedef typename ei_packet_traits<Scalar>::type PacketScalar;
   // FIXME default copy constructors seems bugged with std::complex<>
-  inline ei_scalar_multiple_op(const ei_scalar_multiple_op& other) : m_other(other.m_other) { }
-  inline ei_scalar_multiple_op(const Scalar& other) : m_other(other) { }
-  inline Scalar operator() (const Scalar& a) const { return a * m_other; }
-  inline const PacketScalar packetOp(const PacketScalar& a) const
+  EIGEN_STRONG_INLINE ei_scalar_multiple_op(const ei_scalar_multiple_op& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE ei_scalar_multiple_op(const Scalar& other) : m_other(other) { }
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a) const
   { return ei_pmul(a, ei_pset1(m_other)); }
   const Scalar m_other;
 };
@@ -274,10 +288,10 @@ template<typename Scalar, bool HasFloatingPoint>
 struct ei_scalar_quotient1_impl {
   typedef typename ei_packet_traits<Scalar>::type PacketScalar;
   // FIXME default copy constructors seems bugged with std::complex<>
-  inline ei_scalar_quotient1_impl(const ei_scalar_quotient1_impl& other) : m_other(other.m_other) { }
-  inline ei_scalar_quotient1_impl(const Scalar& other) : m_other(static_cast<Scalar>(1) / other) {}
-  inline Scalar operator() (const Scalar& a) const { return a * m_other; }
-  inline const PacketScalar packetOp(const PacketScalar& a) const
+  EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const ei_scalar_quotient1_impl& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const Scalar& other) : m_other(static_cast<Scalar>(1) / other) {}
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
+  EIGEN_STRONG_INLINE const PacketScalar packetOp(const PacketScalar& a) const
   { return ei_pmul(a, ei_pset1(m_other)); }
   const Scalar m_other;
 };
@@ -288,9 +302,9 @@ struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,true> >
 template<typename Scalar>
 struct ei_scalar_quotient1_impl<Scalar,false> {
   // FIXME default copy constructors seems bugged with std::complex<>
-  inline ei_scalar_quotient1_impl(const ei_scalar_quotient1_impl& other) : m_other(other.m_other) { }
-  inline ei_scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
-  inline Scalar operator() (const Scalar& a) const { return a / m_other; }
+  EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const ei_scalar_quotient1_impl& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
   const Scalar m_other;
 };
 template<typename Scalar>
@@ -307,7 +321,7 @@ struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,false> >
   */
 template<typename Scalar>
 struct ei_scalar_quotient1_op : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::HasFloatingPoint > {
-  inline ei_scalar_quotient1_op(const Scalar& other)
+  EIGEN_STRONG_INLINE ei_scalar_quotient1_op(const Scalar& other)
     : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::HasFloatingPoint >(other) {}
 };
 
@@ -316,10 +330,10 @@ struct ei_scalar_quotient1_op : ei_scalar_quotient1_impl<Scalar, NumTraits<Scala
 template<typename Scalar>
 struct ei_scalar_constant_op {
   typedef typename ei_packet_traits<Scalar>::type PacketScalar;
-  inline ei_scalar_constant_op(const ei_scalar_constant_op& other) : m_other(other.m_other) { }
-  inline ei_scalar_constant_op(const Scalar& other) : m_other(other) { }
-  inline const Scalar operator() (int, int = 0) const { return m_other; }
-  inline const PacketScalar packetOp() const { return ei_pset1(m_other); }
+  EIGEN_STRONG_INLINE ei_scalar_constant_op(const ei_scalar_constant_op& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE ei_scalar_constant_op(const Scalar& other) : m_other(other) { }
+  EIGEN_STRONG_INLINE const Scalar operator() (int, int = 0) const { return m_other; }
+  EIGEN_STRONG_INLINE const PacketScalar packetOp() const { return ei_pset1(m_other); }
   const Scalar m_other;
 };
 template<typename Scalar>
@@ -327,18 +341,22 @@ struct ei_functor_traits<ei_scalar_constant_op<Scalar> >
 { enum { Cost = 1, PacketAccess = ei_packet_traits<Scalar>::size>1, IsRepeatable = true }; };
 
 template<typename Scalar> struct ei_scalar_identity_op EIGEN_EMPTY_STRUCT {
-  inline ei_scalar_identity_op(void) {}
-  inline const Scalar operator() (int row, int col) const { return row==col ? Scalar(1) : Scalar(0); }
+  EIGEN_STRONG_INLINE ei_scalar_identity_op(void) {}
+  EIGEN_STRONG_INLINE const Scalar operator() (int row, int col) const { return row==col ? Scalar(1) : Scalar(0); }
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_identity_op<Scalar> >
 { enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
 
-// NOTE quick hack:
 // all functors allow linear access, except ei_scalar_identity_op. So we fix here a quick meta
 // to indicate whether a functor allows linear access, just always answering 'yes' except for
 // ei_scalar_identity_op.
 template<typename Functor> struct ei_functor_has_linear_access { enum { ret = 1 }; };
 template<typename Scalar> struct ei_functor_has_linear_access<ei_scalar_identity_op<Scalar> > { enum { ret = 0 }; };
 
+// in CwiseBinaryOp, we require the Lhs and Rhs to have the same scalar type, except for multiplication
+// where we only require them to have the same _real_ scalar type so one may multiply, say, float by complex<float>.
+template<typename Functor> struct ei_functor_allows_mixing_real_and_complex { enum { ret = 0 }; };
+template<typename Scalar> struct ei_functor_allows_mixing_real_and_complex<ei_scalar_product_op<Scalar> > { enum { ret = 1 }; };
+
 #endif // EIGEN_FUNCTORS_H

Eigen/src/Core/Fuzzy.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -176,19 +176,19 @@ struct ei_fuzzy_selector<Derived,OtherDerived,true>
   typedef typename Derived::RealScalar RealScalar;
   static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
   {
-    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived);
+    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
     ei_assert(self.size() == other.size());
-    return((self - other).norm2() <= std::min(self.norm2(), other.norm2()) * prec * prec);
+    return((self - other).squaredNorm() <= std::min(self.squaredNorm(), other.squaredNorm()) * prec * prec);
   }
   static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
   {
-    return(self.norm2() <= ei_abs2(other * prec));
+    return(self.squaredNorm() <= ei_abs2(other * prec));
   }
   static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
   {
-    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived);
+    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
     ei_assert(self.size() == other.size());
-    return(self.norm2() <= other.norm2() * prec * prec);
+    return(self.squaredNorm() <= other.squaredNorm() * prec * prec);
   }
 };
 
@@ -198,32 +198,32 @@ struct ei_fuzzy_selector<Derived,OtherDerived,false>
   typedef typename Derived::RealScalar RealScalar;
   static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
   {
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived);
+    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
     ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
     typename Derived::Nested nested(self);
     typename OtherDerived::Nested otherNested(other);
-    for(int i = 0; i < self.cols(); i++)
-      if((nested.col(i) - otherNested.col(i)).norm2()
-          > std::min(nested.col(i).norm2(), otherNested.col(i).norm2()) * prec * prec)
+    for(int i = 0; i < self.cols(); ++i)
+      if((nested.col(i) - otherNested.col(i)).squaredNorm()
+          > std::min(nested.col(i).squaredNorm(), otherNested.col(i).squaredNorm()) * prec * prec)
         return false;
     return true;
   }
   static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
   {
     typename Derived::Nested nested(self);
-    for(int i = 0; i < self.cols(); i++)
-      if(nested.col(i).norm2() > ei_abs2(other * prec))
+    for(int i = 0; i < self.cols(); ++i)
+      if(nested.col(i).squaredNorm() > ei_abs2(other * prec))
         return false;
     return true;
   }
   static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
   {
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived);
+    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
     ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
     typename Derived::Nested nested(self);
     typename OtherDerived::Nested otherNested(other);
-    for(int i = 0; i < self.cols(); i++)
-      if(nested.col(i).norm2() > otherNested.col(i).norm2() * prec * prec)
+    for(int i = 0; i < self.cols(); ++i)
+      if(nested.col(i).squaredNorm() > otherNested.col(i).squaredNorm() * prec * prec)
         return false;
     return true;
   }

Eigen/src/Core/GenericPacketMath.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/IO.h

@@ -1,16 +1,17 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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 
+// 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 
+// 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
@@ -18,7 +19,7 @@
 // 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 
+// 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/>.
 
@@ -57,7 +58,7 @@ struct IOFormat
     coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
   {
     rowSpacer = "";
-    int i=matSuffix.length()-1;
+    int i = int(matSuffix.length())-1;
     while (i>=0 && matSuffix[i]!='\n')
     {
       rowSpacer += ' ';
@@ -80,7 +81,7 @@ struct IOFormat
   * This class represents an expression with stream operators controlled by a given IOFormat.
   * It is the return type of MatrixBase::format()
   * and most of the time this is the only way it is used.
-  * 
+  *
   * See class IOFormat for some examples.
   *
   * \sa MatrixBase::format(), class IOFormat
@@ -121,33 +122,33 @@ MatrixBase<Derived>::format(const IOFormat& fmt) const
 /** \internal
   * print the matrix \a _m to the output stream \a s using the output format \a fmt */
 template<typename Derived>
-std::ostream & ei_print_matrix(std::ostream & s, const MatrixBase<Derived> & _m,
-                               const IOFormat& fmt = IOFormat())
+std::ostream & ei_print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
 {
   const typename Derived::Nested m = _m;
+
   int width = 0;
   if (fmt.flags & AlignCols)
   {
     // compute the largest width
-    for(int j = 1; j < m.cols(); j++)
-      for(int i = 0; i < m.rows(); i++)
+    for(int j = 1; j < m.cols(); ++j)
+      for(int i = 0; i < m.rows(); ++i)
       {
         std::stringstream sstr;
         sstr.precision(fmt.precision);
         sstr << m.coeff(i,j);
-        width = std::max<int>(width, sstr.str().length());
+        width = std::max<int>(width, int(sstr.str().length()));
       }
   }
   s.precision(fmt.precision);
   s << fmt.matPrefix;
-  for(int i = 0; i < m.rows(); i++)
+  for(int i = 0; i < m.rows(); ++i)
   {
     if (i)
       s << fmt.rowSpacer;
     s << fmt.rowPrefix;
     if(width) s.width(width);
     s << m.coeff(i, 0);
-    for(int j = 1; j < m.cols(); j++)
+    for(int j = 1; j < m.cols(); ++j)
     {
       s << fmt.coeffSeparator;
       if (width) s.width(width);
@@ -163,8 +164,12 @@ std::ostream & ei_print_matrix(std::ostream & s, const MatrixBase<Derived> & _m,
 
 /** \relates MatrixBase
   *
-  * Outputs the matrix, laid out as an array as usual, to the given stream.
-  * You can control the way the matrix is printed using MatrixBase::format().
+  * Outputs the matrix, to the given stream.
+  *
+  * If you wish to print the matrix with a format different than the default, use MatrixBase::format().
+  *
+  * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
+  * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters.
   *
   * \sa MatrixBase::format()
   */
@@ -173,7 +178,7 @@ std::ostream & operator <<
 (std::ostream & s,
  const MatrixBase<Derived> & m)
 {
-  return ei_print_matrix(s, m.eval());
+  return ei_print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
 }
 
 #endif // EIGEN_IO_H

Eigen/src/Core/Map.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -40,9 +40,9 @@
   * It can be used to let Eigen interface without any overhead with non-Eigen data structures,
   * such as plain C arrays or structures from other libraries.
   *
-  * This class is the return type of Matrix::map() but can also be used directly.
+  * This class is the return type of Matrix::Map() but can also be used directly.
   *
-  * \sa Matrix::map()
+  * \sa Matrix::Map()
   */
 template<typename MatrixType, int _PacketAccess>
 struct ei_traits<Map<MatrixType, _PacketAccess> > : public ei_traits<MatrixType>
@@ -90,7 +90,7 @@ template<typename MatrixType, int PacketAccess> class Map
 
     inline void resize(int size)
     {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(MatrixType);
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(MatrixType)
       EIGEN_ONLY_USED_FOR_DEBUG(size);
       ei_assert(size == this->size());
     }
@@ -106,13 +106,13 @@ template<typename MatrixType, int PacketAccess> class Map
   * for the dimensions.
   *
   * \sa Matrix(const Scalar *, int), Matrix(const Scalar *, int, int),
-  * Matrix::map(const Scalar *)
+  * Matrix::Map(const Scalar *)
   */
 template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder, int _MaxRows, int _MaxCols>
 inline Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols>
   ::Matrix(const Scalar *data)
 {
-  *this = Map<Matrix>(data);
+  *this = Eigen::Map<Matrix>(data);
 }
 
 #endif // EIGEN_MAP_H

Eigen/src/Core/MapBase.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -53,7 +53,7 @@ template<typename Derived> class MapBase
       ColsAtCompileTime = ei_traits<Derived>::ColsAtCompileTime,
       SizeAtCompileTime = Base::SizeAtCompileTime
     };
-    
+
     typedef typename ei_traits<Derived>::AlignedDerivedType AlignedDerivedType;
     typedef typename ei_traits<Derived>::Scalar Scalar;
     typedef typename Base::PacketScalar PacketScalar;
@@ -83,7 +83,7 @@ template<typename Derived> class MapBase
       else // column-major
         return const_cast<Scalar*>(m_data)[row + col * stride()];
     }
-    
+
     inline const Scalar coeff(int index) const
     {
       ei_assert(Derived::IsVectorAtCompileTime || (ei_traits<Derived>::Flags & LinearAccessBit));
@@ -138,28 +138,29 @@ template<typename Derived> class MapBase
               m_cols(ColsAtCompileTime == Dynamic ? size : ColsAtCompileTime)
     {
       EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      ei_assert(size > 0);
+      ei_assert(size > 0 || data == 0);
       ei_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
     }
 
     inline MapBase(const Scalar* data, int rows, int cols)
             : m_data(data), m_rows(rows), m_cols(cols)
     {
-      ei_assert(rows > 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
-             && cols > 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
+      ei_assert( (data == 0)
+              || (   rows > 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
+                  && cols > 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
     }
 
     template<typename OtherDerived>
     Derived& operator+=(const MatrixBase<OtherDerived>& other)
     { return derived() = forceAligned() + other; }
-    
+
     template<typename OtherDerived>
     Derived& operator-=(const MatrixBase<OtherDerived>& other)
     { return derived() = forceAligned() - other; }
 
     Derived& operator*=(const Scalar& other)
     { return derived() = forceAligned() * other; }
-    
+
     Derived& operator/=(const Scalar& other)
     { return derived() = forceAligned() / other; }
 

Eigen/src/Core/MathFunctions.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -51,7 +51,7 @@ inline int ei_sin(int)  { ei_assert(false); return 0; }
 inline int ei_cos(int)  { ei_assert(false); return 0; }
 
 #if EIGEN_GNUC_AT_LEAST(4,3)
-inline int ei_pow(int x, int y) { return std::pow(x, y); }
+inline int ei_pow(int x, int y) { return int(std::pow(x, y)); }
 #else
 inline int ei_pow(int x, int y) { return int(std::pow(double(x), y)); }
 #endif
@@ -101,9 +101,9 @@ template<> inline float ei_random(float a, float b)
   int i;
   do { i = ei_random<int>(256*int(a),256*int(b));
   } while(i==0);
-  return i/256.f;
+  return float(i)/256.f;
 #else
-  return a + (b-a) * std::rand() / RAND_MAX;
+  return a + (b-a) * float(std::rand()) / float(RAND_MAX);
 #endif
 }
 template<> inline float ei_random()
@@ -254,7 +254,7 @@ inline long double ei_pow(long double x, long double y)  { return std::pow(x, y)
 
 template<> inline long double ei_random(long double a, long double b)
 {
-  return ei_random<double>(a,b);
+  return ei_random<double>(static_cast<double>(a),static_cast<double>(b));
 }
 template<> inline long double ei_random()
 {

Eigen/src/Core/Matrix.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -25,63 +25,85 @@
 #ifndef EIGEN_MATRIX_H
 #define EIGEN_MATRIX_H
 
-
 /** \class Matrix
   *
   * \brief The matrix class, also used for vectors and row-vectors
   *
-  * \param _Scalar the scalar type, i.e. the type of the coefficients
-  * \param _Rows the number of rows at compile-time. Use the special value \a Dynamic to
-  *              specify that the number of rows is dynamic, i.e. is not fixed at compile-time.
-  * \param _Cols the number of columns at compile-time. Use the special value \a Dynamic to
-  *              specify that the number of columns is dynamic, i.e. is not fixed at compile-time.
-  * \param _StorageOrder can be either RowMajor or ColMajor. The default is ColMajor.
-  * \param _MaxRows the maximum number of rows at compile-time. By default this is equal to \a _Rows.
-  *              The most common exception is when you don't know the exact number of rows, but know that
-  *              it is smaller than some given value. Then you can set \a _MaxRows to that value, and set
-  *              _Rows to \a Dynamic.
-  * \param _MaxCols the maximum number of cols at compile-time. By default this is equal to \a _Cols.
-  *              The most common exception is when you don't know the exact number of cols, but know that
-  *              it is smaller than some given value. Then you can set \a _MaxCols to that value, and set
-  *              _Cols to \a Dynamic.
+  * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen.
+  * Vectors are matrices with one column, and row-vectors are matrices with one row.
   *
-  * This single class template covers all kinds of matrix and vectors that Eigen can handle.
-  * All matrix and vector types are just typedefs to specializations of this class template.
+  * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
   *
-  * These typedefs are as follows:
-  * \li \c %Matrix\#\#Size\#\#Type for square matrices
-  * \li \c Vector\#\#Size\#\#Type for vectors (matrices with one column)
-  * \li \c RowVector\#\#Size\#\#Type for row-vectors (matrices with one row)
+  * The first three template parameters are required:
+  * \param _Scalar Numeric type, i.e. float, double, int
+  * \param _Rows Number of rows, or \b Dynamic
+  * \param _Cols Number of columns, or \b Dynamic
   *
-  * where \c Size can be
-  * \li \c 2 for fixed size 2
-  * \li \c 3 for fixed size 3
-  * \li \c 4 for fixed size 4
-  * \li \c X for dynamic size
+  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
+  * \param _Options A combination of either \b Matrix_RowMajor or \b Matrix_ColMajor, and of either
+  *                 \b Matrix_AutoAlign or \b Matrix_DontAlign.
+  *                 The former controls storage order, and defaults to column-major. The latter controls alignment, which is required
+  *                 for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
+  * \param _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
+  * \param _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
   *
-  * and \c Type can be
-  * \li \c i for type \c int
-  * \li \c f for type \c float
-  * \li \c d for type \c double
-  * \li \c cf for type \c std::complex<float>
-  * \li \c cd for type \c std::complex<double>
+  * Eigen provides a number of typedefs covering the usual cases. Here are some examples:
   *
-  * Examples:
-  * \li \c Matrix2d is a typedef for \c Matrix<double,2,2>
-  * \li \c VectorXf is a typedef for \c Matrix<float,Dynamic,1>
-  * \li \c RowVector3i is a typedef for \c Matrix<int,1,3>
+  * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
+  * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
+  * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
   *
-  * See \ref matrixtypedefs for an explicit list of all matrix typedefs.
+  * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
+  * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
   *
-  * Of course these typedefs do not exhaust all the possibilities offered by the Matrix class
-  * template, they only address some of the most common cases. For instance, if you want a
-  * fixed-size matrix with 3 rows and 5 columns, there is no typedef for that, so you should use
-  * \c Matrix<double,3,5>.
+  * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
   *
-  * Note that most of the API is in the base class MatrixBase.
+  * You can access elements of vectors and matrices using normal subscripting:
+  *
+  * \code
+  * Eigen::VectorXd v(10);
+  * v[0] = 0.1;
+  * v[1] = 0.2;
+  * v(0) = 0.3;
+  * v(1) = 0.4;
+  *
+  * Eigen::MatrixXi m(10, 10);
+  * m(0, 1) = 1;
+  * m(0, 2) = 2;
+  * m(0, 3) = 3;
+  * \endcode
+  *
+  * <i><b>Some notes:</b></i>
+  *
+  * <dl>
+  * <dt><b>\anchor dense Dense versus sparse:</b></dt>
+  * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module.
+  *
+  * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array.
+  * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.</dd>
+  *
+  * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt>
+  * <dd>Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array
+  * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up
+  * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time.
+  *
+  * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime
+  * variables, and the array of coefficients is allocated dynamically on the heap.
+  *
+  * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map.
+  * If you want this behavior, see the Sparse module.</dd>
+  *
+  * <dt><b>\anchor maxrows _MaxRows and _MaxCols:</b></dt>
+  * <dd>In most cases, one just leaves these parameters to the default values.
+  * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
+  * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot
+  * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols
+  * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
+  *
+  * \see MatrixBase for the majority of the API methods for matrices
   */
-template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder, int _MaxRows, int _MaxCols>
-struct ei_traits<Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols> >
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
+struct ei_traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
 {
   typedef _Scalar Scalar;
   enum {
@@ -89,33 +111,60 @@ struct ei_traits<Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols
     ColsAtCompileTime = _Cols,
     MaxRowsAtCompileTime = _MaxRows,
     MaxColsAtCompileTime = _MaxCols,
-    Flags = ei_compute_matrix_flags<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols>::ret,
+    Flags = ei_compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
     CoeffReadCost = NumTraits<Scalar>::ReadCost,
     SupportedAccessPatterns = RandomAccessPattern
   };
 };
 
-template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder, int _MaxRows, int _MaxCols>
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
 class Matrix
-  : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols> >
-    #ifdef EIGEN_VECTORIZE
-    , public ei_with_aligned_operator_new<_Scalar,ei_size_at_compile_time<_Rows,_Cols>::ret>
-    #endif
+  : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
 {
   public:
     EIGEN_GENERIC_PUBLIC_INTERFACE(Matrix)
+    enum { Options = _Options };
     friend class Eigen::Map<Matrix, Unaligned>;
+    typedef class Eigen::Map<Matrix, Unaligned> UnalignedMapType;
     friend class Eigen::Map<Matrix, Aligned>;
+    typedef class Eigen::Map<Matrix, Aligned> AlignedMapType;
 
   protected:
-    ei_matrix_storage<Scalar, MaxSizeAtCompileTime, RowsAtCompileTime, ColsAtCompileTime> m_storage;
+    ei_matrix_storage<Scalar, MaxSizeAtCompileTime, RowsAtCompileTime, ColsAtCompileTime, Options> m_storage;
+
+  public:
+    enum { NeedsToAlign = (Options&Matrix_AutoAlign) == Matrix_AutoAlign
+                          && SizeAtCompileTime!=Dynamic && ((sizeof(Scalar)*SizeAtCompileTime)%16)==0 };
+    typedef typename ei_meta_if<NeedsToAlign, ei_byte_forcing_aligned_malloc, char>::ret ByteAlignedAsNeeded;
+    void *operator new(size_t size) throw()
+    {
+      return ei_aligned_malloc<ByteAlignedAsNeeded>(size);
+    }
+
+    void *operator new(size_t, void *ptr) throw()
+    {
+      return static_cast<void*>(ptr);
+    }
+
+    void *operator new[](size_t size) throw()
+    {
+      return ei_aligned_malloc<ByteAlignedAsNeeded>(size);
+    }
+
+    void *operator new[](size_t, void *ptr) throw()
+    {
+      return static_cast<void*>(ptr);
+    }
+
+    void operator delete(void * ptr) { ei_aligned_free(static_cast<ByteAlignedAsNeeded *>(ptr), 0); }
+    void operator delete[](void * ptr) { ei_aligned_free(static_cast<ByteAlignedAsNeeded *>(ptr), 0); }
 
   public:
 
-    inline int rows() const { return m_storage.rows(); }
-    inline int cols() const { return m_storage.cols(); }
+    EIGEN_STRONG_INLINE int rows() const { return m_storage.rows(); }
+    EIGEN_STRONG_INLINE int cols() const { return m_storage.cols(); }
 
-    inline int stride(void) const
+    EIGEN_STRONG_INLINE int stride(void) const
     {
       if(Flags & RowMajorBit)
         return m_storage.cols();
@@ -123,7 +172,7 @@ class Matrix
         return m_storage.rows();
     }
 
-    inline const Scalar& coeff(int row, int col) const
+    EIGEN_STRONG_INLINE const Scalar& coeff(int row, int col) const
     {
       if(Flags & RowMajorBit)
         return m_storage.data()[col + row * m_storage.cols()];
@@ -131,12 +180,12 @@ class Matrix
         return m_storage.data()[row + col * m_storage.rows()];
     }
 
-    inline const Scalar& coeff(int index) const
+    EIGEN_STRONG_INLINE const Scalar& coeff(int index) const
     {
       return m_storage.data()[index];
     }
 
-    inline Scalar& coeffRef(int row, int col)
+    EIGEN_STRONG_INLINE Scalar& coeffRef(int row, int col)
     {
       if(Flags & RowMajorBit)
         return m_storage.data()[col + row * m_storage.cols()];
@@ -144,13 +193,13 @@ class Matrix
         return m_storage.data()[row + col * m_storage.rows()];
     }
 
-    inline Scalar& coeffRef(int index)
+    EIGEN_STRONG_INLINE Scalar& coeffRef(int index)
     {
       return m_storage.data()[index];
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int row, int col) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int row, int col) const
     {
       return ei_ploadt<Scalar, LoadMode>
                (m_storage.data() + (Flags & RowMajorBit
@@ -159,13 +208,13 @@ class Matrix
     }
 
     template<int LoadMode>
-    inline PacketScalar packet(int index) const
+    EIGEN_STRONG_INLINE PacketScalar packet(int index) const
     {
       return ei_ploadt<Scalar, LoadMode>(m_storage.data() + index);
     }
 
     template<int StoreMode>
-    inline void writePacket(int row, int col, const PacketScalar& x)
+    EIGEN_STRONG_INLINE void writePacket(int row, int col, const PacketScalar& x)
     {
       ei_pstoret<Scalar, PacketScalar, StoreMode>
               (m_storage.data() + (Flags & RowMajorBit
@@ -174,19 +223,30 @@ class Matrix
     }
 
     template<int StoreMode>
-    inline void writePacket(int index, const PacketScalar& x)
+    EIGEN_STRONG_INLINE void writePacket(int index, const PacketScalar& x)
     {
       ei_pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
     }
 
     /** \returns a const pointer to the data array of this matrix */
-    inline const Scalar *data() const
+    EIGEN_STRONG_INLINE const Scalar *data() const
     { return m_storage.data(); }
 
     /** \returns a pointer to the data array of this matrix */
-    inline Scalar *data()
+    EIGEN_STRONG_INLINE Scalar *data()
     { return m_storage.data(); }
 
+    /** Resizes \c *this to a \a rows x \a cols matrix.
+      *
+      * Makes sense for dynamic-size matrices only.
+      *
+      * If the current number of coefficients of \c *this exactly matches the
+      * product \a rows * \a cols, then no memory allocation is performed and
+      * the current values are left unchanged. In all other cases, including
+      * shrinking, the data is reallocated and all previous values are lost.
+      *
+      * \sa resize(int) for vectors.
+      */
     inline void resize(int rows, int cols)
     {
       ei_assert(rows > 0
@@ -198,8 +258,13 @@ class Matrix
       m_storage.resize(rows * cols, rows, cols);
     }
 
+    /** Resizes \c *this to a vector of length \a size
+      *
+      * \sa resize(int,int) for the details.
+      */
     inline void resize(int size)
     {
+      ei_assert(size>0 && "a vector cannot be resized to 0 length");
       EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
       if(RowsAtCompileTime == 1)
         m_storage.resize(size, 1, size);
@@ -207,16 +272,36 @@ class Matrix
         m_storage.resize(size, size, 1);
     }
 
-    /** Copies the value of the expression \a other into *this.
+    /** Copies the value of the expression \a other into \c *this.
       *
-      * *this is resized (if possible) to match the dimensions of \a other.
+      * \warning Note that the sizes of \c *this and \a other must match.
+      * If you want automatic resizing, then you must use the function set().
       *
       * As a special exception, copying a row-vector into a vector (and conversely)
-      * is allowed. The resizing, if any, is then done in the appropriate way so that
-      * row-vectors remain row-vectors and vectors remain vectors.
+      * is allowed.
+      *
+      * \sa set()
       */
     template<typename OtherDerived>
-    inline Matrix& operator=(const MatrixBase<OtherDerived>& other)
+    EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other)
+    {
+      ei_assert(m_storage.data()!=0 && "you cannot use operator= with a non initialized matrix (instead use set()");
+      return Base::operator=(other.derived());
+    }
+
+    /** Copies the value of the expression \a other into \c *this with automatic resizing.
+      *
+      * This function is the same than the assignment operator = excepted that \c *this might
+      * be resized to match the dimensions of \a other.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      *
+      * \sa operator=()
+      */
+    template<typename OtherDerived>
+    inline Matrix& set(const MatrixBase<OtherDerived>& other)
     {
       if(RowsAtCompileTime == 1)
       {
@@ -235,7 +320,7 @@ class Matrix
     /** This is a special case of the templated operator=. Its purpose is to
       * prevent a default operator= from hiding the templated operator=.
       */
-    inline Matrix& operator=(const Matrix& other)
+    EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
     {
       return operator=<Matrix>(other);
     }
@@ -249,10 +334,28 @@ class Matrix
       *
       * For fixed-size matrices, does nothing.
       *
-      * For dynamic-size matrices, initializes with initial size 1x1, which is inefficient, hence
-      * when performance matters one should avoid using this constructor on dynamic-size matrices.
+      * For dynamic-size matrices, creates an empty matrix of size null.
+      * \warning while creating such an \em null matrix is allowed, it \b cannot
+      * \b be \b used before having being resized or initialized with the function set().
+      * In particular, initializing a null matrix with operator = is not supported.
+      * Finally, this constructor is the unique way to create null matrices: resizing
+      * a matrix to 0 is not supported.
+      * Here are some examples:
+      * \code
+      * MatrixXf r = MatrixXf::Random(3,4); // create a random matrix of floats
+      * MatrixXf m1, m2;  // creates two null matrices of float
+      *
+      * m1 = r;           // illegal (raise an assertion)
+      * r = m1;           // illegal (raise an assertion)
+      * m1 = m2;          // illegal (raise an assertion)
+      * m1.set(r);        // OK
+      * m2.resize(3,4);
+      * m2 = r;           // OK
+      * \endcode
+      *
+      * \sa resize(int,int), set()
       */
-    inline explicit Matrix() : m_storage(1, 1, 1)
+    EIGEN_STRONG_INLINE explicit Matrix() : m_storage()
     {
       ei_assert(RowsAtCompileTime > 0 && ColsAtCompileTime > 0);
     }
@@ -263,7 +366,7 @@ class Matrix
       * it is redundant to pass the dimension here, so it makes more sense to use the default
       * constructor Matrix() instead.
       */
-    inline explicit Matrix(int dim)
+    EIGEN_STRONG_INLINE explicit Matrix(int dim)
       : m_storage(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
     {
       EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
@@ -281,7 +384,7 @@ class Matrix
       *     it is redundant to pass these parameters, so one should use the default constructor
       *     Matrix() instead.
       */
-    inline Matrix(int x, int y) : m_storage(x*y, x, y)
+    EIGEN_STRONG_INLINE Matrix(int x, int y) : m_storage(x*y, x, y)
     {
       if((RowsAtCompileTime == 1 && ColsAtCompileTime == 2)
       || (RowsAtCompileTime == 2 && ColsAtCompileTime == 1))
@@ -296,31 +399,31 @@ class Matrix
       }
     }
     /** constructs an initialized 2D vector with given coefficients */
-    inline Matrix(const float& x, const float& y)
+    EIGEN_STRONG_INLINE Matrix(const float& x, const float& y)
     {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2);
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2)
       m_storage.data()[0] = x;
       m_storage.data()[1] = y;
     }
     /** constructs an initialized 2D vector with given coefficients */
-    inline Matrix(const double& x, const double& y)
+    EIGEN_STRONG_INLINE Matrix(const double& x, const double& y)
     {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2);
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 2)
       m_storage.data()[0] = x;
       m_storage.data()[1] = y;
     }
     /** constructs an initialized 3D vector with given coefficients */
-    inline Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
+    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
     {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3);
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
       m_storage.data()[0] = x;
       m_storage.data()[1] = y;
       m_storage.data()[2] = z;
     }
     /** constructs an initialized 4D vector with given coefficients */
-    inline Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
+    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
     {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4);
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
       m_storage.data()[0] = x;
       m_storage.data()[1] = y;
       m_storage.data()[2] = z;
@@ -331,14 +434,14 @@ class Matrix
 
     /** Constructor copying the value of the expression \a other */
     template<typename OtherDerived>
-    inline Matrix(const MatrixBase<OtherDerived>& other)
+    EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other)
              : m_storage(other.rows() * other.cols(), other.rows(), other.cols())
     {
       ei_assign_selector<Matrix,OtherDerived,false>::run(*this, other.derived());
       //Base::operator=(other.derived());
     }
     /** Copy constructor */
-    inline Matrix(const Matrix& other)
+    EIGEN_STRONG_INLINE Matrix(const Matrix& other)
             : Base(), m_storage(other.rows() * other.cols(), other.rows(), other.cols())
     {
       Base::lazyAssign(other);
@@ -346,18 +449,10 @@ class Matrix
     /** Destructor */
     inline ~Matrix() {}
 
-    /** Override MatrixBase::eval() since matrices don't need to be evaluated, it is enough to just read them.
-      * This prevents a useless copy when doing e.g. "m1 = m2.eval()"
-      */
-    const Matrix& eval() const
-    {
-      return *this;
-    }
-
     /** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
       * data pointers.
       */
-    void swap(Matrix& other)
+    inline void swap(Matrix& other)
     {
       if (Base::SizeAtCompileTime==Dynamic)
         m_storage.swap(other.m_storage);
@@ -365,12 +460,52 @@ class Matrix
         this->Base::swap(other);
     }
 
+    /** \name Map
+      * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
+      * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
+      * \a data pointers.
+      *
+      * \see class Map
+      */
+    //@{
+    inline static const UnalignedMapType Map(const Scalar* data)
+    { return UnalignedMapType(data); }
+    inline static UnalignedMapType Map(Scalar* data)
+    { return UnalignedMapType(data); }
+    inline static const UnalignedMapType Map(const Scalar* data, int size)
+    { return UnalignedMapType(data, size); }
+    inline static UnalignedMapType Map(Scalar* data, int size)
+    { return UnalignedMapType(data, size); }
+    inline static const UnalignedMapType Map(const Scalar* data, int rows, int cols)
+    { return UnalignedMapType(data, rows, cols); }
+    inline static UnalignedMapType Map(Scalar* data, int rows, int cols)
+    { return UnalignedMapType(data, rows, cols); }
+
+    inline static const AlignedMapType MapAligned(const Scalar* data)
+    { return AlignedMapType(data); }
+    inline static AlignedMapType MapAligned(Scalar* data)
+    { return AlignedMapType(data); }
+    inline static const AlignedMapType MapAligned(const Scalar* data, int size)
+    { return AlignedMapType(data, size); }
+    inline static AlignedMapType MapAligned(Scalar* data, int size)
+    { return AlignedMapType(data, size); }
+    inline static const AlignedMapType MapAligned(const Scalar* data, int rows, int cols)
+    { return AlignedMapType(data, rows, cols); }
+    inline static AlignedMapType MapAligned(Scalar* data, int rows, int cols)
+    { return AlignedMapType(data, rows, cols); }
+    //@}
+
 /////////// Geometry module ///////////
 
     template<typename OtherDerived>
     explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
     template<typename OtherDerived>
     Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
+
+    // allow to extend Matrix outside Eigen
+    #ifdef EIGEN_MATRIX_PLUGIN
+    #include EIGEN_MATRIX_PLUGIN
+    #endif
 };
 
 /** \defgroup matrixtypedefs Global matrix typedefs

Eigen/src/Core/MatrixBase.h

@@ -1,7 +1,8 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -55,10 +56,12 @@ template<typename Derived> class MatrixBase
 {
   public:
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     class InnerIterator;
 
     typedef typename ei_traits<Derived>::Scalar Scalar;
     typedef typename ei_packet_traits<Scalar>::type PacketScalar;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
     enum {
 
@@ -139,6 +142,7 @@ template<typename Derived> class MatrixBase
       ei_assert(ei_are_flags_consistent<Flags>::ret);
     }
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     /** This is the "real scalar" type; if the \a Scalar type is already real numbers
       * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
       * \a Scalar is \a std::complex<T> then RealScalar is \a T.
@@ -150,6 +154,7 @@ template<typename Derived> class MatrixBase
     /** type of the equivalent square matrix */
     typedef Matrix<Scalar,EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime),
                           EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
     /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
     inline int rows() const { return derived().rows(); }
@@ -173,9 +178,21 @@ template<typename Derived> class MatrixBase
       * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
     int innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
 
-    /** \internal the type to which the expression gets evaluated (needed by MSVC) */
-    typedef typename ei_eval<Derived>::type EvalType;
-    /** \internal Represents a constant matrix */
+#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 guaranteed however, that the return type of eval() is either
+      * PlainMatrixType or const PlainMatrixType&.
+      */
+    typedef typename ei_plain_matrix_type<Derived>::type PlainMatrixType;
+    /** \internal the column-major 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!
+      * The only difference from PlainMatrixType is that PlainMatrixType_ColMajor is guaranteed to be column-major.
+      */
+    typedef typename ei_plain_matrix_type<Derived>::type PlainMatrixType_ColMajor;
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
     typedef CwiseNullaryOp<ei_scalar_constant_op<Scalar>,Derived> ConstantReturnType;
     /** \internal Represents a scalar multiple of a matrix */
     typedef CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, Derived> ScalarMultipleReturnType;
@@ -188,8 +205,10 @@ template<typename Derived> class MatrixBase
                      >::ret ConjugateReturnType;
     /** \internal the return type of MatrixBase::real() */
     typedef CwiseUnaryOp<ei_scalar_real_op<Scalar>, Derived> RealReturnType;
+    /** \internal the return type of MatrixBase::imag() */
+    typedef CwiseUnaryOp<ei_scalar_imag_op<Scalar>, Derived> ImagReturnType;
     /** \internal the return type of MatrixBase::adjoint() */
-    typedef Transpose<NestByValue<typename ei_cleantype<ConjugateReturnType>::type> >
+    typedef Eigen::Transpose<NestByValue<typename ei_cleantype<ConjugateReturnType>::type> >
             AdjointReturnType;
     /** \internal the return type of MatrixBase::eigenvalues() */
     typedef Matrix<typename NumTraits<typename ei_traits<Derived>::Scalar>::Real, ei_traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
@@ -203,6 +222,7 @@ template<typename Derived> class MatrixBase
     typedef Block<CwiseNullaryOp<ei_scalar_identity_op<Scalar>, SquareMatrixType>,
                   ei_traits<Derived>::RowsAtCompileTime,
                   ei_traits<Derived>::ColsAtCompileTime> BasisReturnType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
 
     /** Copies \a other into *this. \returns a reference to *this. */
@@ -253,6 +273,7 @@ template<typename Derived> class MatrixBase
     Scalar& operator[](int index);
     Scalar& operator()(int index);
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename OtherDerived>
     void copyCoeff(int row, int col, const MatrixBase<OtherDerived>& other);
     template<typename OtherDerived>
@@ -261,6 +282,7 @@ template<typename Derived> class MatrixBase
     void copyPacket(int row, int col, const MatrixBase<OtherDerived>& other);
     template<typename OtherDerived, int StoreMode, int LoadMode>
     void copyPacket(int index, const MatrixBase<OtherDerived>& other);
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
     template<int LoadMode>
     PacketScalar packet(int row, int col) const;
@@ -320,7 +342,8 @@ template<typename Derived> class MatrixBase
     Derived& operator*=(const MatrixBase<OtherDerived>& other);
 
     template<typename OtherDerived>
-    typename OtherDerived::Eval solveTriangular(const MatrixBase<OtherDerived>& other) const;
+    typename ei_plain_matrix_type_column_major<OtherDerived>::type
+		solveTriangular(const MatrixBase<OtherDerived>& other) const;
 
     template<typename OtherDerived>
     void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const;
@@ -328,13 +351,15 @@ template<typename Derived> class MatrixBase
 
     template<typename OtherDerived>
     Scalar dot(const MatrixBase<OtherDerived>& other) const;
+    RealScalar squaredNorm() const;
     RealScalar norm2() const;
     RealScalar norm()  const;
-    const EvalType normalized() const;
+    const PlainMatrixType normalized() const;
     void normalize();
 
-    Transpose<Derived> transpose();
-    const Transpose<Derived> transpose() const;
+    Eigen::Transpose<Derived> transpose();
+    const Eigen::Transpose<Derived> transpose() const;
+    void transposeInPlace();
     const AdjointReturnType adjoint() const;
 
 
@@ -351,8 +376,8 @@ template<typename Derived> class MatrixBase
     const typename BlockReturnType<Derived>::Type
     block(int startRow, int startCol, int blockRows, int blockCols) const;
 
-    typename BlockReturnType<Derived>::SubVectorType block(int start, int size);
-    const typename BlockReturnType<Derived>::SubVectorType block(int start, int size) const;
+    typename BlockReturnType<Derived>::SubVectorType segment(int start, int size);
+    const typename BlockReturnType<Derived>::SubVectorType segment(int start, int size) const;
 
     typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size);
     const typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size) const;
@@ -379,8 +404,8 @@ template<typename Derived> class MatrixBase
     template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType end();
     template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType end() const;
 
-    template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType block(int start);
-    template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType block(int start) const;
+    template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType segment(int start);
+    template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType segment(int start) const;
 
     DiagonalCoeffs<Derived> diagonal();
     const DiagonalCoeffs<Derived> diagonal() const;
@@ -445,8 +470,8 @@ template<typename Derived> class MatrixBase
     bool isIdentity(RealScalar prec = precision<Scalar>()) const;
     bool isDiagonal(RealScalar prec = precision<Scalar>()) const;
 
-    bool isUpper(RealScalar prec = precision<Scalar>()) const;
-    bool isLower(RealScalar prec = precision<Scalar>()) const;
+    bool isUpperTriangular(RealScalar prec = precision<Scalar>()) const;
+    bool isLowerTriangular(RealScalar prec = precision<Scalar>()) const;
 
     template<typename OtherDerived>
     bool isOrthogonal(const MatrixBase<OtherDerived>& other,
@@ -467,11 +492,11 @@ template<typename Derived> class MatrixBase
 
     /** \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.
       */
-    EIGEN_ALWAYS_INLINE const typename ei_eval<Derived>::type eval() const
-    {
-      return typename ei_eval<Derived>::type(derived());
-    }
+    EIGEN_STRONG_INLINE const typename ei_eval<Derived>::type eval() const
+    { return typename ei_eval<Derived>::type(derived()); }
 
     template<typename OtherDerived>
     void swap(const MatrixBase<OtherDerived>& other);
@@ -492,6 +517,7 @@ template<typename Derived> class MatrixBase
 
     ConjugateReturnType conjugate() const;
     const RealReturnType real() const;
+    const ImagReturnType imag() const;
 
     template<typename CustomUnaryOp>
     const CwiseUnaryOp<CustomUnaryOp, Derived> unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const;
@@ -517,11 +543,12 @@ template<typename Derived> class MatrixBase
     template<typename Visitor>
     void visit(Visitor& func) const;
 
-
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
     inline Derived& derived() { return *static_cast<Derived*>(this); }
     inline Derived& const_cast_derived() const
     { return *static_cast<Derived*>(const_cast<MatrixBase*>(this)); }
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
     const Cwise<Derived> cwise() const;
     Cwise<Derived> cwise();
@@ -544,44 +571,50 @@ template<typename Derived> class MatrixBase
     const Select<Derived,ThenDerived,ElseDerived>
     select(const MatrixBase<ThenDerived>& thenMatrix,
            const MatrixBase<ElseDerived>& elseMatrix) const;
-   
+
     template<typename ThenDerived>
     inline const Select<Derived,ThenDerived, NestByValue<typename ThenDerived::ConstantReturnType> >
     select(const MatrixBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
-    
+
     template<typename ElseDerived>
     inline const Select<Derived, NestByValue<typename ElseDerived::ConstantReturnType>, ElseDerived >
     select(typename ElseDerived::Scalar thenScalar, const MatrixBase<ElseDerived>& elseMatrix) const;
 
+    template<int p> RealScalar lpNorm() const;
+
 /////////// LU module ///////////
 
-    const LU<EvalType> lu() const;
-    const EvalType inverse() const;
-    void computeInverse(EvalType *result) const;
+    const LU<PlainMatrixType> lu() const;
+    const PlainMatrixType inverse() const;
+    void computeInverse(PlainMatrixType *result) const;
     Scalar determinant() const;
 
 /////////// Cholesky module ///////////
 
-    const Cholesky<EvalType> cholesky() const;
-    const CholeskyWithoutSquareRoot<EvalType> choleskyNoSqrt() const;
+    const LLT<PlainMatrixType>  llt() const;
+    const LDLT<PlainMatrixType> ldlt() const;
+    // deprecated:
+    const Cholesky<PlainMatrixType> cholesky() const;
+    const CholeskyWithoutSquareRoot<PlainMatrixType> choleskyNoSqrt() const;
 
 /////////// QR module ///////////
 
-    const QR<EvalType> qr() const;
+    const QR<PlainMatrixType> qr() const;
 
     EigenvaluesReturnType eigenvalues() const;
     RealScalar operatorNorm() const;
 
 /////////// SVD module ///////////
 
-    SVD<EvalType> svd() const;
+    SVD<PlainMatrixType> svd() const;
 
 /////////// Geometry module ///////////
 
     template<typename OtherDerived>
-    EvalType cross(const MatrixBase<OtherDerived>& other) const;
-    EvalType unitOrthogonal(void) const;
-    
+    PlainMatrixType cross(const MatrixBase<OtherDerived>& other) const;
+    PlainMatrixType unitOrthogonal(void) const;
+    Matrix<Scalar,3,1> eulerAngles(int a0, int a1, int a2) const;
+
     #ifdef EIGEN_MATRIXBASE_PLUGIN
     #include EIGEN_MATRIXBASE_PLUGIN
     #endif

Eigen/src/Core/MatrixStorage.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -26,6 +26,27 @@
 #ifndef EIGEN_MATRIXSTORAGE_H
 #define EIGEN_MATRIXSTORAGE_H
 
+/** \internal
+  * Static array automatically aligned if the total byte size is a multiple of 16 and the matrix options require auto alignment
+  */
+template <typename T, int Size, int MatrixOptions,
+          bool Align = (MatrixOptions&Matrix_AutoAlign) && (((Size*sizeof(T))&0xf)==0)
+> struct ei_matrix_array
+{
+  EIGEN_ALIGN_128 T array[Size];
+
+  ei_matrix_array()
+  {
+    ei_assert((reinterpret_cast<size_t>(array) & 0xf) == 0
+              && "this assertion is explained here: http://eigen.tuxfamily.org/api/UnalignedArrayAssert.html  **** READ THIS WEB PAGE !!! ****");
+  }
+};
+
+template <typename T, int Size, int MatrixOptions> struct ei_matrix_array<T,Size,MatrixOptions,false>
+{
+  T array[Size];
+};
+
 /** \internal
   *
   * \class ei_matrix_storage
@@ -37,14 +58,14 @@
   *
   * \sa Matrix
   */
-template<typename T, int Size, int _Rows, int _Cols> class ei_matrix_storage;
+template<typename T, int Size, int _Rows, int _Cols, int _Options> class ei_matrix_storage;
 
 // purely fixed-size matrix
-template<typename T, int Size, int _Rows, int _Cols> class ei_matrix_storage
+template<typename T, int Size, int _Rows, int _Cols, int _Options> class ei_matrix_storage
 {
-    ei_aligned_array<T,Size,((Size*sizeof(T))%16)==0> m_data;
+    ei_matrix_array<T,Size,_Options> m_data;
   public:
-    inline ei_matrix_storage() {}
+    inline explicit ei_matrix_storage() {}
     inline ei_matrix_storage(int,int,int) {}
     inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); }
     inline static int rows(void) {return _Rows;}
@@ -55,12 +76,13 @@ template<typename T, int Size, int _Rows, int _Cols> class ei_matrix_storage
 };
 
 // dynamic-size matrix with fixed-size storage
-template<typename T, int Size> class ei_matrix_storage<T, Size, Dynamic, Dynamic>
+template<typename T, int Size, int _Options> class ei_matrix_storage<T, Size, Dynamic, Dynamic, _Options>
 {
-    ei_aligned_array<T,Size,((Size*sizeof(T))%16)==0> m_data;
+    ei_matrix_array<T,Size,_Options> m_data;
     int m_rows;
     int m_cols;
   public:
+    inline explicit ei_matrix_storage() : m_rows(0), m_cols(0) {}
     inline ei_matrix_storage(int, int rows, int cols) : m_rows(rows), m_cols(cols) {}
     inline ~ei_matrix_storage() {}
     inline void swap(ei_matrix_storage& other)
@@ -77,11 +99,12 @@ template<typename T, int Size> class ei_matrix_storage<T, Size, Dynamic, Dynamic
 };
 
 // dynamic-size matrix with fixed-size storage and fixed width
-template<typename T, int Size, int _Cols> class ei_matrix_storage<T, Size, Dynamic, _Cols>
+template<typename T, int Size, int _Cols, int _Options> class ei_matrix_storage<T, Size, Dynamic, _Cols, _Options>
 {
-    ei_aligned_array<T,Size,((Size*sizeof(T))%16)==0> m_data;
+    ei_matrix_array<T,Size,_Options> m_data;
     int m_rows;
   public:
+    inline explicit ei_matrix_storage() : m_rows(0) {}
     inline ei_matrix_storage(int, int rows, int) : m_rows(rows) {}
     inline ~ei_matrix_storage() {}
     inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
@@ -96,17 +119,18 @@ template<typename T, int Size, int _Cols> class ei_matrix_storage<T, Size, Dynam
 };
 
 // dynamic-size matrix with fixed-size storage and fixed height
-template<typename T, int Size, int _Rows> class ei_matrix_storage<T, Size, _Rows, Dynamic>
+template<typename T, int Size, int _Rows, int _Options> class ei_matrix_storage<T, Size, _Rows, Dynamic, _Options>
 {
-    ei_aligned_array<T,Size,((Size*sizeof(T))%16)==0> m_data;
+    ei_matrix_array<T,Size,_Options> m_data;
     int m_cols;
   public:
+    inline explicit ei_matrix_storage() : m_cols(0) {}
     inline ei_matrix_storage(int, int, int cols) : m_cols(cols) {}
     inline ~ei_matrix_storage() {}
     inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
     inline int rows(void) const {return _Rows;}
     inline int cols(void) const {return m_cols;}
-    inline void resize(int size, int, int cols)
+    inline void resize(int, int, int cols)
     {
       m_cols = cols;
     }
@@ -115,15 +139,16 @@ template<typename T, int Size, int _Rows> class ei_matrix_storage<T, Size, _Rows
 };
 
 // purely dynamic matrix.
-template<typename T> class ei_matrix_storage<T, Dynamic, Dynamic, Dynamic>
+template<typename T, int _Options> class ei_matrix_storage<T, Dynamic, Dynamic, Dynamic, _Options>
 {
     T *m_data;
     int m_rows;
     int m_cols;
   public:
+    inline explicit ei_matrix_storage() : m_data(0), m_rows(0), m_cols(0) {}
     inline ei_matrix_storage(int size, int rows, int cols)
       : m_data(ei_aligned_malloc<T>(size)), m_rows(rows), m_cols(cols) {}
-    inline ~ei_matrix_storage() { ei_aligned_free(m_data); }
+    inline ~ei_matrix_storage() { ei_aligned_free(m_data, m_rows*m_cols); }
     inline void swap(ei_matrix_storage& other)
     { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
     inline int rows(void) const {return m_rows;}
@@ -132,7 +157,7 @@ template<typename T> class ei_matrix_storage<T, Dynamic, Dynamic, Dynamic>
     {
       if(size != m_rows*m_cols)
       {
-        ei_aligned_free(m_data);
+        ei_aligned_free(m_data, m_rows*m_cols);
         m_data = ei_aligned_malloc<T>(size);
       }
       m_rows = rows;
@@ -143,13 +168,14 @@ template<typename T> class ei_matrix_storage<T, Dynamic, Dynamic, Dynamic>
 };
 
 // matrix with dynamic width and fixed height (so that matrix has dynamic size).
-template<typename T, int _Rows> class ei_matrix_storage<T, Dynamic, _Rows, Dynamic>
+template<typename T, int _Rows, int _Options> class ei_matrix_storage<T, Dynamic, _Rows, Dynamic, _Options>
 {
     T *m_data;
     int m_cols;
   public:
+    inline explicit ei_matrix_storage() : m_data(0), m_cols(0) {}
     inline ei_matrix_storage(int size, int, int cols) : m_data(ei_aligned_malloc<T>(size)), m_cols(cols) {}
-    inline ~ei_matrix_storage() { ei_aligned_free(m_data); }
+    inline ~ei_matrix_storage() { ei_aligned_free(m_data, _Rows*m_cols); }
     inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
     inline static int rows(void) {return _Rows;}
     inline int cols(void) const {return m_cols;}
@@ -157,7 +183,7 @@ template<typename T, int _Rows> class ei_matrix_storage<T, Dynamic, _Rows, Dynam
     {
       if(size != _Rows*m_cols)
       {
-        ei_aligned_free(m_data);
+        ei_aligned_free(m_data, _Rows*m_cols);
         m_data = ei_aligned_malloc<T>(size);
       }
       m_cols = cols;
@@ -167,13 +193,14 @@ template<typename T, int _Rows> class ei_matrix_storage<T, Dynamic, _Rows, Dynam
 };
 
 // matrix with dynamic height and fixed width (so that matrix has dynamic size).
-template<typename T, int _Cols> class ei_matrix_storage<T, Dynamic, Dynamic, _Cols>
+template<typename T, int _Cols, int _Options> class ei_matrix_storage<T, Dynamic, Dynamic, _Cols, _Options>
 {
     T *m_data;
     int m_rows;
   public:
+    inline explicit ei_matrix_storage() : m_data(0), m_rows(0) {}
     inline ei_matrix_storage(int size, int rows, int) : m_data(ei_aligned_malloc<T>(size)), m_rows(rows) {}
-    inline ~ei_matrix_storage() { ei_aligned_free(m_data); }
+    inline ~ei_matrix_storage() { ei_aligned_free(m_data, _Cols*m_rows); }
     inline void swap(ei_matrix_storage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
     inline int rows(void) const {return m_rows;}
     inline static int cols(void) {return _Cols;}
@@ -181,7 +208,7 @@ template<typename T, int _Cols> class ei_matrix_storage<T, Dynamic, Dynamic, _Co
     {
       if(size != m_rows*_Cols)
       {
-        ei_aligned_free(m_data);
+        ei_aligned_free(m_data, _Cols*m_rows);
         m_data = ei_aligned_malloc<T>(size);
       }
       m_rows = rows;

Eigen/src/Core/Minor.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/NestByValue.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/NumTraits.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/Part.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -31,8 +31,8 @@
   * \brief Expression of a triangular matrix extracted from a given matrix
   *
   * \param MatrixType the type of the object in which we are taking the triangular part
-  * \param Mode the kind of triangular matrix expression to construct. Can be Upper, StrictlyUpper,
-  *             UnitUpper, Lower, StrictlyLower, UnitLower. This is in fact a bit field; it must have either
+  * \param Mode the kind of triangular matrix expression to construct. Can be UpperTriangular, StrictlyUpperTriangular,
+  *             UnitUpperTriangular, LowerTriangular, StrictlyLowerTriangular, UnitLowerTriangular. This is in fact a bit field; it must have either
   *             UpperTriangularBit or LowerTriangularBit, and additionnaly it may have either ZeroDiagBit or
   *             UnitDiagBit.
   *
@@ -88,8 +88,10 @@ template<typename MatrixType, unsigned int Mode> class Part
 
     inline Scalar coeff(int row, int col) const
     {
+      // SelfAdjointBit doesn't play any role here: just because a matrix is selfadjoint doesn't say anything about
+      // each individual coefficient, except for the not-very-useful-here fact that diagonal coefficients are real.
       if( ((Flags & LowerTriangularBit) && (col>row)) || ((Flags & UpperTriangularBit) && (row>col)) )
-        return (Flags & SelfAdjointBit) ? ei_conj(m_matrix.coeff(col, row)) : (Scalar)0;
+        return (Scalar)0;
       if(Flags & UnitDiagBit)
         return col==row ? (Scalar)1 : m_matrix.coeff(row, col);
       else if(Flags & ZeroDiagBit)
@@ -100,12 +102,12 @@ template<typename MatrixType, unsigned int Mode> class Part
 
     inline Scalar& coeffRef(int row, int col)
     {
-      EIGEN_STATIC_ASSERT(!(Flags & UnitDiagBit), writting_to_triangular_part_with_unit_diag_is_not_supported);
-      EIGEN_STATIC_ASSERT(!(Flags & SelfAdjointBit), default_writting_to_selfadjoint_not_supported);
-      ei_assert(   (Mode==Upper && col>=row)
-                || (Mode==Lower && col<=row)
-                || (Mode==StrictlyUpper && col>row)
-                || (Mode==StrictlyLower && col<row));
+      EIGEN_STATIC_ASSERT(!(Flags & UnitDiagBit), WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED)
+      EIGEN_STATIC_ASSERT(!(Flags & SelfAdjointBit), COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED)
+      ei_assert(   (Mode==UpperTriangular && col>=row)
+                || (Mode==LowerTriangular && col<=row)
+                || (Mode==StrictlyUpperTriangular && col>row)
+                || (Mode==StrictlyLowerTriangular && col<row));
       return m_matrix.const_cast_derived().coeffRef(row, col);
     }
 
@@ -119,6 +121,12 @@ template<typename MatrixType, unsigned int Mode> class Part
     const Block<Part, RowsAtCompileTime, 1> col(int i) { return Base::col(i); }
     const Block<Part, RowsAtCompileTime, 1> col(int i) const { return Base::col(i); }
 
+    template<typename OtherDerived/*, int OtherMode*/>
+    void swap(const MatrixBase<OtherDerived>& other)
+    {
+      Part<SwapWrapper<MatrixType>,Mode>(SwapWrapper<MatrixType>(const_cast<MatrixType&>(m_matrix))).lazyAssign(other.derived());
+    }
+
   protected:
 
     const typename MatrixType::Nested m_matrix;
@@ -126,8 +134,8 @@ template<typename MatrixType, unsigned int Mode> class Part
 
 /** \returns an expression of a triangular matrix extracted from the current matrix
   *
-  * The parameter \a Mode can have the following values: \c Upper, \c StrictlyUpper, \c UnitUpper,
-  * \c Lower, \c StrictlyLower, \c UnitLower.
+  * The parameter \a Mode can have the following values: \c UpperTriangular, \c StrictlyUpperTriangular, \c UnitUpperTriangular,
+  * \c LowerTriangular, \c StrictlyLowerTriangular, \c UnitLowerTriangular.
   *
   * \addexample PartExample \label How to extract a triangular part of an arbitrary matrix
   *
@@ -149,7 +157,7 @@ inline Part<MatrixType, Mode>& Part<MatrixType, Mode>::operator=(const Other& ot
 {
   if(Other::Flags & EvalBeforeAssigningBit)
   {
-    typename ei_eval<Other>::type other_evaluated(other.rows(), other.cols());
+    typename MatrixBase<Other>::PlainMatrixType other_evaluated(other.rows(), other.cols());
     other_evaluated.template part<Mode>().lazyAssign(other);
     lazyAssign(other_evaluated);
   }
@@ -179,12 +187,12 @@ struct ei_part_assignment_impl
     }
     else
     {
-      ei_assert(Mode == Upper || Mode == Lower || Mode == StrictlyUpper || Mode == StrictlyLower);
-      if((Mode == Upper && row <= col)
-      || (Mode == Lower && row >= col)
-      || (Mode == StrictlyUpper && row < col)
-      || (Mode == StrictlyLower && row > col))
-        dst.coeffRef(row, col) = src.coeff(row, col);
+      ei_assert(Mode == UpperTriangular || Mode == LowerTriangular || Mode == StrictlyUpperTriangular || Mode == StrictlyLowerTriangular);
+      if((Mode == UpperTriangular && row <= col)
+      || (Mode == LowerTriangular && row >= col)
+      || (Mode == StrictlyUpperTriangular && row < col)
+      || (Mode == StrictlyLowerTriangular && row > col))
+        dst.copyCoeff(row, col, src);
     }
   }
 };
@@ -195,7 +203,7 @@ struct ei_part_assignment_impl<Derived1, Derived2, Mode, 1>
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
     if(!(Mode & ZeroDiagBit))
-      dst.coeffRef(0, 0) = src.coeff(0, 0);
+      dst.copyCoeff(0, 0, src);
   }
 };
 
@@ -207,45 +215,45 @@ struct ei_part_assignment_impl<Derived1, Derived2, Mode, 0>
 };
 
 template<typename Derived1, typename Derived2>
-struct ei_part_assignment_impl<Derived1, Derived2, Upper, Dynamic>
+struct ei_part_assignment_impl<Derived1, Derived2, UpperTriangular, Dynamic>
 {
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
-    for(int j = 0; j < dst.cols(); j++)
-      for(int i = 0; i <= j; i++)
-        dst.coeffRef(i, j) = src.coeff(i, j);
+    for(int j = 0; j < dst.cols(); ++j)
+      for(int i = 0; i <= j; ++i)
+        dst.copyCoeff(i, j, src);
   }
 };
 
 template<typename Derived1, typename Derived2>
-struct ei_part_assignment_impl<Derived1, Derived2, Lower, Dynamic>
+struct ei_part_assignment_impl<Derived1, Derived2, LowerTriangular, Dynamic>
 {
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
-    for(int j = 0; j < dst.cols(); j++)
-      for(int i = j; i < dst.rows(); i++)
-        dst.coeffRef(i, j) = src.coeff(i, j);
+    for(int j = 0; j < dst.cols(); ++j)
+      for(int i = j; i < dst.rows(); ++i)
+        dst.copyCoeff(i, j, src);
   }
 };
 
 template<typename Derived1, typename Derived2>
-struct ei_part_assignment_impl<Derived1, Derived2, StrictlyUpper, Dynamic>
+struct ei_part_assignment_impl<Derived1, Derived2, StrictlyUpperTriangular, Dynamic>
 {
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
-    for(int j = 0; j < dst.cols(); j++)
-      for(int i = 0; i < j; i++)
-        dst.coeffRef(i, j) = src.coeff(i, j);
+    for(int j = 0; j < dst.cols(); ++j)
+      for(int i = 0; i < j; ++i)
+        dst.copyCoeff(i, j, src);
   }
 };
 template<typename Derived1, typename Derived2>
-struct ei_part_assignment_impl<Derived1, Derived2, StrictlyLower, Dynamic>
+struct ei_part_assignment_impl<Derived1, Derived2, StrictlyLowerTriangular, Dynamic>
 {
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
-    for(int j = 0; j < dst.cols(); j++)
-      for(int i = j+1; i < dst.rows(); i++)
-        dst.coeffRef(i, j) = src.coeff(i, j);
+    for(int j = 0; j < dst.cols(); ++j)
+      for(int i = j+1; i < dst.rows(); ++i)
+        dst.copyCoeff(i, j, src);
   }
 };
 template<typename Derived1, typename Derived2>
@@ -253,9 +261,9 @@ struct ei_part_assignment_impl<Derived1, Derived2, SelfAdjoint, Dynamic>
 {
   inline static void run(Derived1 &dst, const Derived2 &src)
   {
-    for(int j = 0; j < dst.cols(); j++)
+    for(int j = 0; j < dst.cols(); ++j)
     {
-      for(int i = 0; i < j; i++)
+      for(int i = 0; i < j; ++i)
         dst.coeffRef(j, i) = ei_conj(dst.coeffRef(i, j) = src.coeff(i, j));
       dst.coeffRef(j, j) = ei_real(src.coeff(j, j));
     }
@@ -277,8 +285,8 @@ void Part<MatrixType, Mode>::lazyAssign(const Other& other)
 
 /** \returns a lvalue pseudo-expression allowing to perform special operations on \c *this.
   *
-  * The \a Mode parameter can have the following values: \c Upper, \c StrictlyUpper, \c Lower,
-  * \c StrictlyLower, \c SelfAdjoint.
+  * The \a Mode parameter can have the following values: \c UpperTriangular, \c StrictlyUpperTriangular, \c LowerTriangular,
+  * \c StrictlyLowerTriangular, \c SelfAdjoint.
   *
   * \addexample PartExample \label How to write to a triangular part of a matrix
   *
@@ -297,44 +305,44 @@ inline Part<Derived, Mode> MatrixBase<Derived>::part()
 /** \returns true if *this is approximately equal to an upper triangular matrix,
   *          within the precision given by \a prec.
   *
-  * \sa isLower(), extract(), part(), marked()
+  * \sa isLowerTriangular(), extract(), part(), marked()
   */
 template<typename Derived>
-bool MatrixBase<Derived>::isUpper(RealScalar prec) const
+bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
 {
   if(cols() != rows()) return false;
-  RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
-  for(int j = 0; j < cols(); j++)
-    for(int i = 0; i <= j; i++)
+  RealScalar maxAbsOnUpperTriangularPart = static_cast<RealScalar>(-1);
+  for(int j = 0; j < cols(); ++j)
+    for(int i = 0; i <= j; ++i)
     {
       RealScalar absValue = ei_abs(coeff(i,j));
-      if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
+      if(absValue > maxAbsOnUpperTriangularPart) maxAbsOnUpperTriangularPart = absValue;
     }
-  for(int j = 0; j < cols()-1; j++)
-    for(int i = j+1; i < rows(); i++)
-      if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnUpperPart, prec)) return false;
+  for(int j = 0; j < cols()-1; ++j)
+    for(int i = j+1; i < rows(); ++i)
+      if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnUpperTriangularPart, prec)) return false;
   return true;
 }
 
 /** \returns true if *this is approximately equal to a lower triangular matrix,
   *          within the precision given by \a prec.
   *
-  * \sa isUpper(), extract(), part(), marked()
+  * \sa isUpperTriangular(), extract(), part(), marked()
   */
 template<typename Derived>
-bool MatrixBase<Derived>::isLower(RealScalar prec) const
+bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
 {
   if(cols() != rows()) return false;
-  RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
-  for(int j = 0; j < cols(); j++)
-    for(int i = j; i < rows(); i++)
+  RealScalar maxAbsOnLowerTriangularPart = static_cast<RealScalar>(-1);
+  for(int j = 0; j < cols(); ++j)
+    for(int i = j; i < rows(); ++i)
     {
       RealScalar absValue = ei_abs(coeff(i,j));
-      if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
+      if(absValue > maxAbsOnLowerTriangularPart) maxAbsOnLowerTriangularPart = absValue;
     }
-  for(int j = 1; j < cols(); j++)
-    for(int i = 0; i < j; i++)
-      if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnLowerPart, prec)) return false;
+  for(int j = 1; j < cols(); ++j)
+    for(int i = 0; i < j; ++i)
+      if(!ei_isMuchSmallerThan(coeff(i, j), maxAbsOnLowerTriangularPart, prec)) return false;
   return true;
 }
 

Eigen/src/Core/Product.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 //
 // Eigen is free software; you can redistribute it and/or
@@ -30,14 +30,12 @@
 *** Forward declarations ***
 ***************************/
 
-template<int VectorizationMode, int Index, typename Lhs, typename Rhs>
+template<int VectorizationMode, int Index, typename Lhs, typename Rhs, typename RetScalar>
 struct ei_product_coeff_impl;
 
 template<int StorageOrder, int Index, typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl;
 
-template<typename T> struct ei_product_eval_to_column_major;
-
 /** \class ProductReturnType
   *
   * \brief Helper class to get the correct and optimized returned type of operator*
@@ -64,13 +62,14 @@ struct ProductReturnType
 };
 
 // cache friendly specialization
+// note that there is a DiagonalProduct specialization in DiagonalProduct.h
 template<typename Lhs, typename Rhs>
 struct ProductReturnType<Lhs,Rhs,CacheFriendlyProduct>
 {
   typedef typename ei_nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
 
   typedef typename ei_nested<Rhs,Lhs::RowsAtCompileTime,
-                             typename ei_product_eval_to_column_major<Rhs>::type
+                             typename ei_plain_matrix_type_column_major<Rhs>::type
                    >::type RhsNested;
 
   typedef Product<LhsNested, RhsNested, CacheFriendlyProduct> Type;
@@ -79,7 +78,8 @@ struct ProductReturnType<Lhs,Rhs,CacheFriendlyProduct>
 /*  Helper class to determine the type of the product, can be either:
  *    - NormalProduct
  *    - CacheFriendlyProduct
- *    - NormalProduct
+ *    - DiagonalProduct
+ *    - SparseProduct
  */
 template<typename Lhs, typename Rhs> struct ei_product_mode
 {
@@ -89,11 +89,12 @@ template<typename Lhs, typename Rhs> struct ei_product_mode
           ? DiagonalProduct
           : (Rhs::Flags & Lhs::Flags & SparseBit)
           ? SparseProduct
-          : Lhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-            && ( Lhs::MaxRowsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-              || Rhs::MaxColsAtCompileTime >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD )
+          : Lhs::MaxColsAtCompileTime == Dynamic
+            && ( Lhs::MaxRowsAtCompileTime == Dynamic
+              || Rhs::MaxColsAtCompileTime == Dynamic )
             && (!(Rhs::IsVectorAtCompileTime && (Lhs::Flags&RowMajorBit)  && (!(Lhs::Flags&DirectAccessBit))))
             && (!(Lhs::IsVectorAtCompileTime && (!(Rhs::Flags&RowMajorBit)) && (!(Rhs::Flags&DirectAccessBit))))
+            && (ei_is_same_type<typename Lhs::Scalar, typename Rhs::Scalar>::ret)
           ? CacheFriendlyProduct
           : NormalProduct };
 };
@@ -118,9 +119,9 @@ template<typename LhsNested, typename RhsNested, int ProductMode>
 struct ei_traits<Product<LhsNested, RhsNested, ProductMode> >
 {
   // clean the nested types:
-  typedef typename ei_unconst<typename ei_unref<LhsNested>::type>::type _LhsNested;
-  typedef typename ei_unconst<typename ei_unref<RhsNested>::type>::type _RhsNested;
-  typedef typename _LhsNested::Scalar Scalar;
+  typedef typename ei_cleantype<LhsNested>::type _LhsNested;
+  typedef typename ei_cleantype<RhsNested>::type _RhsNested;
+  typedef typename ei_scalar_product_traits<typename _LhsNested::Scalar, typename _RhsNested::Scalar>::ReturnType Scalar;
 
   enum {
     LhsCoeffReadCost = _LhsNested::CoeffReadCost,
@@ -151,7 +152,8 @@ struct ei_traits<Product<LhsNested, RhsNested, ProductMode> >
     Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
           | EvalBeforeAssigningBit
           | EvalBeforeNestingBit
-          | (CanVectorizeLhs || CanVectorizeRhs ? PacketAccessBit : 0),
+          | (CanVectorizeLhs || CanVectorizeRhs ? PacketAccessBit : 0)
+          | (LhsFlags & RhsFlags & AlignedBit),
 
     CoeffReadCost = InnerSize == Dynamic ? Dynamic
                   : InnerSize * (NumTraits<Scalar>::MulCost + LhsCoeffReadCost + RhsCoeffReadCost)
@@ -188,7 +190,7 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
 
     typedef ei_product_coeff_impl<CanVectorizeInner ? InnerVectorization : NoVectorization,
                                   Unroll ? InnerSize-1 : Dynamic,
-                                  _LhsNested, _RhsNested> ScalarCoeffImpl;
+                                  _LhsNested, _RhsNested, Scalar> ScalarCoeffImpl;
 
   public:
 
@@ -196,7 +198,13 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
     inline Product(const Lhs& lhs, const Rhs& rhs)
       : m_lhs(lhs), m_rhs(rhs)
     {
-      ei_assert(lhs.cols() == rhs.rows());
+      // we don't allow taking products of matrices of different real types, as that wouldn't be vectorizable.
+      // We still allow to mix T and complex<T>.
+      EIGEN_STATIC_ASSERT((ei_is_same_type<typename Lhs::RealScalar, typename Rhs::RealScalar>::ret),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+      ei_assert(lhs.cols() == rhs.rows()
+        && "invalid matrix product"
+        && "if you wanted a coeff-wise or a dot product use the respective explicit functions");
     }
 
     /** \internal
@@ -208,17 +216,17 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
     /** \internal
       * \returns whether it is worth it to use the cache friendly product.
       */
-    inline bool _useCacheFriendlyProduct() const
+    EIGEN_STRONG_INLINE bool _useCacheFriendlyProduct() const
     {
       return  m_lhs.cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
               && (  rows()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
                  || cols()>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD);
     }
 
-    inline int rows() const { return m_lhs.rows(); }
-    inline int cols() const { return m_rhs.cols(); }
+    EIGEN_STRONG_INLINE int rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE int cols() const { return m_rhs.cols(); }
 
-    const Scalar coeff(int row, int col) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int row, int col) const
     {
       Scalar res;
       ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res);
@@ -228,7 +236,7 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
     /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
      * which is why we don't set the LinearAccessBit.
      */
-    const Scalar coeff(int index) const
+    EIGEN_STRONG_INLINE const Scalar coeff(int index) const
     {
       Scalar res;
       const int row = RowsAtCompileTime == 1 ? 0 : index;
@@ -238,7 +246,7 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
     }
 
     template<int LoadMode>
-    const PacketScalar packet(int row, int col) const
+    EIGEN_STRONG_INLINE const PacketScalar packet(int row, int col) const
     {
       PacketScalar res;
       ei_product_packet_impl<Flags&RowMajorBit ? RowMajor : ColMajor,
@@ -248,8 +256,8 @@ template<typename LhsNested, typename RhsNested, int ProductMode> class Product
       return res;
     }
 
-    inline const _LhsNested& lhs() const { return m_lhs; }
-    inline const _RhsNested& rhs() const { return m_rhs; }
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
 
   protected:
     const LhsNested m_lhs;
@@ -267,6 +275,21 @@ template<typename OtherDerived>
 inline const typename ProductReturnType<Derived,OtherDerived>::Type
 MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
 {
+  enum {
+    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
+                   || OtherDerived::RowsAtCompileTime==Dynamic
+                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwise()*v2
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
   return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
 }
 
@@ -290,41 +313,42 @@ MatrixBase<Derived>::operator*=(const MatrixBase<OtherDerived> &other)
 *** Scalar path  - no vectorization ***
 **************************************/
 
-template<int Index, typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<NoVectorization, Index, Lhs, Rhs>
+template<int Index, typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<NoVectorization, Index, Lhs, Rhs, RetScalar>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
   {
-    ei_product_coeff_impl<NoVectorization, Index-1, Lhs, Rhs>::run(row, col, lhs, rhs, res);
+    ei_product_coeff_impl<NoVectorization, Index-1, Lhs, Rhs, RetScalar>::run(row, col, lhs, rhs, res);
     res += lhs.coeff(row, Index) * rhs.coeff(Index, col);
   }
 };
 
-template<typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<NoVectorization, 0, Lhs, Rhs>
+template<typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<NoVectorization, 0, Lhs, Rhs, RetScalar>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
   {
     res = lhs.coeff(row, 0) * rhs.coeff(0, col);
   }
 };
 
-template<typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<NoVectorization, Dynamic, Lhs, Rhs>
+template<typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<NoVectorization, Dynamic, Lhs, Rhs, RetScalar>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar& res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, RetScalar& res)
   {
+    ei_assert(lhs.cols()>0 && "you are using a non initialized matrix");
     res = lhs.coeff(row, 0) * rhs.coeff(0, col);
-      for(int i = 1; i < lhs.cols(); i++)
+      for(int i = 1; i < lhs.cols(); ++i)
         res += lhs.coeff(row, i) * rhs.coeff(i, col);
   }
 };
 
 // prevent buggy user code from causing an infinite recursion
-template<typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<NoVectorization, -1, Lhs, Rhs>
+template<typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<NoVectorization, -1, Lhs, Rhs, RetScalar>
 {
-  inline static void run(int, int, const Lhs&, const Rhs&, typename Lhs::Scalar&) {}
+  EIGEN_STRONG_INLINE static void run(int, int, const Lhs&, const Rhs&, RetScalar&) {}
 };
 
 /*******************************************
@@ -335,7 +359,7 @@ template<int Index, typename Lhs, typename Rhs, typename PacketScalar>
 struct ei_product_coeff_vectorized_unroller
 {
   enum { PacketSize = ei_packet_traits<typename Lhs::Scalar>::size };
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
   {
     ei_product_coeff_vectorized_unroller<Index-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
     pres = ei_padd(pres, ei_pmul( lhs.template packet<Aligned>(row, Index) , rhs.template packet<Aligned>(Index, col) ));
@@ -345,22 +369,22 @@ struct ei_product_coeff_vectorized_unroller
 template<typename Lhs, typename Rhs, typename PacketScalar>
 struct ei_product_coeff_vectorized_unroller<0, Lhs, Rhs, PacketScalar>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
   {
     pres = ei_pmul(lhs.template packet<Aligned>(row, 0) , rhs.template packet<Aligned>(0, col));
   }
 };
 
-template<int Index, typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<InnerVectorization, Index, Lhs, Rhs>
+template<int Index, typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<InnerVectorization, Index, Lhs, Rhs, RetScalar>
 {
   typedef typename Lhs::PacketScalar PacketScalar;
   enum { PacketSize = ei_packet_traits<typename Lhs::Scalar>::size };
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
   {
     PacketScalar pres;
     ei_product_coeff_vectorized_unroller<Index+1-PacketSize, Lhs, Rhs, PacketScalar>::run(row, col, lhs, rhs, pres);
-    ei_product_coeff_impl<NoVectorization,Index,Lhs,Rhs>::run(row, col, lhs, rhs, res);
+    ei_product_coeff_impl<NoVectorization,Index,Lhs,Rhs,RetScalar>::run(row, col, lhs, rhs, res);
     res = ei_predux(pres);
   }
 };
@@ -368,7 +392,7 @@ struct ei_product_coeff_impl<InnerVectorization, Index, Lhs, Rhs>
 template<typename Lhs, typename Rhs, int LhsRows = Lhs::RowsAtCompileTime, int RhsCols = Rhs::ColsAtCompileTime>
 struct ei_product_coeff_vectorized_dyn_selector
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
   {
     res = ei_dot_impl<
       Block<Lhs, 1, ei_traits<Lhs>::ColsAtCompileTime>,
@@ -382,7 +406,7 @@ struct ei_product_coeff_vectorized_dyn_selector
 template<typename Lhs, typename Rhs, int RhsCols>
 struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols>
 {
-  inline static void run(int /*row*/, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int /*row*/, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
   {
     res = ei_dot_impl<
       Lhs,
@@ -394,7 +418,7 @@ struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols>
 template<typename Lhs, typename Rhs, int LhsRows>
 struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1>
 {
-  inline static void run(int row, int /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
   {
     res = ei_dot_impl<
       Block<Lhs, 1, ei_traits<Lhs>::ColsAtCompileTime>,
@@ -406,7 +430,7 @@ struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1>
 template<typename Lhs, typename Rhs>
 struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,1>
 {
-  inline static void run(int /*row*/, int /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int /*row*/, int /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
   {
     res = ei_dot_impl<
       Lhs,
@@ -415,10 +439,10 @@ struct ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,1>
   }
 };
 
-template<typename Lhs, typename Rhs>
-struct ei_product_coeff_impl<InnerVectorization, Dynamic, Lhs, Rhs>
+template<typename Lhs, typename Rhs, typename RetScalar>
+struct ei_product_coeff_impl<InnerVectorization, Dynamic, Lhs, Rhs, RetScalar>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
   {
     ei_product_coeff_vectorized_dyn_selector<Lhs,Rhs>::run(row, col, lhs, rhs, res);
   }
@@ -431,7 +455,7 @@ struct ei_product_coeff_impl<InnerVectorization, Dynamic, Lhs, Rhs>
 template<int Index, typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<RowMajor, Index, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
   {
     ei_product_packet_impl<RowMajor, Index-1, Lhs, Rhs, PacketScalar, LoadMode>::run(row, col, lhs, rhs, res);
     res =  ei_pmadd(ei_pset1(lhs.coeff(row, Index)), rhs.template packet<LoadMode>(Index, col), res);
@@ -441,7 +465,7 @@ struct ei_product_packet_impl<RowMajor, Index, Lhs, Rhs, PacketScalar, LoadMode>
 template<int Index, typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<ColMajor, Index, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
   {
     ei_product_packet_impl<ColMajor, Index-1, Lhs, Rhs, PacketScalar, LoadMode>::run(row, col, lhs, rhs, res);
     res =  ei_pmadd(lhs.template packet<LoadMode>(row, Index), ei_pset1(rhs.coeff(Index, col)), res);
@@ -451,7 +475,7 @@ struct ei_product_packet_impl<ColMajor, Index, Lhs, Rhs, PacketScalar, LoadMode>
 template<typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<RowMajor, 0, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
   {
     res = ei_pmul(ei_pset1(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col));
   }
@@ -460,7 +484,7 @@ struct ei_product_packet_impl<RowMajor, 0, Lhs, Rhs, PacketScalar, LoadMode>
 template<typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<ColMajor, 0, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar &res)
   {
     res = ei_pmul(lhs.template packet<LoadMode>(row, 0), ei_pset1(rhs.coeff(0, col)));
   }
@@ -469,10 +493,11 @@ struct ei_product_packet_impl<ColMajor, 0, Lhs, Rhs, PacketScalar, LoadMode>
 template<typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar& res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar& res)
   {
+    ei_assert(lhs.cols()>0 && "you are using a non initialized matrix");
     res = ei_pmul(ei_pset1(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col));
-      for(int i = 1; i < lhs.cols(); i++)
+      for(int i = 1; i < lhs.cols(); ++i)
         res =  ei_pmadd(ei_pset1(lhs.coeff(row, i)), rhs.template packet<LoadMode>(i, col), res);
   }
 };
@@ -480,10 +505,11 @@ struct ei_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, PacketScalar, LoadMod
 template<typename Lhs, typename Rhs, typename PacketScalar, int LoadMode>
 struct ei_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, PacketScalar, LoadMode>
 {
-  inline static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar& res)
+  EIGEN_STRONG_INLINE static void run(int row, int col, const Lhs& lhs, const Rhs& rhs, PacketScalar& res)
   {
+    ei_assert(lhs.cols()>0 && "you are using a non initialized matrix");
     res = ei_pmul(lhs.template packet<LoadMode>(row, 0), ei_pset1(rhs.coeff(0, col)));
-      for(int i = 1; i < lhs.cols(); i++)
+      for(int i = 1; i < lhs.cols(); ++i)
         res =  ei_pmadd(lhs.template packet<LoadMode>(row, i), ei_pset1(rhs.coeff(i, col)), res);
   }
 };
@@ -547,7 +573,7 @@ struct ei_cache_friendly_product_selector<ProductType,LhsRows,ColMajor,HasDirect
        _res = &res.coeffRef(0);
     else
     {
-      _res = ei_alloc_stack(Scalar,res.size());
+      _res = ei_aligned_stack_alloc(Scalar,res.size());
       Map<Matrix<Scalar,DestDerived::RowsAtCompileTime,1> >(_res, res.size()) = res;
     }
     ei_cache_friendly_product_colmajor_times_vector(res.size(),
@@ -557,7 +583,7 @@ struct ei_cache_friendly_product_selector<ProductType,LhsRows,ColMajor,HasDirect
     if (!EvalToRes)
     {
       res = Map<Matrix<Scalar,DestDerived::SizeAtCompileTime,1> >(_res, res.size());
-      ei_free_stack(_res, Scalar, res.size());
+      ei_aligned_stack_free(_res, Scalar, res.size());
     }
   }
 };
@@ -593,7 +619,7 @@ struct ei_cache_friendly_product_selector<ProductType,1,LhsOrder,LhsAccess,RhsCo
        _res = &res.coeffRef(0);
     else
     {
-      _res = ei_alloc_stack(Scalar, res.size());
+      _res = ei_aligned_stack_alloc(Scalar, res.size());
       Map<Matrix<Scalar,DestDerived::SizeAtCompileTime,1> >(_res, res.size()) = res;
     }
     ei_cache_friendly_product_colmajor_times_vector(res.size(),
@@ -603,7 +629,7 @@ struct ei_cache_friendly_product_selector<ProductType,1,LhsOrder,LhsAccess,RhsCo
     if (!EvalToRes)
     {
       res = Map<Matrix<Scalar,DestDerived::SizeAtCompileTime,1> >(_res, res.size());
-      ei_free_stack(_res, Scalar, res.size());
+      ei_aligned_stack_free(_res, Scalar, res.size());
     }
   }
 };
@@ -626,13 +652,13 @@ struct ei_cache_friendly_product_selector<ProductType,LhsRows,RowMajor,HasDirect
        _rhs = &product.rhs().const_cast_derived().coeffRef(0);
     else
     {
-      _rhs = ei_alloc_stack(Scalar, product.rhs().size());
+      _rhs = ei_aligned_stack_alloc(Scalar, product.rhs().size());
       Map<Matrix<Scalar,Rhs::SizeAtCompileTime,1> >(_rhs, product.rhs().size()) = product.rhs();
     }
     ei_cache_friendly_product_rowmajor_times_vector(&product.lhs().const_cast_derived().coeffRef(0,0), product.lhs().stride(),
                                                     _rhs, product.rhs().size(), res);
 
-    if (!UseRhsDirectly) ei_free_stack(_rhs, Scalar, product.rhs().size());
+    if (!UseRhsDirectly) ei_aligned_stack_free(_rhs, Scalar, product.rhs().size());
   }
 };
 
@@ -654,13 +680,13 @@ struct ei_cache_friendly_product_selector<ProductType,1,LhsOrder,LhsAccess,RhsCo
        _lhs = &product.lhs().const_cast_derived().coeffRef(0);
     else
     {
-      _lhs = ei_alloc_stack(Scalar, product.lhs().size());
+      _lhs = ei_aligned_stack_alloc(Scalar, product.lhs().size());
       Map<Matrix<Scalar,Lhs::SizeAtCompileTime,1> >(_lhs, product.lhs().size()) = product.lhs();
     }
     ei_cache_friendly_product_rowmajor_times_vector(&product.rhs().const_cast_derived().coeffRef(0,0), product.rhs().stride(),
                                                     _lhs, product.lhs().size(), res);
 
-    if(!UseLhsDirectly) ei_free_stack(_lhs, Scalar, product.lhs().size());
+    if(!UseLhsDirectly) ei_aligned_stack_free(_lhs, Scalar, product.lhs().size());
   }
 };
 
@@ -706,23 +732,12 @@ inline Derived& MatrixBase<Derived>::lazyAssign(const Product<Lhs,Rhs,CacheFrien
   return derived();
 }
 
-template<typename T> struct ei_product_eval_to_column_major
-{
-  typedef Matrix<typename ei_traits<T>::Scalar,
-                ei_traits<T>::RowsAtCompileTime,
-                ei_traits<T>::ColsAtCompileTime,
-                ColMajor,
-                ei_traits<T>::MaxRowsAtCompileTime,
-                ei_traits<T>::MaxColsAtCompileTime
-          > type;
-};
-
 template<typename T> struct ei_product_copy_rhs
 {
   typedef typename ei_meta_if<
          (ei_traits<T>::Flags & RowMajorBit)
       || (!(ei_traits<T>::Flags & DirectAccessBit)),
-      typename ei_product_eval_to_column_major<T>::type,
+      typename ei_plain_matrix_type_column_major<T>::type,
       const T&
     >::ret type;
 };
@@ -731,7 +746,7 @@ template<typename T> struct ei_product_copy_lhs
 {
   typedef typename ei_meta_if<
       (!(int(ei_traits<T>::Flags) & DirectAccessBit)),
-      typename ei_eval<T>::type,
+      typename ei_plain_matrix_type<T>::type,
       const T&
     >::ret type;
 };

Eigen/src/Core/Redux.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -65,12 +65,13 @@ struct ei_redux_impl<BinaryOp, Derived, Start, Dynamic>
   typedef typename ei_result_of<BinaryOp(typename Derived::Scalar)>::type Scalar;
   static Scalar run(const Derived& mat, const BinaryOp& func)
   {
+    ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
     Scalar res;
     res = mat.coeff(0,0);
-    for(int i = 1; i < mat.rows(); i++)
+    for(int i = 1; i < mat.rows(); ++i)
       res = func(res, mat.coeff(i, 0));
-    for(int j = 1; j < mat.cols(); j++)
-      for(int i = 0; i < mat.rows(); i++)
+    for(int j = 1; j < mat.cols(); ++j)
+      for(int i = 0; i < mat.rows(); ++i)
         res = func(res, mat.coeff(i, j));
     return res;
   }

Eigen/src/Core/SolveTriangular.h

@@ -30,9 +30,9 @@ template<typename XprType, unsigned int Mode> struct ei_is_part<Part<XprType,Mod
 
 template<typename Lhs, typename Rhs,
   int TriangularPart = (int(Lhs::Flags) & LowerTriangularBit)
-                     ? Lower
+                     ? LowerTriangular
                      : (int(Lhs::Flags) & UpperTriangularBit)
-                     ? Upper
+                     ? UpperTriangular
                      : -1,
   int StorageOrder = ei_is_part<Lhs>::value ? -1  // this is to solve ambiguous specializations
                    : int(Lhs::Flags) & (RowMajorBit|SparseBit)
@@ -56,14 +56,14 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,RowMajor|IsDense>
   typedef typename Rhs::Scalar Scalar;
   static void run(const Lhs& lhs, Rhs& other)
   {
-    const bool IsLower = (UpLo==Lower);
+    const bool IsLowerTriangular = (UpLo==LowerTriangular);
     const int size = lhs.cols();
     /* We perform the inverse product per block of 4 rows such that we perfectly match
      * our optimized matrix * vector product. blockyStart represents the number of rows
      * we have process first using the non-block version.
      */
     int blockyStart = (std::max(size-5,0)/4)*4;
-    if (IsLower)
+    if (IsLowerTriangular)
       blockyStart = size - blockyStart;
     else
       blockyStart -= 1;
@@ -72,15 +72,15 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,RowMajor|IsDense>
       // process first rows using the non block version
       if(!(Lhs::Flags & UnitDiagBit))
       {
-        if (IsLower)
+        if (IsLowerTriangular)
           other.coeffRef(0,c) = other.coeff(0,c)/lhs.coeff(0, 0);
         else
           other.coeffRef(size-1,c) = other.coeff(size-1, c)/lhs.coeff(size-1, size-1);
       }
-      for(int i=(IsLower ? 1 : size-2); IsLower ? i<blockyStart : i>blockyStart; i += (IsLower ? 1 : -1) )
+      for(int i=(IsLowerTriangular ? 1 : size-2); IsLowerTriangular ? i<blockyStart : i>blockyStart; i += (IsLowerTriangular ? 1 : -1) )
       {
         Scalar tmp = other.coeff(i,c)
-          - (IsLower ? ((lhs.row(i).start(i)) * other.col(c).start(i)).coeff(0,0)
+          - (IsLowerTriangular ? ((lhs.row(i).start(i)) * other.col(c).start(i)).coeff(0,0)
                      : ((lhs.row(i).end(size-i-1)) * other.col(c).end(size-i-1)).coeff(0,0));
         if (Lhs::Flags & UnitDiagBit)
           other.coeffRef(i,c) = tmp;
@@ -88,39 +88,39 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,RowMajor|IsDense>
           other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
       }
 
-      // now let process the remaining rows 4 at once
-      for(int i=blockyStart; IsLower ? i<size : i>0; )
+      // now let's process the remaining rows 4 at once
+      for(int i=blockyStart; IsLowerTriangular ? i<size : i>0; )
       {
         int startBlock = i;
-        int endBlock = startBlock + (IsLower ? 4 : -4);
-        
+        int endBlock = startBlock + (IsLowerTriangular ? 4 : -4);
+
         /* Process the i cols times 4 rows block, and keep the result in a temporary vector */
         // FIXME use fixed size block but take care to small fixed size matrices...
         Matrix<Scalar,Dynamic,1> btmp(4);
-        if (IsLower)
+        if (IsLowerTriangular)
           btmp = lhs.block(startBlock,0,4,i) * other.col(c).start(i);
         else
           btmp = lhs.block(i-3,i+1,4,size-1-i) * other.col(c).end(size-1-i);
-        
+
         /* Let's process the 4x4 sub-matrix as usual.
          * btmp stores the diagonal coefficients used to update the remaining part of the result.
          */
         {
-          Scalar tmp = other.coeff(startBlock,c)-btmp.coeff(IsLower?0:3);
+          Scalar tmp = other.coeff(startBlock,c)-btmp.coeff(IsLowerTriangular?0:3);
           if (Lhs::Flags & UnitDiagBit)
             other.coeffRef(i,c) = tmp;
           else
             other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
         }
 
-        i += IsLower ? 1 : -1;
-        for (;IsLower ? i<endBlock : i>endBlock; i += IsLower ? 1 : -1)
+        i += IsLowerTriangular ? 1 : -1;
+        for (;IsLowerTriangular ? i<endBlock : i>endBlock; i += IsLowerTriangular ? 1 : -1)
         {
-          int remainingSize = IsLower ? i-startBlock : startBlock-i;
+          int remainingSize = IsLowerTriangular ? i-startBlock : startBlock-i;
           Scalar tmp = other.coeff(i,c)
-            - btmp.coeff(IsLower ? remainingSize : 3-remainingSize)
-            - (   lhs.row(i).block(IsLower ? startBlock : i+1, remainingSize)
-              * other.col(c).block(IsLower ? startBlock : i+1, remainingSize)).coeff(0,0);
+            - btmp.coeff(IsLowerTriangular ? remainingSize : 3-remainingSize)
+            - (   lhs.row(i).segment(IsLowerTriangular ? startBlock : i+1, remainingSize)
+              * other.col(c).segment(IsLowerTriangular ? startBlock : i+1, remainingSize)).coeff(0,0);
 
           if (Lhs::Flags & UnitDiagBit)
             other.coeffRef(i,c) = tmp;
@@ -133,10 +133,10 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,RowMajor|IsDense>
 };
 
 // Implements the following configurations:
-//  - inv(Lower,         ColMajor) * Column vector
-//  - inv(Lower,UnitDiag,ColMajor) * Column vector
-//  - inv(Upper,         ColMajor) * Column vector
-//  - inv(Upper,UnitDiag,ColMajor) * Column vector
+//  - inv(LowerTriangular,         ColMajor) * Column vector
+//  - inv(LowerTriangular,UnitDiag,ColMajor) * Column vector
+//  - inv(UpperTriangular,         ColMajor) * Column vector
+//  - inv(UpperTriangular,UnitDiag,ColMajor) * Column vector
 template<typename Lhs, typename Rhs, int UpLo>
 struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
 {
@@ -146,7 +146,7 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
 
   static void run(const Lhs& lhs, Rhs& other)
   {
-    static const bool IsLower = (UpLo==Lower);
+    static const bool IsLowerTriangular = (UpLo==LowerTriangular);
     const int size = lhs.cols();
     for(int c=0 ; c<other.cols() ; ++c)
     {
@@ -155,27 +155,27 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
        * we can process using the block version.
        */
       int blockyEnd = (std::max(size-5,0)/4)*4;
-      if (!IsLower)
+      if (!IsLowerTriangular)
         blockyEnd = size-1 - blockyEnd;
-      for(int i=IsLower ? 0 : size-1; IsLower ? i<blockyEnd : i>blockyEnd;)
+      for(int i=IsLowerTriangular ? 0 : size-1; IsLowerTriangular ? i<blockyEnd : i>blockyEnd;)
       {
         /* Let's process the 4x4 sub-matrix as usual.
          * btmp stores the diagonal coefficients used to update the remaining part of the result.
          */
         int startBlock = i;
-        int endBlock = startBlock + (IsLower ? 4 : -4);
+        int endBlock = startBlock + (IsLowerTriangular ? 4 : -4);
         Matrix<Scalar,4,1> btmp;
-        for (;IsLower ? i<endBlock : i>endBlock;
-             i += IsLower ? 1 : -1)
+        for (;IsLowerTriangular ? i<endBlock : i>endBlock;
+             i += IsLowerTriangular ? 1 : -1)
         {
           if(!(Lhs::Flags & UnitDiagBit))
             other.coeffRef(i,c) /= lhs.coeff(i,i);
-          int remainingSize = IsLower ? endBlock-i-1 : i-endBlock-1;
+          int remainingSize = IsLowerTriangular ? endBlock-i-1 : i-endBlock-1;
           if (remainingSize>0)
-            other.col(c).block((IsLower ? i : endBlock) + 1, remainingSize) -=
+            other.col(c).segment((IsLowerTriangular ? i : endBlock) + 1, remainingSize) -=
                 other.coeffRef(i,c)
-              * Block<Lhs,Dynamic,1>(lhs, (IsLower ? i : endBlock) + 1, i, remainingSize, 1);
-          btmp.coeffRef(IsLower ? i-startBlock : remainingSize) = -other.coeffRef(i,c);
+              * Block<Lhs,Dynamic,1>(lhs, (IsLowerTriangular ? i : endBlock) + 1, i, remainingSize, 1);
+          btmp.coeffRef(IsLowerTriangular ? i-startBlock : remainingSize) = -other.coeffRef(i,c);
         }
 
         /* Now we can efficiently update the remaining part of the result as a matrix * vector product.
@@ -187,15 +187,21 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
         // FIXME this is cool but what about conjugate/adjoint expressions ? do we want to evaluate them ?
         // this is a more general problem though.
         ei_cache_friendly_product_colmajor_times_vector(
-          IsLower ? size-endBlock : endBlock+1,
-          &(lhs.const_cast_derived().coeffRef(IsLower ? endBlock : 0, IsLower ? startBlock : endBlock+1)),
+          IsLowerTriangular ? size-endBlock : endBlock+1,
+          &(lhs.const_cast_derived().coeffRef(IsLowerTriangular ? endBlock : 0, IsLowerTriangular ? startBlock : endBlock+1)),
           lhs.stride(),
-          btmp, &(other.coeffRef(IsLower ? endBlock : 0, c)));
+          btmp, &(other.coeffRef(IsLowerTriangular ? endBlock : 0, c)));
+// 				if (IsLowerTriangular)
+//           other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
+//                                           * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
+// 				else
+//           other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
+//                                           * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
       }
 
       /* Now we have to process the remaining part as usual */
       int i;
-      for(i=blockyEnd; IsLower ? i<size-1 : i>0; i += (IsLower ? 1 : -1) )
+      for(i=blockyEnd; IsLowerTriangular ? i<size-1 : i>0; i += (IsLowerTriangular ? 1 : -1) )
       {
         if(!(Lhs::Flags & UnitDiagBit))
           other.coeffRef(i,c) /= lhs.coeff(i,i);
@@ -203,7 +209,7 @@ struct ei_solve_triangular_selector<Lhs,Rhs,UpLo,ColMajor|IsDense>
         /* NOTE we cannot use lhs.col(i).end(size-i-1) because Part::coeffRef gets called by .col() to
          * get the address of the start of the row
          */
-        if(IsLower)
+        if(IsLowerTriangular)
           other.col(c).end(size-i-1) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, i+1,i, size-i-1,1);
         else
           other.col(c).start(i) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, 0,i, i, 1);
@@ -227,7 +233,16 @@ void MatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other
   ei_assert(!(Flags & ZeroDiagBit));
   ei_assert(Flags & (UpperTriangularBit|LowerTriangularBit));
 
-  ei_solve_triangular_selector<Derived, OtherDerived>::run(derived(), other.derived());
+  enum { copy = ei_traits<OtherDerived>::Flags & RowMajorBit };
+
+  typedef typename ei_meta_if<copy,
+    typename ei_plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::ret OtherCopy;
+  OtherCopy otherCopy(other.derived());
+
+  ei_solve_triangular_selector<Derived, typename ei_unref<OtherCopy>::type>::run(derived(), otherCopy);
+
+  if (copy)
+    other = otherCopy;
 }
 
 /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
@@ -240,17 +255,17 @@ void MatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other
   * It is required that \c *this be marked as either an upper or a lower triangular matrix, which
   * can be done by marked(), and that is automatically the case with expressions such as those returned
   * by extract().
-  * 
+  *
   * \addexample SolveTriangular \label How to solve a triangular system (aka. how to multiply the inverse of a triangular matrix by another one)
-  * 
+  *
   * Example: \include MatrixBase_marked.cpp
   * Output: \verbinclude MatrixBase_marked.out
-  * 
+  *
   * This function is essentially a wrapper to the faster solveTriangularInPlace() function creating
   * a temporary copy of \a other, calling solveTriangularInPlace() on the copy and returning it.
   * Therefore, if \a other is not needed anymore, it is quite faster to call solveTriangularInPlace()
   * instead of solveTriangular().
-  * 
+  *
   * For users coming from BLAS, this function (and more specifically solveTriangularInPlace()) offer
   * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
   *
@@ -258,14 +273,15 @@ void MatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other
   * \code
   * M * T^1  <=>  T.transpose().solveTriangularInPlace(M.transpose());
   * \endcode
-  * 
+  *
   * \sa solveTriangularInPlace(), marked(), extract()
   */
 template<typename Derived>
 template<typename OtherDerived>
-typename OtherDerived::Eval MatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
+typename ei_plain_matrix_type_column_major<OtherDerived>::type
+MatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
 {
-  typename OtherDerived::Eval res(other);
+  typename ei_plain_matrix_type_column_major<OtherDerived>::type res(other);
   solveTriangularInPlace(res);
   return res;
 }

Eigen/src/Core/Sum.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -165,12 +165,13 @@ struct ei_sum_impl<Derived, NoVectorization, NoUnrolling>
   typedef typename Derived::Scalar Scalar;
   static Scalar run(const Derived& mat)
   {
+    ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
     Scalar res;
     res = mat.coeff(0, 0);
-    for(int i = 1; i < mat.rows(); i++)
+    for(int i = 1; i < mat.rows(); ++i)
       res += mat.coeff(i, 0);
-    for(int j = 1; j < mat.cols(); j++)
-      for(int i = 0; i < mat.rows(); i++)
+    for(int j = 1; j < mat.cols(); ++j)
+      for(int i = 0; i < mat.rows(); ++i)
         res += mat.coeff(i, j);
     return res;
   }
@@ -216,10 +217,10 @@ struct ei_sum_impl<Derived, LinearVectorization, NoUnrolling>
       res = Scalar(0);
     }
 
-    for(int index = 0; index < alignedStart; index++)
+    for(int index = 0; index < alignedStart; ++index)
       res += mat.coeff(index);
 
-    for(int index = alignedEnd; index < size; index++)
+    for(int index = alignedEnd; index < size; ++index)
       res += mat.coeff(index);
 
     return res;

Eigen/src/Core/Swap.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/Core/Transpose.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -156,4 +156,49 @@ MatrixBase<Derived>::adjoint() const
   return conjugate().nestByValue();
 }
 
+/***************************************************************************
+* "in place" transpose implementation
+***************************************************************************/
+
+template<typename MatrixType,
+  bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic>
+struct ei_inplace_transpose_selector;
+
+template<typename MatrixType>
+struct ei_inplace_transpose_selector<MatrixType,true> { // square matrix
+  static void run(MatrixType& m) {
+    m.template part<StrictlyUpperTriangular>().swap(m.transpose());
+  }
+};
+
+template<typename MatrixType>
+struct ei_inplace_transpose_selector<MatrixType,false> { // non square matrix
+  static void run(MatrixType& m) {
+    if (m.rows()==m.cols())
+      m.template part<StrictlyUpperTriangular>().swap(m.transpose());
+    else
+      m.set(m.transpose().eval());
+  }
+};
+
+/** This is the "in place" version of transpose: it transposes \c *this.
+  *
+  * In most cases it is probably better to simply use the transposed expression
+  * of a matrix. However, when transposing the matrix data itself is really needed,
+  * then this "in-place" version is probably the right choice because it provides 
+  * the following additional features:
+  *  - less error prone: doing the same operation with .transpose() requires special care:
+  *    \code m.set(m.transpose().eval()); \endcode
+  *  - no temporary object is created (currently only for squared matrices)
+  *  - it allows future optimizations (cache friendliness, etc.)
+  *
+  * \note if the matrix is not square, then \c *this must be a resizable matrix.
+  *
+  * \sa transpose(), adjoint() */
+template<typename Derived>
+inline void MatrixBase<Derived>::transposeInPlace()
+{
+  ei_inplace_transpose_selector<Derived>::run(derived());
+}
+
 #endif // EIGEN_TRANSPOSE_H

Eigen/src/Core/Visitor.h

@@ -55,10 +55,10 @@ struct ei_visitor_impl<Visitor, Derived, Dynamic>
   inline static void run(const Derived& mat, Visitor& visitor)
   {
     visitor.init(mat.coeff(0,0), 0, 0);
-    for(int i = 1; i < mat.rows(); i++)
+    for(int i = 1; i < mat.rows(); ++i)
       visitor(mat.coeff(i, 0), i, 0);
-    for(int j = 1; j < mat.cols(); j++)
-      for(int i = 0; i < mat.rows(); i++)
+    for(int j = 1; j < mat.cols(); ++j)
+      for(int i = 0; i < mat.rows(); ++i)
         visitor(mat.coeff(i, j), i, j);
   }
 };

Eigen/src/Core/arch/SSE/PacketMath.h

@@ -37,17 +37,17 @@ template<> struct ei_unpacket_traits<__m128>  { typedef float  type; enum {size=
 template<> struct ei_unpacket_traits<__m128d> { typedef double type; enum {size=2}; };
 template<> struct ei_unpacket_traits<__m128i> { typedef int    type; enum {size=4}; };
 
-template<> inline __m128  ei_padd(const __m128&  a, const __m128&  b) { return _mm_add_ps(a,b); }
-template<> inline __m128d ei_padd(const __m128d& a, const __m128d& b) { return _mm_add_pd(a,b); }
-template<> inline __m128i ei_padd(const __m128i& a, const __m128i& b) { return _mm_add_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE __m128  ei_padd<__m128>(const __m128&  a, const __m128&  b) { return _mm_add_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_padd<__m128d>(const __m128d& a, const __m128d& b) { return _mm_add_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128i ei_padd<__m128i>(const __m128i& a, const __m128i& b) { return _mm_add_epi32(a,b); }
 
-template<> inline __m128  ei_psub(const __m128&  a, const __m128&  b) { return _mm_sub_ps(a,b); }
-template<> inline __m128d ei_psub(const __m128d& a, const __m128d& b) { return _mm_sub_pd(a,b); }
-template<> inline __m128i ei_psub(const __m128i& a, const __m128i& b) { return _mm_sub_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE __m128  ei_psub<__m128>(const __m128&  a, const __m128&  b) { return _mm_sub_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_psub<__m128d>(const __m128d& a, const __m128d& b) { return _mm_sub_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128i ei_psub<__m128i>(const __m128i& a, const __m128i& b) { return _mm_sub_epi32(a,b); }
 
-template<> inline __m128  ei_pmul(const __m128&  a, const __m128&  b) { return _mm_mul_ps(a,b); }
-template<> inline __m128d ei_pmul(const __m128d& a, const __m128d& b) { return _mm_mul_pd(a,b); }
-template<> inline __m128i ei_pmul(const __m128i& a, const __m128i& b)
+template<> EIGEN_STRONG_INLINE __m128  ei_pmul<__m128>(const __m128&  a, const __m128&  b) { return _mm_mul_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pmul<__m128d>(const __m128d& a, const __m128d& b) { return _mm_mul_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128i ei_pmul<__m128i>(const __m128i& a, const __m128i& b)
 {
   return _mm_or_si128(
     _mm_and_si128(
@@ -59,108 +59,108 @@ template<> inline __m128i ei_pmul(const __m128i& a, const __m128i& b)
         _mm_setr_epi32(0xffffffff,0,0xffffffff,0)), 4));
 }
 
-template<> inline __m128  ei_pdiv(const __m128&  a, const __m128&  b) { return _mm_div_ps(a,b); }
-template<> inline __m128d ei_pdiv(const __m128d& a, const __m128d& b) { return _mm_div_pd(a,b); }
-template<> inline __m128i ei_pdiv(const __m128i& /*a*/, const __m128i& /*b*/)
+template<> EIGEN_STRONG_INLINE __m128  ei_pdiv<__m128>(const __m128&  a, const __m128&  b) { return _mm_div_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pdiv<__m128d>(const __m128d& a, const __m128d& b) { return _mm_div_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128i ei_pdiv<__m128i>(const __m128i& /*a*/, const __m128i& /*b*/)
 { ei_assert(false && "packet integer division are not supported by SSE");
   __m128i dummy;
   return dummy;
 }
 
 // for some weird raisons, it has to be overloaded for packet integer
-template<> inline __m128i ei_pmadd(const __m128i& a, const __m128i& b, const __m128i& c) { return ei_padd(ei_pmul(a,b), c); }
+template<> EIGEN_STRONG_INLINE __m128i ei_pmadd(const __m128i& a, const __m128i& b, const __m128i& c) { return ei_padd(ei_pmul(a,b), c); }
 
-template<> inline __m128  ei_pmin(const __m128&  a, const __m128&  b) { return _mm_min_ps(a,b); }
-template<> inline __m128d ei_pmin(const __m128d& a, const __m128d& b) { return _mm_min_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128  ei_pmin<__m128>(const __m128&  a, const __m128&  b) { return _mm_min_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pmin<__m128d>(const __m128d& a, const __m128d& b) { return _mm_min_pd(a,b); }
 // FIXME this vectorized min operator is likely to be slower than the standard one
-template<> inline __m128i ei_pmin(const __m128i& a, const __m128i& b)
+template<> EIGEN_STRONG_INLINE __m128i ei_pmin<__m128i>(const __m128i& a, const __m128i& b)
 {
   __m128i mask = _mm_cmplt_epi32(a,b);
   return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
 }
 
-template<> inline __m128  ei_pmax(const __m128&  a, const __m128&  b) { return _mm_max_ps(a,b); }
-template<> inline __m128d ei_pmax(const __m128d& a, const __m128d& b) { return _mm_max_pd(a,b); }
+template<> EIGEN_STRONG_INLINE __m128  ei_pmax<__m128>(const __m128&  a, const __m128&  b) { return _mm_max_ps(a,b); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pmax<__m128d>(const __m128d& a, const __m128d& b) { return _mm_max_pd(a,b); }
 // FIXME this vectorized max operator is likely to be slower than the standard one
-template<> inline __m128i ei_pmax(const __m128i& a, const __m128i& b)
+template<> EIGEN_STRONG_INLINE __m128i ei_pmax<__m128i>(const __m128i& a, const __m128i& b)
 {
   __m128i mask = _mm_cmpgt_epi32(a,b);
   return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
 }
 
-template<> inline __m128  ei_pload(const float*   from) { return _mm_load_ps(from); }
-template<> inline __m128d ei_pload(const double*  from) { return _mm_load_pd(from); }
-template<> inline __m128i ei_pload(const int* from) { return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
+template<> EIGEN_STRONG_INLINE __m128  ei_pload<float>(const float*   from) { return _mm_load_ps(from); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pload<double>(const double*  from) { return _mm_load_pd(from); }
+template<> EIGEN_STRONG_INLINE __m128i ei_pload<int>(const int* from) { return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
 
-template<> inline __m128  ei_ploadu(const float*   from) { return _mm_loadu_ps(from); }
-// template<> inline __m128  ei_ploadu(const float*   from) {
+template<> EIGEN_STRONG_INLINE __m128  ei_ploadu<float>(const float*   from) { return _mm_loadu_ps(from); }
+// template<> EIGEN_STRONG_INLINE __m128  ei_ploadu(const float*   from) {
 //   if (size_t(from)&0xF)
 //     return _mm_loadu_ps(from);
 //   else 
 //     return _mm_loadu_ps(from);
 // }
-template<> inline __m128d ei_ploadu(const double*  from) { return _mm_loadu_pd(from); }
-template<> inline __m128i ei_ploadu(const int* from) { return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from)); }
+template<> EIGEN_STRONG_INLINE __m128d ei_ploadu<double>(const double*  from) { return _mm_loadu_pd(from); }
+template<> EIGEN_STRONG_INLINE __m128i ei_ploadu<int>(const int* from) { return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from)); }
 
-template<> inline __m128  ei_pset1(const float&  from) { return _mm_set1_ps(from); }
-template<> inline __m128d ei_pset1(const double& from) { return _mm_set1_pd(from); }
-template<> inline __m128i ei_pset1(const int&    from) { return _mm_set1_epi32(from); }
+template<> EIGEN_STRONG_INLINE __m128  ei_pset1<float>(const float&  from) { return _mm_set1_ps(from); }
+template<> EIGEN_STRONG_INLINE __m128d ei_pset1<double>(const double& from) { return _mm_set1_pd(from); }
+template<> EIGEN_STRONG_INLINE __m128i ei_pset1<int>(const int&    from) { return _mm_set1_epi32(from); }
 
-template<> inline void ei_pstore(float*  to, const __m128&  from) { _mm_store_ps(to, from); }
-template<> inline void ei_pstore(double* to, const __m128d& from) { _mm_store_pd(to, from); }
-template<> inline void ei_pstore(int*    to, const __m128i& from) { _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void ei_pstore<float>(float*  to, const __m128&  from) { _mm_store_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void ei_pstore<double>(double* to, const __m128d& from) { _mm_store_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void ei_pstore<int>(int*    to, const __m128i& from) { _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
 
-template<> inline void ei_pstoreu(float*  to, const __m128&  from) { _mm_storeu_ps(to, from); }
-template<> inline void ei_pstoreu(double* to, const __m128d& from) { _mm_storeu_pd(to, from); }
-template<> inline void ei_pstoreu(int*    to, const __m128i& from) { _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void ei_pstoreu<float>(float*  to, const __m128&  from) { _mm_storeu_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void ei_pstoreu<double>(double* to, const __m128d& from) { _mm_storeu_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void ei_pstoreu<int>(int*    to, const __m128i& from) { _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
 
-template<> inline float  ei_pfirst(const __m128&  a) { return _mm_cvtss_f32(a); }
-template<> inline double ei_pfirst(const __m128d& a) { return _mm_cvtsd_f64(a); }
-template<> inline int    ei_pfirst(const __m128i& a) { return _mm_cvtsi128_si32(a); }
+template<> EIGEN_STRONG_INLINE float  ei_pfirst<__m128>(const __m128&  a) { return _mm_cvtss_f32(a); }
+template<> EIGEN_STRONG_INLINE double ei_pfirst<__m128d>(const __m128d& a) { return _mm_cvtsd_f64(a); }
+template<> EIGEN_STRONG_INLINE int    ei_pfirst<__m128i>(const __m128i& a) { return _mm_cvtsi128_si32(a); }
 
 #ifdef __SSE3__
 // TODO implement SSE2 versions as well as integer versions
-inline __m128 ei_preduxp(const __m128* vecs)
+template<> EIGEN_STRONG_INLINE __m128 ei_preduxp<__m128>(const __m128* vecs)
 {
   return _mm_hadd_ps(_mm_hadd_ps(vecs[0], vecs[1]),_mm_hadd_ps(vecs[2], vecs[3]));
 }
-inline __m128d ei_preduxp(const __m128d* vecs)
+template<> EIGEN_STRONG_INLINE __m128d ei_preduxp<__m128d>(const __m128d* vecs)
 {
   return _mm_hadd_pd(vecs[0], vecs[1]);
 }
 // SSSE3 version:
-// inline __m128i ei_preduxp(const __m128i* vecs)
+// EIGEN_STRONG_INLINE __m128i ei_preduxp(const __m128i* vecs)
 // {
 //   return _mm_hadd_epi32(_mm_hadd_epi32(vecs[0], vecs[1]),_mm_hadd_epi32(vecs[2], vecs[3]));
 // }
 
-inline float ei_predux(const __m128& a)
+template<> EIGEN_STRONG_INLINE float ei_predux<__m128>(const __m128& a)
 {
   __m128 tmp0 = _mm_hadd_ps(a,a);
   return ei_pfirst(_mm_hadd_ps(tmp0, tmp0));
 }
 
-inline double ei_predux(const __m128d& a) { return ei_pfirst(_mm_hadd_pd(a, a)); }
+template<> EIGEN_STRONG_INLINE double ei_predux<__m128d>(const __m128d& a) { return ei_pfirst(_mm_hadd_pd(a, a)); }
 
 // SSSE3 version:
-// inline float ei_predux(const __m128i& a)
+// EIGEN_STRONG_INLINE float ei_predux(const __m128i& a)
 // {
 //   __m128i tmp0 = _mm_hadd_epi32(a,a);
 //   return ei_pfirst(_mm_hadd_epi32(tmp0, tmp0));
 // }
 #else
 // SSE2 versions
-inline float ei_predux(const __m128& a)
+template<> EIGEN_STRONG_INLINE float ei_predux<__m128>(const __m128& a)
 {
   __m128 tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
   return ei_pfirst(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
 }
-inline double ei_predux(const __m128d& a)
+template<> EIGEN_STRONG_INLINE double ei_predux<__m128d>(const __m128d& a)
 {
   return ei_pfirst(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
 }
 
-inline __m128 ei_preduxp(const __m128* vecs)
+template<> EIGEN_STRONG_INLINE __m128 ei_preduxp<__m128>(const __m128* vecs)
 {
   __m128 tmp0, tmp1, tmp2;
   tmp0 = _mm_unpacklo_ps(vecs[0], vecs[1]);
@@ -174,19 +174,19 @@ inline __m128 ei_preduxp(const __m128* vecs)
   return _mm_add_ps(tmp0, tmp2);
 }
 
-inline __m128d ei_preduxp(const __m128d* vecs)
+template<> EIGEN_STRONG_INLINE __m128d ei_preduxp<__m128d>(const __m128d* vecs)
 {
   return _mm_add_pd(_mm_unpacklo_pd(vecs[0], vecs[1]), _mm_unpackhi_pd(vecs[0], vecs[1]));
 }
 #endif  // SSE3
 
-inline int ei_predux(const __m128i& a)
+template<> EIGEN_STRONG_INLINE int ei_predux<__m128i>(const __m128i& a)
 {
   __m128i tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a,a));
   return ei_pfirst(tmp) + ei_pfirst(_mm_shuffle_epi32(tmp, 1));
 }
 
-inline __m128i ei_preduxp(const __m128i* vecs)
+template<> EIGEN_STRONG_INLINE __m128i ei_preduxp<__m128i>(const __m128i* vecs)
 {
   __m128i tmp0, tmp1, tmp2;
   tmp0 = _mm_unpacklo_epi32(vecs[0], vecs[1]);
@@ -201,13 +201,13 @@ inline __m128i ei_preduxp(const __m128i* vecs)
 }
 
 #if (defined __GNUC__)
-// template <> inline __m128 ei_pmadd(const __m128&  a, const __m128&  b, const __m128&  c)
+// template <> EIGEN_STRONG_INLINE __m128 ei_pmadd(const __m128&  a, const __m128&  b, const __m128&  c)
 // {
 //   __m128 res = b;
 //   asm("mulps %[a], %[b] \n\taddps %[c], %[b]" : [b] "+x" (res) : [a] "x" (a), [c] "x" (c));
 //   return res;
 // }
-// inline __m128i _mm_alignr_epi8(const __m128i&  a, const __m128i&  b, const int i)
+// EIGEN_STRONG_INLINE __m128i _mm_alignr_epi8(const __m128i&  a, const __m128i&  b, const int i)
 // {
 //   __m128i res = a;
 //   asm("palignr %[i], %[a], %[b] " : [b] "+x" (res) : [a] "x" (a), [i] "i" (i));
@@ -220,7 +220,7 @@ inline __m128i ei_preduxp(const __m128i* vecs)
 template<int Offset>
 struct ei_palign_impl<Offset,__m128>
 {
-  inline static void run(__m128& first, const __m128& second)
+  EIGEN_STRONG_INLINE static void run(__m128& first, const __m128& second)
   {
     if (Offset!=0)
       first = _mm_castsi128_ps(_mm_alignr_epi8(_mm_castps_si128(second), _mm_castps_si128(first), Offset*4));
@@ -230,7 +230,7 @@ struct ei_palign_impl<Offset,__m128>
 template<int Offset>
 struct ei_palign_impl<Offset,__m128i>
 {
-  inline static void run(__m128i& first, const __m128i& second)
+  EIGEN_STRONG_INLINE static void run(__m128i& first, const __m128i& second)
   {
     if (Offset!=0)
       first = _mm_alignr_epi8(second,first, Offset*4);
@@ -240,7 +240,7 @@ struct ei_palign_impl<Offset,__m128i>
 template<int Offset>
 struct ei_palign_impl<Offset,__m128d>
 {
-  inline static void run(__m128d& first, const __m128d& second)
+  EIGEN_STRONG_INLINE static void run(__m128d& first, const __m128d& second)
   {
     if (Offset==1)
       first = _mm_castsi128_pd(_mm_alignr_epi8(_mm_castpd_si128(second), _mm_castpd_si128(first), 8));
@@ -251,7 +251,7 @@ struct ei_palign_impl<Offset,__m128d>
 template<int Offset>
 struct ei_palign_impl<Offset,__m128>
 {
-  inline static void run(__m128& first, const __m128& second)
+  EIGEN_STRONG_INLINE static void run(__m128& first, const __m128& second)
   {
     if (Offset==1)
     {
@@ -274,7 +274,7 @@ struct ei_palign_impl<Offset,__m128>
 template<int Offset>
 struct ei_palign_impl<Offset,__m128i>
 {
-  inline static void run(__m128i& first, const __m128i& second)
+  EIGEN_STRONG_INLINE static void run(__m128i& first, const __m128i& second)
   {
     if (Offset==1)
     {
@@ -297,7 +297,7 @@ struct ei_palign_impl<Offset,__m128i>
 template<int Offset>
 struct ei_palign_impl<Offset,__m128d>
 {
-  inline static void run(__m128d& first, const __m128d& second)
+  EIGEN_STRONG_INLINE static void run(__m128d& first, const __m128d& second)
   {
     if (Offset==1)
     {

Eigen/src/Core/util/Constants.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -26,14 +26,37 @@
 #ifndef EIGEN_CONSTANTS_H
 #define EIGEN_CONSTANTS_H
 
+/** This value means that a quantity is not known at compile-time, and that instead the value is
+  * stored in some runtime variable.
+  *
+  * Explanation for the choice of this value:
+  * - It should be positive and larger than any reasonable compile-time-fixed number of rows or columns.
+  *   This allows to simplify many compile-time conditions throughout Eigen.
+  * - It should be smaller than the sqrt of INT_MAX. Indeed, we often multiply a number of rows with a number
+  *   of columns in order to compute a number of coefficients. Even if we guard that with an "if" checking whether
+  *   the values are Dynamic, we still get a compiler warning "integer overflow". So the only way to get around
+  *   it would be a meta-selector. Doing this everywhere would reduce code readability and lenghten compilation times.
+  *   Also, disabling compiler warnings for integer overflow, sounds like a bad idea.
+  *
+  * If you wish to port Eigen to a platform where sizeof(int)==2, it is perfectly possible to set Dynamic to, say, 100.
+  */
 const int Dynamic = 10000;
 
+/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
+  * The value Infinity there means the L-infinity norm.
+  */
+const int Infinity = -1;
+
 /** \defgroup flags flags
   * \ingroup Core_Module
   *
   * These are the possible bits which can be OR'ed to constitute the flags of a matrix or
   * expression.
   *
+  * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of
+  * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any
+  * runtime overhead.
+  *
   * \sa MatrixBase::Flags
   */
 
@@ -162,16 +185,16 @@ const unsigned int HereditaryBits = RowMajorBit
                                   | SparseBit;
 
 // Possible values for the Mode parameter of part() and of extract()
-const unsigned int Upper = UpperTriangularBit;
-const unsigned int StrictlyUpper = UpperTriangularBit | ZeroDiagBit;
-const unsigned int Lower = LowerTriangularBit;
-const unsigned int StrictlyLower = LowerTriangularBit | ZeroDiagBit;
+const unsigned int UpperTriangular = UpperTriangularBit;
+const unsigned int StrictlyUpperTriangular = UpperTriangularBit | ZeroDiagBit;
+const unsigned int LowerTriangular = LowerTriangularBit;
+const unsigned int StrictlyLowerTriangular = LowerTriangularBit | ZeroDiagBit;
 const unsigned int SelfAdjoint = SelfAdjointBit;
 
 // additional possible values for the Mode parameter of extract()
-const unsigned int UnitUpper = UpperTriangularBit | UnitDiagBit;
-const unsigned int UnitLower = LowerTriangularBit | UnitDiagBit;
-const unsigned int Diagonal = Upper | Lower;
+const unsigned int UnitUpperTriangular = UpperTriangularBit | UnitDiagBit;
+const unsigned int UnitLowerTriangular = LowerTriangularBit | UnitDiagBit;
+const unsigned int Diagonal = UpperTriangular | LowerTriangular;
 
 enum { Aligned, Unaligned };
 enum { ForceAligned, AsRequested };
@@ -194,21 +217,28 @@ enum {
 };
 
 enum {
-  CompleteUnrolling,
+  NoUnrolling,
   InnerUnrolling,
-  NoUnrolling
+  CompleteUnrolling
 };
 
 enum {
-  ColMajor = 0,
-  RowMajor = RowMajorBit
+  Matrix_ColMajor = 0,
+  Matrix_RowMajor = 0x1,  // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
+  /** \internal Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be
+                requested to be aligned) */
+  ColMajor = Matrix_ColMajor, // deprecated
+  RowMajor = Matrix_RowMajor, // deprecated
+  Matrix_DontAlign = 0,
+  /** \internal Align the matrix itself */
+  Matrix_AutoAlign = 0x2
 };
 
 enum {
   IsDense         = 0,
+  IsSparse        = SparseBit,
   NoDirectAccess  = 0,
-  HasDirectAccess = DirectAccessBit,
-  IsSparse        = SparseBit
+  HasDirectAccess = DirectAccessBit
 };
 
 const int FullyCoherentAccessPattern  = 0x1;

Eigen/src/Core/util/DisableMSVCWarnings.h

@@ -0,0 +1,9 @@
+#ifndef EIGEN_DISABLEMSVCWARNINGS_H
+#define EIGEN_DISABLEMSVCWARNINGS_H
+
+#ifdef _MSC_VER
+  #pragma warning( push )
+  #pragma warning( disable : 4181 4244 4127 4211 )
+#endif
+
+#endif // EIGEN_DISABLEMSVCWARNINGS_H

Eigen/src/Core/util/EnableMSVCWarnings.h

@@ -0,0 +1,8 @@
+#ifndef EIGEN_ENABLEMSVCWARNINGS_H
+#define EIGEN_ENABLEMSVCWARNINGS_H
+
+#ifdef _MSC_VER
+  #pragma warning( pop )
+#endif
+
+#endif // EIGEN_ENABLEMSVCWARNINGS_H

Eigen/src/Core/util/ForwardDeclarations.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -28,7 +28,8 @@
 template<typename T> struct ei_traits;
 template<typename T> struct NumTraits;
 
-template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder = ColMajor,
+template<typename _Scalar, int _Rows, int _Cols,
+         int _Options = EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION | Matrix_AutoAlign,
          int _MaxRows = _Rows, int _MaxCols = _Cols> class Matrix;
 
 template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged;
@@ -64,6 +65,7 @@ template<typename Scalar> struct ei_scalar_quotient_op;
 template<typename Scalar> struct ei_scalar_opposite_op;
 template<typename Scalar> struct ei_scalar_conjugate_op;
 template<typename Scalar> struct ei_scalar_real_op;
+template<typename Scalar> struct ei_scalar_imag_op;
 template<typename Scalar> struct ei_scalar_abs_op;
 template<typename Scalar> struct ei_scalar_abs2_op;
 template<typename Scalar> struct ei_scalar_sqrt_op;
@@ -102,6 +104,9 @@ template<typename ExpressionType, int Direction> class PartialRedux;
 template<typename MatrixType> class LU;
 template<typename MatrixType> class QR;
 template<typename MatrixType> class SVD;
+template<typename MatrixType> class LLT;
+template<typename MatrixType> class LDLT;
+// deprecated:
 template<typename MatrixType> class Cholesky;
 template<typename MatrixType> class CholeskyWithoutSquareRoot;
 

Eigen/src/Core/util/Macros.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -28,7 +28,24 @@
 
 #undef minor
 
-/** \internal  Defines the maximal loop size to enable meta unrolling of loops */
+#define EIGEN_WORLD_VERSION 2
+#define EIGEN_MAJOR_VERSION 0
+#define EIGEN_MINOR_VERSION 0
+
+#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
+                                      (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
+                                                                 EIGEN_MINOR_VERSION>=z))))
+
+#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Matrix_RowMajor
+#else
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Matrix_ColMajor
+#endif
+
+/** \internal  Defines the maximal loop size to enable meta unrolling of loops.
+  *            Note that the value here is expressed in Eigen's own notion of "number of FLOPS",
+  *            it does not correspond to the number of iterations or the number of instructions
+  */
 #ifndef EIGEN_UNROLLING_LIMIT
 #define EIGEN_UNROLLING_LIMIT 100
 #endif
@@ -36,7 +53,7 @@
 /** \internal Define the maximal size in Bytes of L2 blocks.
   * The current value is set to generate blocks of 256x256 for float */
 #ifndef EIGEN_TUNE_FOR_L2_CACHE_SIZE
-#define EIGEN_TUNE_FOR_L2_CACHE_SIZE (1024*256)
+#define EIGEN_TUNE_FOR_L2_CACHE_SIZE (sizeof(float)*256*256)
 #endif
 
 #define USING_PART_OF_NAMESPACE_EIGEN \
@@ -70,7 +87,7 @@ using Eigen::ei_cos;
 #endif
 
 #ifdef EIGEN_INTERNAL_DEBUGGING
-#define ei_internal_assert(x) ei_assert(x);
+#define ei_internal_assert(x) ei_assert(x)
 #else
 #define ei_internal_assert(x)
 #endif
@@ -81,44 +98,83 @@ using Eigen::ei_cos;
 #define EIGEN_ONLY_USED_FOR_DEBUG(x)
 #endif
 
+// EIGEN_ALWAYS_INLINE_ATTRIB should be use in the declaration of function
+// which should be inlined even in debug mode.
 // FIXME with the always_inline attribute,
 // gcc 3.4.x reports the following compilation error:
 //   Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
 //    : function body not available
 #if EIGEN_GNUC_AT_LEAST(4,0)
-#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
+#define EIGEN_ALWAYS_INLINE_ATTRIB __attribute__((always_inline))
 #else
-#define EIGEN_ALWAYS_INLINE inline
+#define EIGEN_ALWAYS_INLINE_ATTRIB
+#endif
+
+// EIGEN_FORCE_INLINE means "inline as much as possible"
+#if (defined _MSC_VER)
+#define EIGEN_STRONG_INLINE __forceinline
+#else
+#define EIGEN_STRONG_INLINE inline
 #endif
 
 #if (defined __GNUC__)
 #define EIGEN_DONT_INLINE __attribute__((noinline))
+#elif (defined _MSC_VER)
+#define EIGEN_DONT_INLINE __declspec(noinline)
 #else
 #define EIGEN_DONT_INLINE
 #endif
 
 #if (defined __GNUC__)
-#define EIGEN_ALIGN_128 __attribute__ ((aligned(16)))
+#define EIGEN_DEPRECATED __attribute__((deprecated))
+#elif (defined _MSC_VER)
+#define EIGEN_DEPRECATED __declspec(deprecated)
+#else
+#define EIGEN_DEPRECATED
+#endif
+
+/* EIGEN_ALIGN_128 forces data to be 16-byte aligned, EVEN if vectorization (EIGEN_VECTORIZE) is disabled,
+ * so that vectorization doesn't affect binary compatibility.
+ *
+ * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
+ * vectorized and non-vectorized code.
+ */
+#if (defined __GNUC__)
+#define EIGEN_ALIGN_128 __attribute__((aligned(16)))
+#elif (defined _MSC_VER)
+#define EIGEN_ALIGN_128 __declspec(align(16))
 #else
 #define EIGEN_ALIGN_128
 #endif
 
 #define EIGEN_RESTRICT __restrict
 
+#ifndef EIGEN_STACK_ALLOCATION_LIMIT
+#define EIGEN_STACK_ALLOCATION_LIMIT 16000000
+#endif
+
+#ifndef EIGEN_DEFAULT_IO_FORMAT
+#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat()
+#endif
+
+// format used in Eigen's documentation
+// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic.
+#define EIGEN_DOCS_IO_FORMAT IOFormat(3, AlignCols, " ", "\n", "", "")
+
 #define EIGEN_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
 template<typename OtherDerived> \
-Derived& operator Op(const MatrixBase<OtherDerived>& other) \
+EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::MatrixBase<OtherDerived>& other) \
 { \
   return Eigen::MatrixBase<Derived>::operator Op(other.derived()); \
 } \
-Derived& operator Op(const Derived& other) \
+EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
 { \
   return Eigen::MatrixBase<Derived>::operator Op(other); \
 }
 
 #define EIGEN_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
 template<typename Other> \
-Derived& operator Op(const Other& scalar) \
+EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
 { \
   return Eigen::MatrixBase<Derived>::operator Op(scalar); \
 }
@@ -136,7 +192,6 @@ typedef typename Eigen::ei_traits<Derived>::Scalar Scalar; \
 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
 typedef typename Base::PacketScalar PacketScalar; \
 typedef typename Eigen::ei_nested<Derived>::type Nested; \
-typedef typename Eigen::ei_eval<Derived>::type Eval; \
 enum { RowsAtCompileTime = Eigen::ei_traits<Derived>::RowsAtCompileTime, \
        ColsAtCompileTime = Eigen::ei_traits<Derived>::ColsAtCompileTime, \
        MaxRowsAtCompileTime = Eigen::ei_traits<Derived>::MaxRowsAtCompileTime, \

Eigen/src/Core/util/Memory.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -26,52 +26,87 @@
 #ifndef EIGEN_MEMORY_H
 #define EIGEN_MEMORY_H
 
-#ifdef EIGEN_VECTORIZE
+#ifdef __linux
 // it seems we cannot assume posix_memalign is defined in the stdlib header
 extern "C" int posix_memalign (void **, size_t, size_t) throw ();
 #endif
 
-/** \internal
-  * Static array automatically aligned if the total byte size is a multiple of 16
+struct ei_byte_forcing_aligned_malloc
+{
+  unsigned char c; // sizeof must be 1.
+};
+template<typename T> struct ei_force_aligned_malloc { enum { ret = 0 }; };
+template<> struct ei_force_aligned_malloc<ei_byte_forcing_aligned_malloc> { enum { ret = 1 }; };
+
+/** \internal allocates \a size * sizeof(\a T) bytes. If vectorization is enabled and T is such that a packet
+  * containts more than one T, then the returned pointer is guaranteed to have 16 bytes alignment.
+  * On allocation error, the returned pointer is undefined, but if exceptions are enabled then a std::bad_alloc is thrown.
   */
-template <typename T, int Size, bool Align> struct ei_aligned_array
-{
-  EIGEN_ALIGN_128 T array[Size];
-};
-
-template <typename T, int Size> struct ei_aligned_array<T,Size,false>
-{
-  T array[Size];
-};
-
-/** \internal allocates \a size * sizeof(\a T) bytes with a 16 bytes based alignment */
 template<typename T>
 inline T* ei_aligned_malloc(size_t size)
 {
-  #ifdef EIGEN_VECTORIZE
-  if (ei_packet_traits<T>::size>1)
+  if(ei_packet_traits<T>::size>1 || ei_force_aligned_malloc<T>::ret)
   {
-    void* ptr;
-    if (posix_memalign(&ptr, 16, size*sizeof(T))==0)
-      return static_cast<T*>(ptr);
-    else
-      return 0;
+    void *void_result;
+    #ifdef __linux
+      #ifdef EIGEN_EXCEPTIONS
+        const int failed =
+      #endif
+      posix_memalign(&void_result, 16, size*sizeof(T));
+    #else
+      #ifdef _MSC_VER
+        void_result = _aligned_malloc(size*sizeof(T), 16);
+      #elif defined(__APPLE__)
+        void_result = malloc(size*sizeof(T)); // Apple's malloc() already returns aligned ptrs
+      #else
+        void_result = _mm_malloc(size*sizeof(T), 16);
+      #endif
+      #ifdef EIGEN_EXCEPTIONS
+        const int failed = (void_result == 0);
+      #endif
+    #endif
+    #ifdef EIGEN_EXCEPTIONS
+      if(failed)
+        throw std::bad_alloc();
+    #endif
+    // if the user uses Eigen on some fancy scalar type such as multiple-precision numbers,
+    // and this type has a custom operator new, then we want to honor this operator new!
+    // so when we use C functions to allocate memory, we must be careful to call manually the constructor using
+    // the special placement-new syntax.
+    return new(void_result) T[size];
   }
   else
-  #endif
-    return new T[size];
+    return new T[size]; // here we really want a new, not a malloc. Justification: if the user uses Eigen on
+      // some fancy scalar type such as multiple-precision numbers, and this type has a custom operator new,
+      // then we want to honor this operator new! Anyway this type won't have vectorization so the vectorizing path
+      // is irrelevant here. Yes, we should say somewhere in the docs that if the user uses a custom scalar type then
+      // he can't have both vectorization and a custom operator new on his scalar type.
 }
 
-/** \internal free memory allocated with ei_aligned_malloc */
+/** \internal free memory allocated with ei_aligned_malloc
+  * The \a size parameter is used to determine on how many elements to call the destructor. If you don't
+  * want any destructor to be called, just pass 0.
+  */
 template<typename T>
-inline void ei_aligned_free(T* ptr)
+inline void ei_aligned_free(T* ptr, size_t size)
 {
-  #ifdef EIGEN_VECTORIZE
-  if (ei_packet_traits<T>::size>1)
-    free(ptr);
-  else
-  #endif
-    delete[] ptr;
+    if (ei_packet_traits<T>::size>1 || ei_force_aligned_malloc<T>::ret)
+    {
+      // need to call manually the dtor in case T is some user-defined fancy numeric type.
+      // always destruct an array starting from the end.
+      while(size) ptr[--size].~T();
+      #if defined(__linux)
+        free(ptr);
+      #elif defined(__APPLE__)
+        free(ptr);
+      #elif defined(_MSC_VER)
+        _aligned_free(ptr);
+      #else
+        _mm_free(ptr);
+      #endif
+    }
+    else
+      delete[] ptr;
 }
 
 /** \internal \returns the number of elements which have to be skipped such that data are 16 bytes aligned */
@@ -83,40 +118,43 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
   const int PacketAlignedMask = PacketSize-1;
   const bool Vectorized = PacketSize>1;
   return Vectorized
-          ? std::min<int>( (PacketSize - ((size_t(ptr)/sizeof(Scalar)) & PacketAlignedMask))
+          ? std::min<int>( (PacketSize - (int((size_t(ptr)/sizeof(Scalar))) & PacketAlignedMask))
                            & PacketAlignedMask, maxOffset)
           : 0;
 }
 
 /** \internal
-  * ei_alloc_stack(TYPE,SIZE) allocates sizeof(TYPE)*SIZE bytes on the stack if sizeof(TYPE)*SIZE is
-  * smaller than EIGEN_STACK_ALLOCATION_LIMIT. Otherwise the memory is allocated using the operator new.
-  * Data allocated with ei_alloc_stack \b must be freed calling ei_free_stack(PTR,TYPE,SIZE).
+  * ei_aligned_stack_alloc(TYPE,SIZE) allocates an aligned buffer of sizeof(TYPE)*SIZE bytes
+  * on the stack if sizeof(TYPE)*SIZE is smaller than EIGEN_STACK_ALLOCATION_LIMIT.
+  * Otherwise the memory is allocated on the heap.
+  * Data allocated with ei_aligned_stack_alloc \b must be freed by calling ei_aligned_stack_free(PTR,TYPE,SIZE).
   * \code
-  * float * data = ei_alloc_stack(float,array.size());
+  * float * data = ei_aligned_stack_alloc(float,array.size());
   * // ...
-  * ei_free_stack(data,float,array.size());
+  * ei_aligned_stack_free(data,float,array.size());
   * \endcode
   */
 #ifdef __linux__
-# define ei_alloc_stack(TYPE,SIZE) ((sizeof(TYPE)*(SIZE)>16000000) ? new TYPE[SIZE] : (TYPE*)alloca(sizeof(TYPE)*(SIZE)))
-# define ei_free_stack(PTR,TYPE,SIZE) if (sizeof(TYPE)*SIZE>16000000) delete[] PTR
+  #define ei_aligned_stack_alloc(TYPE,SIZE) ((sizeof(TYPE)*(SIZE)>EIGEN_STACK_ALLOCATION_LIMIT) \
+                                    ? ei_aligned_malloc<TYPE>(SIZE) \
+                                    : (TYPE*)alloca(sizeof(TYPE)*(SIZE)))
+  #define ei_aligned_stack_free(PTR,TYPE,SIZE) if (sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT) ei_aligned_free(PTR,SIZE)
 #else
-# define ei_alloc_stack(TYPE,SIZE) new TYPE[SIZE]
-# define ei_free_stack(PTR,TYPE,SIZE) delete[] PTR
+  #define ei_aligned_stack_alloc(TYPE,SIZE) ei_aligned_malloc<TYPE>(SIZE)
+  #define ei_aligned_stack_free(PTR,TYPE,SIZE) ei_aligned_free(PTR,SIZE)
 #endif
 
 /** \class WithAlignedOperatorNew
   *
-  * \brief Enforces inherited classes to be 16 bytes aligned when dynamicalled allocated with operator new
+  * \brief Enforces instances of inherited classes to be 16 bytes aligned when allocated with operator new
   *
   * When Eigen's explicit vectorization is enabled, Eigen assumes that some fixed sizes types are aligned
-  * on a 16 bytes boundary. Such types include:
+  * on a 16 bytes boundary. Those include all Matrix types having a sizeof multiple of 16 bytes, e.g.:
   *  - Vector2d, Vector4f, Vector4i, Vector4d,
   *  - Matrix2d, Matrix4f, Matrix4i, Matrix4d,
   *  - etc.
-  * When objects are statically allocated, the compiler will automatically and always enforces 16 bytes
-  * alignment of the data. However some troubles might appear when data are dynamically allocated.
+  * When an object is statically allocated, the compiler will automatically and always enforces 16 bytes
+  * alignment of the data when needed. However some troubles might appear when data are dynamically allocated.
   * Let's pick an example:
   * \code
   * struct Foo {
@@ -130,55 +168,40 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset)
   * pObj2->some_vector = Vector4f(..);  // =>  !! might segfault !!
   * \endcode
   * Here, the problem is that operator new is not aware of the compile time alignment requirement of the
-  * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returned a 16 bytes
-  * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue, by
+  * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returns a 16 bytes
+  * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue by
   * overloading the operator new to return aligned data when the vectorization is enabled.
   * Here is a similar safe example:
   * \code
-  * struct Foo : WithAlignedOperatorNew {
+  * struct Foo : public WithAlignedOperatorNew {
   *   char dummy;
   *   Vector4f some_vector;
   * };
-  * Foo obj1;                           // static allocation
-  * obj1.some_vector = Vector4f(..);    // =>   OK
-  *
   * Foo *pObj2 = new Foo;               // dynamic allocation
   * pObj2->some_vector = Vector4f(..);  // =>  SAFE !
-  * \endcode 
+  * \endcode
   *
   * \sa class ei_new_allocator
   */
 struct WithAlignedOperatorNew
 {
-  #ifdef EIGEN_VECTORIZE
-
   void *operator new(size_t size) throw()
   {
-    void* ptr = 0;
-    if (posix_memalign(&ptr, 16, size)==0)
-      return ptr;
-    else
-      return 0;
+    return ei_aligned_malloc<ei_byte_forcing_aligned_malloc>(size);
   }
 
   void *operator new[](size_t size) throw()
   {
-    void* ptr = 0;
-    if (posix_memalign(&ptr, 16, size)==0)
-      return ptr;
-    else
-      return 0;
+    return ei_aligned_malloc<ei_byte_forcing_aligned_malloc>(size);
   }
 
-  void operator delete(void * ptr) { free(ptr); }
-  void operator delete[](void * ptr) { free(ptr); }
-  
-  #endif
+  void operator delete(void * ptr) { ei_aligned_free(static_cast<ei_byte_forcing_aligned_malloc *>(ptr), 0); }
+  void operator delete[](void * ptr) { ei_aligned_free(static_cast<ei_byte_forcing_aligned_malloc *>(ptr), 0); }
 };
 
 template<typename T, int SizeAtCompileTime,
          bool NeedsToAlign = (SizeAtCompileTime!=Dynamic) && ((sizeof(T)*SizeAtCompileTime)%16==0)>
-struct ei_with_aligned_operator_new : WithAlignedOperatorNew {};
+struct ei_with_aligned_operator_new : public WithAlignedOperatorNew {};
 
 template<typename T, int SizeAtCompileTime>
 struct ei_with_aligned_operator_new<T,SizeAtCompileTime,false> {};
@@ -190,14 +213,14 @@ struct ei_with_aligned_operator_new<T,SizeAtCompileTime,false> {};
   * STL allocator simply wrapping operators new[] and delete[]. Unlike GCC's default new_allocator,
   * ei_new_allocator call operator new on the type \a T and not the general new operator ignoring
   * overloaded version of operator new.
-  * 
+  *
   * Example:
   * \code
   * // Vector4f requires 16 bytes alignment:
   * std::vector<Vector4f,ei_new_allocator<Vector4f> > dataVec4;
   * // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
   * std::vector<Vector3f> dataVec3;
-  * 
+  *
   * struct Foo : WithAlignedOperatorNew {
   *   char dummy;
   *   Vector4f some_vector;

Eigen/src/Core/util/Meta.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -69,7 +69,7 @@ template<typename T> struct ei_cleantype<T*>        { typedef typename ei_cleant
   *
   * It supports both the current STL mechanism (using the result_type member) as well as
   * upcoming next STL generation (using a templated result member).
-  * If none of these members is provided, then the type of the first argument is returned.
+  * If none of these members is provided, then the type of the first argument is returned. FIXME, that behavior is a pretty bad hack.
   */
 template<typename T> struct ei_result_of {};
 
@@ -146,4 +146,38 @@ class ei_meta_sqrt
 template<int Y, int InfX, int SupX>
 class ei_meta_sqrt<Y, InfX, SupX, true> { public:  enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
 
+/** \internal determines whether the product of two numeric types is allowed and what the return type is */
+template<typename T, typename U> struct ei_scalar_product_traits
+{
+  // dummy general case where T and U aren't compatible -- not allowed anyway but we catch it elsewhere
+  //enum { Cost = NumTraits<T>::MulCost };
+  typedef T ReturnType;
+};
+
+template<typename T> struct ei_scalar_product_traits<T,T>
+{
+  //enum { Cost = NumTraits<T>::MulCost };
+  typedef T ReturnType;
+};
+
+template<typename T> struct ei_scalar_product_traits<T,std::complex<T> >
+{
+  //enum { Cost = 2*NumTraits<T>::MulCost };
+  typedef std::complex<T> ReturnType;
+};
+
+template<typename T> struct ei_scalar_product_traits<std::complex<T>, T>
+{
+  //enum { Cost = 2*NumTraits<T>::MulCost  };
+  typedef std::complex<T> ReturnType;
+};
+
+// FIXME quick workaround around current limitation of ei_result_of
+template<typename Scalar, typename ArgType0, typename ArgType1>
+struct ei_result_of<ei_scalar_product_op<Scalar>(ArgType0,ArgType1)> {
+typedef typename ei_scalar_product_traits<typename ei_cleantype<ArgType0>::type, typename ei_cleantype<ArgType1>::type>::ReturnType type;
+};
+
+
+
 #endif // EIGEN_META_H

Eigen/src/Core/util/StaticAssert.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -44,7 +44,7 @@
   #ifdef __GXX_EXPERIMENTAL_CXX0X__
 
     // if native static_assert is enabled, let's use it
-    #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG)
+    #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG);
 
   #else // CXX0X
 
@@ -55,29 +55,46 @@
     struct ei_static_assert<true>
     {
       enum {
-        you_tried_calling_a_vector_method_on_a_matrix,
-        you_mixed_vectors_of_different_sizes,
-        you_mixed_matrices_of_different_sizes,
-        this_method_is_only_for_vectors_of_a_specific_size,
-        this_method_is_only_for_matrices_of_a_specific_size,
-        you_did_a_programming_error,
-        you_called_a_fixed_size_method_on_a_dynamic_size_matrix_or_vector,
-        unaligned_load_and_store_operations_unimplemented_on_AltiVec,
-        scalar_type_must_be_floating_point,
-        default_writting_to_selfadjoint_not_supported,
-        writting_to_triangular_part_with_unit_diag_is_not_supported,
-        this_method_is_only_for_fixed_size
+        YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX,
+        YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES,
+        YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES,
+        THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE,
+        THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE,
+        YOU_MADE_A_PROGRAMMING_MISTAKE,
+        YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR,
+        UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC,
+        NUMERIC_TYPE_MUST_BE_FLOATING_POINT,
+        COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED,
+        WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED,
+        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE,
+        INVALID_MATRIX_PRODUCT,
+        INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS,
+        INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION,
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY,
+        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES
       };
     };
 
-    #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
-      if (ei_static_assert<CONDITION ? true : false>::MSG) {}
+    // Specialized implementation for MSVC to avoid "conditional
+    // expression is constant" warnings.  This implementation doesn't
+    // appear to work under GCC, hence the multiple implementations.
+    #ifdef _MSC_VER
 
-  #endif // CXX0X
+      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
+        {Eigen::ei_static_assert<CONDITION ? true : false>::MSG;}
+
+    #else
+
+      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
+        if (Eigen::ei_static_assert<CONDITION ? true : false>::MSG) {}
+
+    #endif
+
+  #endif // not CXX0X
 
 #else // EIGEN_NO_STATIC_ASSERT
 
-  #define EIGEN_STATIC_ASSERT(CONDITION,MSG) ei_assert((CONDITION) && #MSG)
+  #define EIGEN_STATIC_ASSERT(CONDITION,MSG) ei_assert((CONDITION) && #MSG);
 
 #endif // EIGEN_NO_STATIC_ASSERT
 
@@ -85,22 +102,22 @@
 // static assertion failing if the type \a TYPE is not a vector type
 #define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \
   EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \
-                      you_tried_calling_a_vector_method_on_a_matrix)
+                      YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)
 
 // static assertion failing if the type \a TYPE is not fixed-size
 #define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \
   EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \
-                      you_called_a_fixed_size_method_on_a_dynamic_size_matrix_or_vector)
+                      YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR)
 
 // static assertion failing if the type \a TYPE is not a vector type of the given size
 #define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \
   EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \
-                      this_method_is_only_for_vectors_of_a_specific_size)
+                      THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE)
 
 // static assertion failing if the type \a TYPE is not a vector type of the given size
 #define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \
   EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \
-                      this_method_is_only_for_matrices_of_a_specific_size)
+                      THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
 
 // static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size)
 #define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \
@@ -108,17 +125,20 @@
       (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \
     || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \
     || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\
-    you_mixed_vectors_of_different_sizes)
+    YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
 
-// static assertion failing if the two matrix expression types are not compatible (same fixed-size or dynamic size)
-#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
-  EIGEN_STATIC_ASSERT( \
-     ((int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \
+#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
+      ((int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \
     || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \
     || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \
    && (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \
     || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \
-    || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))),\
-    you_mixed_matrices_of_different_sizes)
+    || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime)))
+
+// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes
+#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
+  EIGEN_STATIC_ASSERT( \
+     EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\
+    YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
 
 #endif // EIGEN_STATIC_ASSERT_H

Eigen/src/Core/util/XprHelper.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -41,6 +41,10 @@ class ei_no_assignment_operator
     ei_no_assignment_operator& operator=(const ei_no_assignment_operator&);
 };
 
+/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around an int variable that
+  * can be accessed using value() and setValue().
+  * Otherwise, this class is an empty structure and value() just returns the template parameter Value.
+  */
 template<int Value> class ei_int_if_dynamic EIGEN_EMPTY_STRUCT
 {
   public:
@@ -81,22 +85,16 @@ template<typename T> struct ei_unpacket_traits
   enum {size=1};
 };
 
-
-template<typename Scalar, int Rows, int Cols, int StorageOrder, int MaxRows, int MaxCols>
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
 class ei_compute_matrix_flags
 {
     enum {
-      row_major_bit = (Rows != 1 && Cols != 1)  // if this is not a vector,
-                                                // then the storage order really matters,
-                                                // so let us strictly honor the user's choice.
-                    ? StorageOrder
-                    : Cols > 1 ? RowMajorBit : 0,
+      row_major_bit = Options&Matrix_RowMajor ? RowMajorBit : 0,
       inner_max_size = row_major_bit ? MaxCols : MaxRows,
       is_big = inner_max_size == Dynamic,
-      is_packet_size_multiple = (Cols * Rows)%ei_packet_traits<Scalar>::size==0,
-      packet_access_bit = ei_packet_traits<Scalar>::size > 1
-                          && (is_big || is_packet_size_multiple) ? PacketAccessBit : 0,
-      aligned_bit = packet_access_bit && (is_big || is_packet_size_multiple) ? AlignedBit : 0
+      is_packet_size_multiple = (Cols*Rows) % ei_packet_traits<Scalar>::size == 0,
+      aligned_bit = ((Options&Matrix_AutoAlign) && (is_big || is_packet_size_multiple)) ? AlignedBit : 0,
+      packet_access_bit = ei_packet_traits<Scalar>::size > 1 && aligned_bit ? PacketAccessBit : 0
     };
 
   public:
@@ -108,6 +106,10 @@ template<int _Rows, int _Cols> struct ei_size_at_compile_time
   enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols };
 };
 
+/* ei_eval : the return type of eval(). For matrices, this is just a const reference
+ * in order to avoid a useless copy
+ */
+
 template<typename T, int Sparseness = ei_traits<T>::Flags&SparseBit> class ei_eval;
 
 template<typename T> struct ei_eval<T,IsDense>
@@ -115,7 +117,41 @@ template<typename T> struct ei_eval<T,IsDense>
   typedef Matrix<typename ei_traits<T>::Scalar,
                 ei_traits<T>::RowsAtCompileTime,
                 ei_traits<T>::ColsAtCompileTime,
-                ei_traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor,
+                Matrix_AutoAlign | (ei_traits<T>::Flags&RowMajorBit ? Matrix_RowMajor : Matrix_ColMajor),
+                ei_traits<T>::MaxRowsAtCompileTime,
+                ei_traits<T>::MaxColsAtCompileTime
+          > type;
+};
+
+// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
+template<typename _Scalar, int _Rows, int _Cols, int _StorageOrder, int _MaxRows, int _MaxCols>
+struct ei_eval<Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols>, IsDense>
+{
+  typedef const Matrix<_Scalar, _Rows, _Cols, _StorageOrder, _MaxRows, _MaxCols>& type;
+};
+
+/* ei_plain_matrix_type : the difference from ei_eval is that ei_plain_matrix_type is always a plain matrix type,
+ * whereas ei_eval is a const reference in the case of a matrix
+ */
+template<typename T> struct ei_plain_matrix_type
+{
+  typedef Matrix<typename ei_traits<T>::Scalar,
+                ei_traits<T>::RowsAtCompileTime,
+                ei_traits<T>::ColsAtCompileTime,
+                Matrix_AutoAlign | (ei_traits<T>::Flags&RowMajorBit ? Matrix_RowMajor : Matrix_ColMajor),
+                ei_traits<T>::MaxRowsAtCompileTime,
+                ei_traits<T>::MaxColsAtCompileTime
+          > type;
+};
+
+/* ei_plain_matrix_type_column_major : same as ei_plain_matrix_type but guaranteed to be column-major
+ */
+template<typename T> struct ei_plain_matrix_type_column_major
+{
+  typedef Matrix<typename ei_traits<T>::Scalar,
+                ei_traits<T>::RowsAtCompileTime,
+                ei_traits<T>::ColsAtCompileTime,
+                Matrix_AutoAlign | Matrix_ColMajor,
                 ei_traits<T>::MaxRowsAtCompileTime,
                 ei_traits<T>::MaxColsAtCompileTime
           > type;
@@ -124,7 +160,25 @@ template<typename T> struct ei_eval<T,IsDense>
 template<typename T> struct ei_must_nest_by_value { enum { ret = false }; };
 template<typename T> struct ei_must_nest_by_value<NestByValue<T> > { enum { ret = true }; };
 
-template<typename T, int n=1, typename EvalType = typename ei_eval<T>::type> struct ei_nested
+/** \internal Determines how a given expression should be nested into another one.
+  * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
+  * nested into the bigger product expression. The choice is between nesting the expression b+c as-is, or
+  * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
+  * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
+  * many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
+  *
+  * \param T the type of the expression being nested
+  * \param n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
+  *
+  * Example. Suppose that a, b, and c are of type Matrix3d. The user forms the expression a*(b+c).
+  * b+c is an expression "sum of matrices", which we will denote by S. In order to determine how to nest it,
+  * the Product expression uses: ei_nested<S, 3>::ret, which turns out to be Matrix3d because the internal logic of
+  * ei_nested determined that in this case it was better to evaluate the expression b+c into a temporary. On the other hand,
+  * since a is of type Matrix3d, the Product expression nests it as ei_nested<Matrix3d, 3>::ret, which turns out to be
+  * const Matrix3d&, because the internal logic of ei_nested determined that since a was already a matrix, there was no point
+  * in copying it into another matrix.
+  */
+template<typename T, int n=1, typename PlainMatrixType = typename ei_eval<T>::type> struct ei_nested
 {
   enum {
     CostEval   = (n+1) * int(NumTraits<typename ei_traits<T>::Scalar>::ReadCost),
@@ -136,7 +190,7 @@ template<typename T, int n=1, typename EvalType = typename ei_eval<T>::type> str
     typename ei_meta_if<
       (int(ei_traits<T>::Flags) & EvalBeforeNestingBit)
       || ( int(CostEval) <= int(CostNoEval) ),
-      EvalType,
+      PlainMatrixType,
       const T&
     >::ret
   >::ret type;
@@ -157,4 +211,9 @@ template<typename ExpressionType, int RowsOrSize=Dynamic, int Cols=Dynamic> stru
   typedef Block<ExpressionType, RowsOrSize, Cols> Type;
 };
 
+template<typename CurrentType, typename NewType> struct ei_cast_return_type
+{
+  typedef typename ei_meta_if<ei_is_same_type<CurrentType,NewType>::ret,const CurrentType&,NewType>::ret type;
+};
+
 #endif // EIGEN_XPRHELPER_H

Eigen/src/Geometry/AlignedBox.h

@@ -0,0 +1,174 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_ALIGNEDBOX_H
+#define EIGEN_ALIGNEDBOX_H
+
+/** \geometry_module \ingroup GeometryModule
+  * \nonstableyet
+  *
+  * \class AlignedBox
+  *
+  * \brief An axis aligned box
+  *
+  * \param _Scalar the type of the scalar coefficients
+  * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
+  *
+  * This class represents an axis aligned box as a pair of the minimal and maximal corners.
+  */
+template <typename _Scalar, int _AmbientDim>
+class AlignedBox
+  : public ei_with_aligned_operator_new<_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1>
+{
+public:
+
+  enum { AmbientDimAtCompileTime = _AmbientDim };
+  typedef _Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
+
+  /** Default constructor initializing a null box. */
+  inline explicit AlignedBox()
+  { if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
+
+  /** Constructs a null box with \a _dim the dimension of the ambient space. */
+  inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim)
+  { setNull(); }
+
+  /** Constructs a box with extremities \a _min and \a _max. */
+  inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {}
+
+  /** Constructs a box containing a single point \a p. */
+  inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {}
+
+  ~AlignedBox() {}
+
+  /** \returns the dimension in which the box holds */
+  inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : AmbientDimAtCompileTime; }
+
+  /** \returns true if the box is null, i.e, empty. */
+  inline bool isNull() const { return (m_min.cwise() > m_max).any(); }
+
+  /** Makes \c *this a null/empty box. */
+  inline void setNull()
+  {
+    m_min.setConstant( std::numeric_limits<Scalar>::max());
+    m_max.setConstant(-std::numeric_limits<Scalar>::max());
+  }
+
+  /** \returns the minimal corner */
+  inline const VectorType& min() const { return m_min; }
+  /** \returns a non const reference to the minimal corner */
+  inline VectorType& min() { return m_min; }
+  /** \returns the maximal corner */
+  inline const VectorType& max() const { return m_max; }
+  /** \returns a non const reference to the maximal corner */
+  inline VectorType& max() { return m_max; }
+
+  /** \returns true if the point \a p is inside the box \c *this. */
+  inline bool contains(const VectorType& p) const
+  { return (m_min.cwise()<=p).all() && (p.cwise()<=m_max).all(); }
+
+  /** \returns true if the box \a b is entirely inside the box \c *this. */
+  inline bool contains(const AlignedBox& b) const
+  { return (m_min.cwise()<=b.min()).all() && (b.max().cwise()<=m_max).all(); }
+
+  /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
+  inline AlignedBox& extend(const VectorType& p)
+  { m_min = m_min.cwise().min(p); m_max = m_max.cwise().max(p); return *this; }
+
+  /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */
+  inline AlignedBox& extend(const AlignedBox& b)
+  { m_min = m_min.cwise().min(b.m_min); m_max = m_max.cwise().max(b.m_max); return *this; }
+
+  /** Clamps \c *this by the box \a b and returns a reference to \c *this. */
+  inline AlignedBox& clamp(const AlignedBox& b)
+  { m_min = m_min.cwise().max(b.m_min); m_max = m_max.cwise().min(b.m_max); return *this; }
+
+  /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
+  inline AlignedBox& translate(const VectorType& t)
+  { m_min += t; m_max += t; return *this; }
+
+  /** \returns the squared distance between the point \a p and the box \c *this,
+    * and zero if \a p is inside the box.
+    * \sa exteriorDistance()
+    */
+  inline Scalar squaredExteriorDistance(const VectorType& p) const;
+
+  /** \returns the distance between the point \a p and the box \c *this,
+    * and zero if \a p is inside the box.
+    * \sa squaredExteriorDistance()
+    */
+  inline Scalar exteriorDistance(const VectorType& p) const
+  { return ei_sqrt(squaredExteriorDistance(p)); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<AlignedBox,
+           AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
+  {
+    return typename ei_cast_return_type<AlignedBox,
+                    AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
+  {
+    m_min = other.min().template cast<Scalar>();
+    m_max = other.max().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const AlignedBox& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); }
+
+protected:
+
+  VectorType m_min, m_max;
+};
+
+template<typename Scalar,int AmbiantDim>
+inline Scalar AlignedBox<Scalar,AmbiantDim>::squaredExteriorDistance(const VectorType& p) const
+{
+  Scalar dist2 = 0.;
+  Scalar aux;
+  for (int k=0; k<dim(); ++k)
+  {
+    if ((aux = (p[k]-m_min[k]))<0.)
+      dist2 += aux*aux;
+    else if ( (aux = (m_max[k]-p[k]))<0. )
+      dist2 += aux*aux;
+  }
+  return dist2;
+}
+
+#endif // EIGEN_ALIGNEDBOX_H

Eigen/src/Geometry/AngleAxis.h

@@ -47,7 +47,7 @@
   * \note This class is not aimed to be used to store a rotation transformation,
   * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
   * and transformation objects.
-  * 
+  *
   * \sa class Quaternion, class Transform, MatrixBase::UnitX()
   */
 
@@ -64,7 +64,7 @@ class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3>
 public:
 
   using Base::operator*;
-  
+
   enum { Dim = 3 };
   /** the scalar type of the coefficients */
   typedef _Scalar Scalar;
@@ -132,6 +132,30 @@ public:
   template<typename Derived>
   AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
   Matrix3 toRotationMatrix(void) const;
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const
+  { return typename ei_cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
+  {
+    m_axis = other.axis().template cast<Scalar>();
+    m_angle = other.angle();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const AngleAxis& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_axis.isApprox(other.m_axis, prec) && ei_isApprox(m_angle,other.m_angle, prec); }
 };
 
 /** \ingroup GeometryModule
@@ -147,7 +171,7 @@ typedef AngleAxis<double> AngleAxisd;
 template<typename Scalar>
 AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionType& q)
 {
-  Scalar n2 = q.vec().norm2();
+  Scalar n2 = q.vec().squaredNorm();
   if (n2 < precision<Scalar>()*precision<Scalar>())
   {
     m_angle = 0;

Eigen/src/Geometry/EulerAngles.h

@@ -0,0 +1,96 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_EULERANGLES_H
+#define EIGEN_EULERANGLES_H
+
+/** \geometry_module \ingroup GeometryModule
+  * \nonstableyet
+  *
+  * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2)
+  *
+  * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}.
+  * For instance, in:
+  * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode
+  * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that
+  * we have the following equality:
+  * \code
+  * mat == AngleAxisf(ea[0], Vector3f::UnitZ())
+  *      * AngleAxisf(ea[1], Vector3f::UnitX())
+  *      * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
+  * This corresponds to the right-multiply conventions (with right hand side frames).
+  */
+template<typename Derived>
+inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
+MatrixBase<Derived>::eulerAngles(int a0, int a1, int a2) const
+{
+  /* Implemented from Graphics Gems IV */
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived,3,3)
+
+  Matrix<Scalar,3,1> res;
+  typedef Matrix<typename Derived::Scalar,2,1> Vector2;
+  const Scalar epsilon = precision<Scalar>();
+
+  const int odd = ((a0+1)%3 == a1) ? 0 : 1;
+  const int i = a0;
+  const int j = (a0 + 1 + odd)%3;
+  const int k = (a0 + 2 - odd)%3;
+
+  if (a0==a2)
+  {
+    Scalar s = Vector2(coeff(j,i) , coeff(k,i)).norm();
+    res[1] = std::atan2(s, coeff(i,i));
+    if (s > epsilon)
+    {
+      res[0] = std::atan2(coeff(j,i), coeff(k,i));
+      res[2] = std::atan2(coeff(i,j),-coeff(i,k));
+    }
+    else
+    {
+      res[0] = Scalar(0);
+      res[2] = (coeff(i,i)>0?1:-1)*std::atan2(-coeff(k,j), coeff(j,j));
+    }
+  }
+  else
+  {
+    Scalar c = Vector2(coeff(i,i) , coeff(i,j)).norm();
+    res[1] = std::atan2(-coeff(i,k), c);
+    if (c > epsilon)
+    {
+      res[0] = std::atan2(coeff(j,k), coeff(k,k));
+      res[2] = std::atan2(coeff(i,j), coeff(i,i));
+    }
+    else
+    {
+      res[0] = Scalar(0);
+      res[2] = (coeff(i,k)>0?1:-1)*std::atan2(-coeff(k,j), coeff(j,j));
+    }
+  }
+  if (!odd)
+    res = -res;
+  return res;
+}
+
+
+#endif // EIGEN_EULERANGLES_H

Eigen/src/Geometry/Hyperplane.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -45,177 +45,225 @@
   */
 template <typename _Scalar, int _AmbientDim>
 class Hyperplane
-  #ifdef EIGEN_VECTORIZE
   : public ei_with_aligned_operator_new<_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1>
-  #endif
 {
-  public:
+public:
 
-    enum { AmbientDimAtCompileTime = _AmbientDim };
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
-    typedef Matrix<Scalar,AmbientDimAtCompileTime==Dynamic
-                          ? Dynamic
-                          : AmbientDimAtCompileTime+1,1> Coefficients;
-    typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
+  enum { AmbientDimAtCompileTime = _AmbientDim };
+  typedef _Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
+  typedef Matrix<Scalar,AmbientDimAtCompileTime==Dynamic
+                        ? Dynamic
+                        : AmbientDimAtCompileTime+1,1> Coefficients;
+  typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
 
-    /** Default constructor without initialization */
-    inline explicit Hyperplane(int _dim = AmbientDimAtCompileTime) : m_coeffs(_dim+1) {}
-    
-    /** Construct a plane from its normal \a n and a point \a e onto the plane.
-      * \warning the vector normal is assumed to be normalized.
-      */
-    inline Hyperplane(const VectorType& n, const VectorType e)
-      : m_coeffs(n.size()+1)
-    {
-      normal() = n;
-      offset() = -e.dot(n);
+  /** Default constructor without initialization */
+  inline explicit Hyperplane() {}
+
+  /** Constructs a dynamic-size hyperplane with \a _dim the dimension
+    * of the ambient space */
+  inline explicit Hyperplane(int _dim) : m_coeffs(_dim+1) {}
+
+  /** Construct a plane from its normal \a n and a point \a e onto the plane.
+    * \warning the vector normal is assumed to be normalized.
+    */
+  inline Hyperplane(const VectorType& n, const VectorType& e)
+    : m_coeffs(n.size()+1)
+  {
+    normal() = n;
+    offset() = -e.dot(n);
+  }
+
+  /** Constructs a plane from its normal \a n and distance to the origin \a d
+    * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
+    * \warning the vector normal is assumed to be normalized.
+    */
+  inline Hyperplane(const VectorType& n, Scalar d)
+    : m_coeffs(n.size()+1)
+  {
+    normal() = n;
+    offset() = d;
+  }
+
+  /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
+    * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
+    */
+  static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
+  {
+    Hyperplane result(p0.size());
+    result.normal() = (p1 - p0).unitOrthogonal();
+    result.offset() = -result.normal().dot(p0);
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
+    * is required to be exactly 3.
+    */
+  static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
+    Hyperplane result(p0.size());
+    result.normal() = (p2 - p0).cross(p1 - p0).normalized();
+    result.offset() = -result.normal().dot(p0);
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the parametrized line \a parametrized.
+    * If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
+    * so an arbitrary choice is made.
+    */
+  // FIXME to be consitent with the rest this could be implemented as a static Through function ??
+  explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
+  {
+    normal() = parametrized.direction().unitOrthogonal();
+    offset() = -normal().dot(parametrized.origin());
+  }
+
+  ~Hyperplane() {}
+
+  /** \returns the dimension in which the plane holds */
+  inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : AmbientDimAtCompileTime; }
+
+  /** normalizes \c *this */
+  void normalize(void)
+  {
+    m_coeffs /= normal().norm();
+  }
+
+  /** \returns the signed distance between the plane \c *this and a point \a p.
+    * \sa absDistance()
+    */
+  inline Scalar signedDistance(const VectorType& p) const { return p.dot(normal()) + offset(); }
+
+  /** \returns the absolute distance between the plane \c *this and a point \a p.
+    * \sa signedDistance()
+    */
+  inline Scalar absDistance(const VectorType& p) const { return ei_abs(signedDistance(p)); }
+
+  /** \returns the projection of a point \a p onto the plane \c *this.
+    */
+  inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
+
+  /** \returns a constant reference to the unit normal vector of the plane, which corresponds
+    * to the linear part of the implicit equation.
+    */
+  inline const NormalReturnType normal() const { return NormalReturnType(m_coeffs,0,0,dim(),1); }
+
+  /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
+    * to the linear part of the implicit equation.
+    */
+  inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
+
+  /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
+    * \warning the vector normal is assumed to be normalized.
+    */
+  inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
+
+  /** \returns a non-constant reference to the distance to the origin, which is also the constant part
+    * of the implicit equation */
+  inline Scalar& offset() { return m_coeffs(dim()); }
+
+  /** \returns a constant reference to the coefficients c_i of the plane equation:
+    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+    */
+  inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  /** \returns a non-constant reference to the coefficients c_i of the plane equation:
+    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+    */
+  inline Coefficients& coeffs() { return m_coeffs; }
+
+  /** \returns the intersection of *this with \a other.
+    *
+    * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
+    *
+    * \note If \a other is approximately parallel to *this, this method will return any point on *this.
+    */
+  VectorType intersection(const Hyperplane& other)
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
+    Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
+    // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
+    // whether the two lines are approximately parallel.
+    if(ei_isMuchSmallerThan(det, Scalar(1)))
+    {   // special case where the two lines are approximately parallel. Pick any point on the first line.
+        if(ei_abs(coeffs().coeff(1))>ei_abs(coeffs().coeff(0)))
+            return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
+        else
+            return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
     }
-    
-    /** Constructs a plane from its normal \a n and distance to the origin \a d.
-      * \warning the vector normal is assumed to be normalized.
-      */
-    inline Hyperplane(const VectorType& n, Scalar d)
-      : m_coeffs(n.size()+1)
-    {
-      normal() = n;
-      offset() = d;
+    else
+    {   // general case
+        Scalar invdet = Scalar(1) / det;
+        return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
+                          invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
     }
+  }
 
-    /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
-      * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
-      */
-    static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
+  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
+    *
+    * \param mat the Dim x Dim transformation matrix
+    * \param traits specifies whether the matrix \a mat represents an Isometry
+    *               or a more generic Affine transformation. The default is Affine.
+    */
+  template<typename XprType>
+  inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
+  {
+    if (traits==Affine)
+      normal() = mat.inverse().transpose() * normal();
+    else if (traits==Isometry)
+      normal() = mat * normal();
+    else
     {
-      Hyperplane result(p0.size());
-      result.normal() = (p1 - p0).unitOrthogonal();
-      result.offset() = -result.normal().dot(p0);
-      return result;
+      ei_assert("invalid traits value in Hyperplane::transform()");
     }
+    return *this;
+  }
 
-    /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
-      * is required to be exactly 3.
-      */
-    static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3);
-      Hyperplane result(p0.size());
-      result.normal() = (p2 - p0).cross(p1 - p0).normalized();
-      result.offset() = -result.normal().dot(p0);
-      return result;
-    }
+  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
+    *
+    * \param t the transformation of dimension Dim
+    * \param traits specifies whether the transformation \a t represents an Isometry
+    *               or a more generic Affine transformation. The default is Affine.
+    *               Other kind of transformations are not supported.
+    */
+  inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime>& t,
+                                TransformTraits traits = Affine)
+  {
+    transform(t.linear(), traits);
+    offset() -= t.translation().dot(normal());
+    return *this;
+  }
 
-    Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
-    {
-      normal() = parametrized.direction().unitOrthogonal();
-      offset() = -normal().dot(parametrized.origin());
-    }
-    
-    ~Hyperplane() {}
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Hyperplane,
+           Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
+  {
+    return typename ei_cast_return_type<Hyperplane,
+                    Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
+  }
 
-    /** \returns the dimension in which the plane holds */
-    inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : AmbientDimAtCompileTime; }
-    
-    /** normalizes \c *this */
-    void normalize(void)
-    {
-      m_coeffs /= normal().norm();
-    }
-    
-    /** \returns the signed distance between the plane \c *this and a point \a p.
-      */
-    inline Scalar signedDistance(const VectorType& p) const { return p.dot(normal()) + offset(); }
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime>& other)
+  { m_coeffs = other.coeffs().template cast<Scalar>(); }
 
-    /** \returns the absolute distance between the plane \c *this and a point \a p.
-      */
-    inline Scalar absDistance(const VectorType& p) const { return ei_abs(signedDistance(p)); }
-    
-    /** \returns the projection of a point \a p onto the plane \c *this.
-      */
-    inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
-
-    /** \returns a constant reference to the unit normal vector of the plane, which corresponds
-      * to the linear part of the implicit equation.
-      */
-    inline const NormalReturnType normal() const { return NormalReturnType(m_coeffs,0,0,dim(),1); }
-
-    /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
-      * to the linear part of the implicit equation.
-      */
-    inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
-
-    /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
-      * \warning the vector normal is assumed to be normalized.
-      */
-    inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
-
-    /** \returns a non-constant reference to the distance to the origin, which is also the constant part
-      * of the implicit equation */
-    inline Scalar& offset() { return m_coeffs(dim()); }
-    
-    /** \returns a constant reference to the coefficients c_i of the plane equation:
-      * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-      */
-    inline const Coefficients& coeffs() const { return m_coeffs; }
-
-    /** \returns a non-constant reference to the coefficients c_i of the plane equation:
-      * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-      */
-    inline Coefficients& coeffs() { return m_coeffs; }
-
-    /** \returns the intersection of *this with \a other.
-      *
-      * \warning The ambient space must be a plane, i.e. have dimension 2, so that *this and \a other are lines.
-      *
-      * \note If \a other is approximately parallel to *this, this method will return any point on *this.
-      */
-    VectorType intersection(const Hyperplane& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2);
-      Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
-      // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
-      // whether the two lines are approximately parallel.
-      if(ei_isMuchSmallerThan(det, Scalar(1)))
-      {   // special case where the two lines are approximately parallel. Pick any point on the first line.
-          if(ei_abs(coeffs().coeff(1))>ei_abs(coeffs().coeff(0)))
-              return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
-          else
-              return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
-      }
-      else
-      {   // general case
-          Scalar invdet = Scalar(1) / det;
-          return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
-                            invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
-      }
-    }
-    
-    template<typename XprType>
-    inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
-    {
-      if (traits==Affine)
-        normal() = mat.inverse().transpose() * normal();
-      else if (traits==Isometry)
-        normal() = mat * normal();
-      else
-      {
-        ei_assert("invalid traits value in Hyperplane::transform()");
-      }
-      return *this;
-    }
-
-    inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime>& t,
-                                 TransformTraits traits = Affine)
-    {
-      transform(t.linear(), traits);
-      offset() -= t.translation().dot(normal());
-      return *this;
-    }
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Hyperplane& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
 
 protected:
 
-    Coefficients m_coeffs;
+  Coefficients m_coeffs;
 };
 
 #endif // EIGEN_HYPERPLANE_H

Eigen/src/Geometry/OrthoMethods.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -27,19 +27,23 @@
 #define EIGEN_ORTHOMETHODS_H
 
 /** \geometry_module
-  * \returns the cross product of \c *this and \a other */
+  *
+  * \returns the cross product of \c *this and \a other
+  *
+  * Here is a very good explanation of cross-product: http://xkcd.com/199/
+  */
 template<typename Derived>
 template<typename OtherDerived>
-inline typename MatrixBase<Derived>::EvalType
+inline typename MatrixBase<Derived>::PlainMatrixType
 MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3);
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3)
 
   // Note that there is no need for an expression here since the compiler
   // optimize such a small temporary very well (even within a complex expression)
   const typename ei_nested<Derived,2>::type lhs(derived());
   const typename ei_nested<OtherDerived,2>::type rhs(other.derived());
-  return typename ei_eval<Derived>::type(
+  return typename ei_plain_matrix_type<Derived>::type(
     lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1),
     lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2),
     lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)
@@ -49,7 +53,7 @@ MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
 template<typename Derived, int Size = Derived::SizeAtCompileTime>
 struct ei_unitOrthogonal_selector
 {
-  typedef typename ei_eval<Derived>::type VectorType;
+  typedef typename ei_plain_matrix_type<Derived>::type VectorType;
   typedef typename ei_traits<Derived>::Scalar Scalar;
   typedef typename NumTraits<Scalar>::Real RealScalar;
   inline static VectorType run(const Derived& src)
@@ -92,7 +96,7 @@ struct ei_unitOrthogonal_selector
 template<typename Derived>
 struct ei_unitOrthogonal_selector<Derived,2>
 {
-  typedef typename ei_eval<Derived>::type VectorType;
+  typedef typename ei_plain_matrix_type<Derived>::type VectorType;
   inline static VectorType run(const Derived& src)
   { return VectorType(-ei_conj(src.y()), ei_conj(src.x())).normalized(); }
 };
@@ -105,10 +109,10 @@ struct ei_unitOrthogonal_selector<Derived,2>
   * \sa cross()
   */
 template<typename Derived>
-typename MatrixBase<Derived>::EvalType
+typename MatrixBase<Derived>::PlainMatrixType
 MatrixBase<Derived>::unitOrthogonal() const
 {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   return ei_unitOrthogonal_selector<Derived>::run(derived());
 }
 

Eigen/src/Geometry/ParametrizedLine.h

@@ -2,7 +2,7 @@
 // for linear algebra. Eigen itself is part of the KDE project.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -32,81 +32,119 @@
   *
   * \brief A parametrized line
   *
+  * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
+  * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
+  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ l \in \mathbf{R} \f$.
+  *
   * \param _Scalar the scalar type, i.e., the type of the coefficients
   * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  *             Notice that the dimension of the hyperplane is _AmbientDim-1.
   */
 template <typename _Scalar, int _AmbientDim>
 class ParametrizedLine
-  #ifdef EIGEN_VECTORIZE
   : public ei_with_aligned_operator_new<_Scalar,_AmbientDim>
-  #endif
 {
-  public:
+public:
 
-    enum { AmbientDimAtCompileTime = _AmbientDim };
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
+  enum { AmbientDimAtCompileTime = _AmbientDim };
+  typedef _Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
 
-    /** Default constructor without initialization */
-    inline explicit ParametrizedLine(int _dim = AmbientDimAtCompileTime)
-      : m_origin(_dim), m_direction(_dim)
-    {}
-    
-    ParametrizedLine(const VectorType& origin, const VectorType& direction)
-      : m_origin(origin), m_direction(direction) {}
-    explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
+  /** Default constructor without initialization */
+  inline explicit ParametrizedLine() {}
 
-    ~ParametrizedLine() {}
+  /** Constructs a dynamic-size line with \a _dim the dimension
+    * of the ambient space */
+  inline explicit ParametrizedLine(int _dim) : m_origin(_dim), m_direction(_dim) {}
 
-    /** \returns the dimension in which the line holds */
-    inline int dim() const { return m_direction.size(); }
+  /** Initializes a parametrized line of direction \a direction and origin \a origin.
+    * \warning the vector direction is assumed to be normalized.
+    */
+  ParametrizedLine(const VectorType& origin, const VectorType& direction)
+    : m_origin(origin), m_direction(direction) {}
 
-    const VectorType& origin() const { return m_origin; }
-    VectorType& origin() { return m_origin; }
+  explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
 
-    const VectorType& direction() const { return m_direction; }
-    VectorType& direction() { return m_direction; }
+  /** Constructs a parametrized line going from \a p0 to \a p1. */
+  static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1)
+  { return ParametrizedLine(p0, (p1-p0).normalized()); }
 
-    /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
-      * \sa distance()
-      */
-    RealScalar squaredDistance(const VectorType& p) const
-    {
-      VectorType diff = p-origin();
-      return (diff - diff.dot(direction())* direction()).norm2();
-    }
-    /** \returns the distance of a point \a p to its projection onto the line \c *this.
-      * \sa squaredDistance()
-      */
-    RealScalar distance(const VectorType& p) const { return ei_sqrt(squaredDistance(p)); }
+  ~ParametrizedLine() {}
 
-    /** \returns the projection of a point \a p onto the line \c *this.
-      */
-    VectorType projection(const VectorType& p) const
-    { return origin() + (p-origin()).dot(direction()) * direction(); }
+  /** \returns the dimension in which the line holds */
+  inline int dim() const { return m_direction.size(); }
 
-    Scalar intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
+  const VectorType& origin() const { return m_origin; }
+  VectorType& origin() { return m_origin; }
 
-  protected:
+  const VectorType& direction() const { return m_direction; }
+  VectorType& direction() { return m_direction; }
 
-    VectorType m_origin, m_direction;
+  /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
+    * \sa distance()
+    */
+  RealScalar squaredDistance(const VectorType& p) const
+  {
+    VectorType diff = p-origin();
+    return (diff - diff.dot(direction())* direction()).squaredNorm();
+  }
+  /** \returns the distance of a point \a p to its projection onto the line \c *this.
+    * \sa squaredDistance()
+    */
+  RealScalar distance(const VectorType& p) const { return ei_sqrt(squaredDistance(p)); }
+
+  /** \returns the projection of a point \a p onto the line \c *this. */
+  VectorType projection(const VectorType& p) const
+  { return origin() + (p-origin()).dot(direction()) * direction(); }
+
+  Scalar intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane);
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<ParametrizedLine,
+           ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
+  {
+    return typename ei_cast_return_type<ParametrizedLine,
+                    ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime>& other)
+  {
+    m_origin = other.origin().template cast<Scalar>();
+    m_direction = other.direction().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const ParametrizedLine& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
+
+protected:
+
+  VectorType m_origin, m_direction;
 };
 
-/** Construct a parametrized line from a 2D hyperplane
+/** Constructs a parametrized line from a 2D hyperplane
   *
   * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
   */
 template <typename _Scalar, int _AmbientDim>
 inline ParametrizedLine<_Scalar, _AmbientDim>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2);
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
   direction() = hyperplane.normal().unitOrthogonal();
   origin() = -hyperplane.normal()*hyperplane.offset();
 }
 
-/** \returns the parameter value of the intersection between *this and the given hyperplane
+/** \returns the parameter value of the intersection between \c *this and the given hyperplane
   */
 template <typename _Scalar, int _AmbientDim>
 inline _Scalar ParametrizedLine<_Scalar, _AmbientDim>::intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane)

Eigen/src/Geometry/Quaternion.h

@@ -59,9 +59,7 @@ template<typename _Scalar> struct ei_traits<Quaternion<_Scalar> >
 
 template<typename _Scalar>
 class Quaternion : public RotationBase<Quaternion<_Scalar>,3>
-  #ifdef EIGEN_VECTORIZE
   , public ei_with_aligned_operator_new<_Scalar,4>
-  #endif
 {
   typedef RotationBase<Quaternion<_Scalar>,3> Base;
   typedef Matrix<_Scalar, 4, 1> Coefficients;
@@ -117,7 +115,7 @@ public:
 
   /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
     * its four coefficients \a w, \a x, \a y and \a z.
-    * 
+    *
     * \warning Note the order of the arguments: the real \a w coefficient first,
     * while internally the coefficients are stored in the following order:
     * [\c x, \c y, \c z, \c w]
@@ -154,21 +152,21 @@ public:
   inline Quaternion& setIdentity() { m_coeffs << 1, 0, 0, 0; return *this; }
 
   /** \returns the squared norm of the quaternion's coefficients
-    * \sa Quaternion::norm(), MatrixBase::norm2()
+    * \sa Quaternion::norm(), MatrixBase::squaredNorm()
     */
-  inline Scalar norm2() const { return m_coeffs.norm2(); }
+  inline Scalar squaredNorm() const { return m_coeffs.squaredNorm(); }
 
   /** \returns the norm of the quaternion's coefficients
-    * \sa Quaternion::norm2(), MatrixBase::norm()
+    * \sa Quaternion::squaredNorm(), MatrixBase::norm()
     */
   inline Scalar norm() const { return m_coeffs.norm(); }
-  
-  /** Normalizes the quaternion \c *this 
+
+  /** Normalizes the quaternion \c *this
     * \sa normalized(), MatrixBase::normalize() */
-  inline void normalize() { m_coeffs.normalize(); } 
+  inline void normalize() { m_coeffs.normalize(); }
   /** \returns a normalized version of \c *this
     * \sa normalize(), MatrixBase::normalized() */
-  inline Quaternion normalized() const { Quaternion(m_coeffs.normalized()); } 
+  inline Quaternion normalized() const { return Quaternion(m_coeffs.normalized()); }
 
   /** \returns the dot product of \c *this and \a other
     * Geometrically speaking, the dot product of two unit quaternions
@@ -195,6 +193,27 @@ public:
   template<typename Derived>
   Vector3 operator* (const MatrixBase<Derived>& vec) const;
 
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Quaternion,Quaternion<NewScalarType> >::type cast() const
+  { return typename ei_cast_return_type<Quaternion,Quaternion<NewScalarType> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Quaternion(const Quaternion<OtherScalarType>& other)
+  { m_coeffs = other.coeffs().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Quaternion& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
+
 };
 
 /** \ingroup GeometryModule
@@ -258,7 +277,7 @@ inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const Quaternion& other
 template<typename Scalar>
 inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const AngleAxisType& aa)
 {
-  Scalar ha = 0.5*aa.angle();
+  Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings
   this->w() = ei_cos(ha);
   this->vec() = ei_sin(ha) * aa.axis();
   return *this;
@@ -288,18 +307,18 @@ Quaternion<Scalar>::toRotationMatrix(void) const
   // it has to be inlined, and so the return by value is not an issue
   Matrix3 res;
 
-  Scalar tx  = 2*this->x();
-  Scalar ty  = 2*this->y();
-  Scalar tz  = 2*this->z();
-  Scalar twx = tx*this->w();
-  Scalar twy = ty*this->w();
-  Scalar twz = tz*this->w();
-  Scalar txx = tx*this->x();
-  Scalar txy = ty*this->x();
-  Scalar txz = tz*this->x();
-  Scalar tyy = ty*this->y();
-  Scalar tyz = tz*this->y();
-  Scalar tzz = tz*this->z();
+  const Scalar tx  = 2*this->x();
+  const Scalar ty  = 2*this->y();
+  const Scalar tz  = 2*this->z();
+  const Scalar twx = tx*this->w();
+  const Scalar twy = ty*this->w();
+  const Scalar twz = tz*this->w();
+  const Scalar txx = tx*this->x();
+  const Scalar txy = ty*this->x();
+  const Scalar txz = tz*this->x();
+  const Scalar tyy = ty*this->y();
+  const Scalar tyz = tz*this->y();
+  const Scalar tzz = tz*this->z();
 
   res.coeffRef(0,0) = 1-(tyy+tzz);
   res.coeffRef(0,1) = txy-twz;
@@ -314,9 +333,11 @@ Quaternion<Scalar>::toRotationMatrix(void) const
   return res;
 }
 
-/** Makes a quaternion representing the rotation between two vectors \a a and \a b.
-  * \returns a reference to the actual quaternion
-  * Note that the two input vectors have \b not to be normalized.
+/** Sets *this to be a quaternion representing a rotation sending the vector \a a to the vector \a b.
+  *
+  * \returns a reference to *this.
+  *
+  * Note that the two input vectors do \b not have to be normalized.
   */
 template<typename Scalar>
 template<typename Derived1, typename Derived2>
@@ -334,9 +355,9 @@ inline Quaternion<Scalar>& Quaternion<Scalar>::setFromTwoVectors(const MatrixBas
     this->w() = 1; this->vec().setZero();
   }
   Scalar s = ei_sqrt((1+c)*2);
-  Scalar invs = 1./s;
+  Scalar invs = Scalar(1)/s;
   this->vec() = axis * invs;
-  this->w() = s * 0.5;
+  this->w() = s * Scalar(0.5);
 
   return *this;
 }
@@ -351,7 +372,7 @@ template <typename Scalar>
 inline Quaternion<Scalar> Quaternion<Scalar>::inverse() const
 {
   // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
-  Scalar n2 = this->norm2();
+  Scalar n2 = this->squaredNorm();
   if (n2 > 0)
     return Quaternion(conjugate().coeffs() / n2);
   else
@@ -382,7 +403,7 @@ inline Scalar Quaternion<Scalar>::angularDistance(const Quaternion& other) const
   double d = ei_abs(this->dot(other));
   if (d>=1.0)
     return 0;
-  return 2.0 * std::acos(d);
+  return Scalar(2) * std::acos(d);
 }
 
 /** \returns the spherical linear interpolation between the two quaternions
@@ -439,8 +460,8 @@ struct ei_quaternion_assign_impl<Other,3,3>
       int k = (j+1)%3;
 
       t = ei_sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + 1.0);
-      q.coeffs().coeffRef(i) = 0.5 * t;
-      t = 0.5/t;
+      q.coeffs().coeffRef(i) = Scalar(0.5) * t;
+      t = Scalar(0.5)/t;
       q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;
       q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t;
       q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t;

Eigen/src/Geometry/Rotation2D.h

@@ -100,6 +100,29 @@ public:
     */
   inline Rotation2D slerp(Scalar t, const Rotation2D& other) const
   { return m_angle * (1-t) + other.angle() * t; }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const
+  { return typename ei_cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
+  {
+    m_angle = other.angle();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Rotation2D& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return ei_isApprox(m_angle,other.m_angle, prec); }
 };
 
 /** \ingroup GeometryModule
@@ -117,7 +140,7 @@ template<typename Scalar>
 template<typename Derived>
 Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
 {
-  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
   m_angle = ei_atan2(mat.coeff(1,0), mat.coeff(0,0));
   return *this;
 }

Eigen/src/Geometry/RotationBase.h

@@ -77,7 +77,7 @@ template<typename OtherDerived>
 Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
 ::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
 {
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim));
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
   *this = r.toRotationMatrix();
 }
 
@@ -91,7 +91,7 @@ Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>&
 Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
 ::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
 {
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim));
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
   return *this = r.toRotationMatrix();
 }
 
@@ -116,7 +116,7 @@ Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
 template<typename Scalar, int Dim>
 inline static Matrix<Scalar,2,2> ei_toRotationMatrix(const Scalar& s)
 {
-  EIGEN_STATIC_ASSERT(Dim==2,you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
   return Rotation2D<Scalar>(s).toRotationMatrix();
 }
 
@@ -130,7 +130,7 @@ template<typename Scalar, int Dim, typename OtherDerived>
 inline static const MatrixBase<OtherDerived>& ei_toRotationMatrix(const MatrixBase<OtherDerived>& mat)
 {
   EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim,
-    you_did_a_programming_error);
+    YOU_MADE_A_PROGRAMMING_MISTAKE)
   return mat;
 }
 

Eigen/src/Geometry/Scaling.h

@@ -35,15 +35,13 @@
   * \param _Dim the  dimension of the space, can be a compile time value or Dynamic
   *
   * \note This class is not aimed to be used to store a scaling transformation,
-  * but rather to make easier the constructions and updates of Transformation object.
+  * but rather to make easier the constructions and updates of Transform objects.
   *
   * \sa class Translation, class Transform
   */
 template<typename _Scalar, int _Dim>
 class Scaling
-  #ifdef EIGEN_VECTORIZE
   : public ei_with_aligned_operator_new<_Scalar,_Dim>
-  #endif
 {
 public:
   /** dimension of the space */
@@ -128,6 +126,27 @@ public:
     return *this;
   }
 
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type cast() const
+  { return typename ei_cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Scaling(const Scaling<OtherScalarType,Dim>& other)
+  { m_coeffs = other.coeffs().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Scaling& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
+
 };
 
 /** \addtogroup GeometryModule */

Eigen/src/Geometry/Transform.h

@@ -61,9 +61,7 @@ struct ei_transform_product_impl;
   */
 template<typename _Scalar, int _Dim>
 class Transform
-  #ifdef EIGEN_VECTORIZE
   : public ei_with_aligned_operator_new<_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1)>
-  #endif
 {
 public:
 
@@ -100,10 +98,15 @@ public:
   inline Transform(const Transform& other)
   { m_matrix = other.m_matrix; }
 
+  inline explicit Transform(const TranslationType& t) { *this = t; }
+  inline explicit Transform(const ScalingType& s) { *this = s; }
+  template<typename Derived>
+  inline explicit Transform(const RotationBase<Derived, Dim>& r) { *this = r; }
+
   inline Transform& operator=(const Transform& other)
   { m_matrix = other.m_matrix; return *this; }
 
-  template<typename OtherDerived, bool select = OtherDerived::RowsAtCompileTime == Dim>
+  template<typename OtherDerived, bool BigMatrix> // MSVC 2005 will commit suicide if BigMatrix has a default value
   struct construct_from_matrix
   {
     static inline void run(Transform *transform, const MatrixBase<OtherDerived>& other)
@@ -127,7 +130,7 @@ public:
   template<typename OtherDerived>
   inline explicit Transform(const MatrixBase<OtherDerived>& other)
   {
-    construct_from_matrix<OtherDerived>::run(this, other);
+    construct_from_matrix<OtherDerived, int(OtherDerived::RowsAtCompileTime) == Dim>::run(this, other);
   }
 
   /** Set \c *this from a (Dim+1)^2 matrix. */
@@ -179,10 +182,17 @@ public:
   operator * (const MatrixBase<OtherDerived> &other) const
   { return ei_transform_product_impl<OtherDerived,Dim,HDim>::run(*this,other.derived()); }
 
+  /** \returns the product expression of a transformation matrix \a a times a transform \a b
+    * The transformation matrix \a a must have a Dim+1 x Dim+1 sizes. */
+  template<typename OtherDerived>
+  friend inline const typename ProductReturnType<OtherDerived,MatrixType>::Type
+  operator * (const MatrixBase<OtherDerived> &a, const Transform &b)
+  { return a.derived() * b.matrix(); }
+
   /** Contatenates two transformations */
-  inline const typename ProductReturnType<MatrixType,MatrixType>::Type
+  inline const Transform
   operator * (const Transform& other) const
-  { return m_matrix * other.matrix(); }
+  { return Transform(m_matrix * other.matrix()); }
 
   /** \sa MatrixBase::setIdentity() */
   void setIdentity() { m_matrix.setIdentity(); }
@@ -226,14 +236,15 @@ public:
     return res;
   }
 
-//   template<typename Derived>
-//   inline Transform& operator=(const Rotation<Derived,Dim>& t);
+  template<typename Derived>
+  inline Transform& operator=(const RotationBase<Derived,Dim>& r);
   template<typename Derived>
   inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); }
   template<typename Derived>
   inline Transform operator*(const RotationBase<Derived,Dim>& r) const;
 
-  LinearMatrixType extractRotation(TransformTraits traits = Affine) const;
+  EIGEN_DEPRECATED LinearMatrixType extractRotation(TransformTraits traits = Affine) const { return rotation(traits); }
+  LinearMatrixType rotation(TransformTraits traits = Affine) const;
 
   template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
   Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
@@ -241,9 +252,32 @@ public:
 
   inline const MatrixType inverse(TransformTraits traits = Affine) const;
 
+  /** \returns a const pointer to the column major internal matrix */
   const Scalar* data() const { return m_matrix.data(); }
+  /** \returns a non-const pointer to the column major internal matrix */
   Scalar* data() { return m_matrix.data(); }
 
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Transform,Transform<NewScalarType,Dim> >::type cast() const
+  { return typename ei_cast_return_type<Transform,Transform<NewScalarType,Dim> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Transform(const Transform<OtherScalarType,Dim>& other)
+  { m_matrix = other.matrix().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Transform& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_matrix.isApprox(other.m_matrix, prec); }
+
 protected:
 
 };
@@ -279,7 +313,7 @@ Transform<Scalar,Dim>::Transform(const QMatrix& other)
 template<typename Scalar, int Dim>
 Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QMatrix& other)
 {
-  EIGEN_STATIC_ASSERT(Dim==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   m_matrix << other.m11(), other.m21(), other.dx(),
               other.m12(), other.m22(), other.dy(),
               0, 0, 1;
@@ -295,7 +329,7 @@ Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QMatrix& other)
 template<typename Scalar, int Dim>
 QMatrix Transform<Scalar,Dim>::toQMatrix(void) const
 {
-  EIGEN_STATIC_ASSERT(Dim==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   return QMatrix(other.coeffRef(0,0), other.coeffRef(1,0),
                  other.coeffRef(0,1), other.coeffRef(1,1),
                  other.coeffRef(0,2), other.coeffRef(1,2));
@@ -318,7 +352,7 @@ Transform<Scalar,Dim>::Transform(const QTransform& other)
 template<typename Scalar, int Dim>
 Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QTransform& other)
 {
-  EIGEN_STATIC_ASSERT(Dim==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   m_matrix << other.m11(), other.m21(), other.dx(),
               other.m12(), other.m22(), other.dy(),
               other.m13(), other.m23(), other.m33();
@@ -332,7 +366,7 @@ Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QTransform& other)
 template<typename Scalar, int Dim>
 QMatrix Transform<Scalar,Dim>::toQTransform(void) const
 {
-  EIGEN_STATIC_ASSERT(Dim==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   return QTransform(other.coeffRef(0,0), other.coeffRef(1,0), other.coeffRef(2,0)
                     other.coeffRef(0,1), other.coeffRef(1,1), other.coeffRef(2,1)
                     other.coeffRef(0,2), other.coeffRef(1,2), other.coeffRef(2,2);
@@ -352,7 +386,7 @@ template<typename OtherDerived>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::scale(const MatrixBase<OtherDerived> &other)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim));
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
   linear() = (linear() * other.asDiagonal()).lazy();
   return *this;
 }
@@ -377,7 +411,7 @@ template<typename OtherDerived>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::prescale(const MatrixBase<OtherDerived> &other)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim));
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
   m_matrix.template block<Dim,HDim>(0,0) = (other.asDiagonal() * m_matrix.template block<Dim,HDim>(0,0)).lazy();
   return *this;
 }
@@ -402,7 +436,7 @@ template<typename OtherDerived>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::translate(const MatrixBase<OtherDerived> &other)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim));
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
   translation() += linear() * other;
   return *this;
 }
@@ -416,7 +450,7 @@ template<typename OtherDerived>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::pretranslate(const MatrixBase<OtherDerived> &other)
 {
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim));
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
   translation() += other;
   return *this;
 }
@@ -473,7 +507,7 @@ template<typename Scalar, int Dim>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::shear(Scalar sx, Scalar sy)
 {
-  EIGEN_STATIC_ASSERT(int(Dim)==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   VectorType tmp = linear().col(0)*sy + linear().col(1);
   linear() << linear().col(0) + linear().col(1)*sx, tmp;
   return *this;
@@ -488,7 +522,7 @@ template<typename Scalar, int Dim>
 Transform<Scalar,Dim>&
 Transform<Scalar,Dim>::preshear(Scalar sx, Scalar sy)
 {
-  EIGEN_STATIC_ASSERT(int(Dim)==2, you_did_a_programming_error);
+  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
   return *this;
 }
@@ -500,8 +534,10 @@ Transform<Scalar,Dim>::preshear(Scalar sx, Scalar sy)
 template<typename Scalar, int Dim>
 inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const TranslationType& t)
 {
-  setIdentity();
+  linear().setIdentity();
   translation() = t.vector();
+  m_matrix.template block<1,Dim>(Dim,0).setZero();
+  m_matrix(Dim,Dim) = Scalar(1);
   return *this;
 }
 
@@ -518,7 +554,7 @@ inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const ScalingType
 {
   m_matrix.setZero();
   linear().diagonal() = s.coeffs();
-  m_matrix(Dim,Dim) = Scalar(1);
+  m_matrix.coeffRef(Dim,Dim) = Scalar(1);
   return *this;
 }
 
@@ -530,6 +566,17 @@ inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const ScalingType&
   return res;
 }
 
+template<typename Scalar, int Dim>
+template<typename Derived>
+inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const RotationBase<Derived,Dim>& r)
+{
+  linear() = ei_toRotationMatrix<Scalar,Dim>(r);
+  translation().setZero();
+  m_matrix.template block<1,Dim>(Dim,0).setZero();
+  m_matrix.coeffRef(Dim,Dim) = Scalar(1);
+  return *this;
+}
+
 template<typename Scalar, int Dim>
 template<typename Derived>
 inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const RotationBase<Derived,Dim>& r) const
@@ -559,7 +606,7 @@ inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const RotationBase
   */
 template<typename Scalar, int Dim>
 typename Transform<Scalar,Dim>::LinearMatrixType
-Transform<Scalar,Dim>::extractRotation(TransformTraits traits) const
+Transform<Scalar,Dim>::rotation(TransformTraits traits) const
 {
   ei_assert(traits!=Projective && "you cannot extract a rotation from a non affine transformation");
   if (traits == Affine)
@@ -569,7 +616,7 @@ Transform<Scalar,Dim>::extractRotation(TransformTraits traits) const
     LinearMatrixType matQ = qr.matrixQ();
     LinearMatrixType matR = qr.matrixR();
     for (int i=0 ; i<Dim; ++i)
-      if (matR(i,i)<0)
+      if (matR.coeff(i,i)<0)
         matQ.col(i) = -matQ.col(i);
     return matQ;
   }
@@ -594,8 +641,8 @@ Transform<Scalar,Dim>::fromPositionOrientationScale(const MatrixBase<PositionDer
   linear() = ei_toRotationMatrix<Scalar,Dim>(orientation);
   linear() *= scale.asDiagonal();
   translation() = position;
-  m_matrix(Dim,Dim) = 1.;
   m_matrix.template block<1,Dim>(Dim,0).setZero();
+  m_matrix(Dim,Dim) = Scalar(1);
   return *this;
 }
 

Eigen/src/Geometry/Translation.h

@@ -35,15 +35,13 @@
   * \param _Dim the  dimension of the space, can be a compile time value or Dynamic
   *
   * \note This class is not aimed to be used to store a translation transformation,
-  * but rather to make easier the constructions and updates of Transformation object.
+  * but rather to make easier the constructions and updates of Transform objects.
   *
   * \sa class Scaling, class Transform
   */
 template<typename _Scalar, int _Dim>
 class Translation
-  #ifdef EIGEN_VECTORIZE
   : public ei_with_aligned_operator_new<_Scalar,_Dim>
-  #endif
 {
 public:
   /** dimension of the space */
@@ -91,7 +89,7 @@ public:
   /** Concatenates two translation */
   inline Translation operator* (const Translation& other) const
   { return Translation(m_coeffs + other.m_coeffs); }
-  
+
   /** Concatenates a translation and a scaling */
   inline TransformType operator* (const ScalingType& other) const;
 
@@ -131,6 +129,27 @@ public:
     return *this;
   }
 
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline typename ei_cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const
+  { return typename ei_cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit Translation(const Translation<OtherScalarType,Dim>& other)
+  { m_coeffs = other.vector().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const Translation& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
+
 };
 
 /** \addtogroup GeometryModule */

Eigen/src/LU/Determinant.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public

Eigen/src/LU/Inverse.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -92,7 +92,7 @@ bool ei_compute_inverse_in_size4_case_helper(const MatrixType& matrix, MatrixTyp
     * R' = -S' * (R*P_inverse)
     */
   typedef Block<MatrixType,2,2> XprBlock22;
-  typedef typename XprBlock22::Eval Block22;
+  typedef typename MatrixBase<XprBlock22>::PlainMatrixType Block22;
   Block22 P_inverse;
   if(ei_compute_inverse_in_size2_case_with_check(matrix.template block<2,2>(0,0), &P_inverse))
   {
@@ -216,12 +216,11 @@ struct ei_compute_inverse<MatrixType, 4>
   * \sa inverse()
   */
 template<typename Derived>
-inline void MatrixBase<Derived>::computeInverse(EvalType *result) const
+inline void MatrixBase<Derived>::computeInverse(PlainMatrixType *result) const
 {
-  typedef typename ei_eval<Derived>::type MatrixType;
   ei_assert(rows() == cols());
-  EIGEN_STATIC_ASSERT(NumTraits<Scalar>::HasFloatingPoint,scalar_type_must_be_floating_point);
-  ei_compute_inverse<MatrixType>::run(eval(), result);
+  EIGEN_STATIC_ASSERT(NumTraits<Scalar>::HasFloatingPoint,NUMERIC_TYPE_MUST_BE_FLOATING_POINT)
+  ei_compute_inverse<PlainMatrixType>::run(eval(), result);
 }
 
 /** \lu_module
@@ -239,9 +238,9 @@ inline void MatrixBase<Derived>::computeInverse(EvalType *result) const
   * \sa computeInverse()
   */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::EvalType MatrixBase<Derived>::inverse() const
+inline const typename MatrixBase<Derived>::PlainMatrixType MatrixBase<Derived>::inverse() const
 {
-  EvalType result(rows(), cols());
+  PlainMatrixType result(rows(), cols());
   computeInverse(&result);
   return result;
 }

Eigen/src/LU/LU.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -35,8 +35,9 @@
   *
   * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A
   * is decomposed as A = PLUQ where L is unit-lower-triangular, U is upper-triangular, and P and Q
-  * are permutation matrices. This is a rank-revealing LU decomposition. The eigenvalues of U are
-  * in non-increasing order.
+  * are permutation matrices. This is a rank-revealing LU decomposition. The eigenvalues (diagonal
+  * coefficients) of U are sorted in such a way that any zeros are at the end, so that the rank
+  * of A is the index of the first zero on the diagonal of U (with indices starting at 0) if any.
   *
   * This decomposition provides the generic approach to solving systems of linear equations, computing
   * the rank, invertibility, inverse, kernel, and determinant.
@@ -71,9 +72,24 @@ template<typename MatrixType> class LU
              MatrixType::MaxRowsAtCompileTime)
     };
 
-  typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, Dynamic,
-                 MatrixType::Flags&RowMajorBit,
-                 MatrixType::MaxColsAtCompileTime, MaxSmallDimAtCompileTime> KernelResultType;
+    typedef Matrix<typename MatrixType::Scalar,
+                  MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix" is the number of cols of the original matrix
+                                                 // so that the product "matrix * kernel = zero" makes sense
+                  Dynamic,                       // we don't know at compile-time the dimension of the kernel
+                  MatrixType::Options,
+                  MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter
+                  MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space, whose dimension is the number
+                                                   // of columns of the original matrix
+    > KernelResultType;
+
+    typedef Matrix<typename MatrixType::Scalar,
+                   MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose dimension is the number
+                                                  // of rows of the original matrix
+                   Dynamic,                       // we don't know at compile time the dimension of the image (the rank)
+                   MatrixType::Options,
+                   MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix,
+                   MatrixType::MaxColsAtCompileTime  // so it has the same number of rows and at most as many columns.
+    > ImageResultType;
 
     /** Constructor.
       *
@@ -98,18 +114,16 @@ template<typename MatrixType> class LU
       *
       * \sa matrixLU(), matrixU()
       */
-    inline const Part<MatrixType, UnitLower> matrixL() const
+    inline const Part<MatrixType, UnitLowerTriangular> matrixL() const
     {
       return m_lu;
     }
 
     /** \returns an expression of the U matrix, i.e. the upper-triangular part of the LU matrix.
-      *
-      * \note The eigenvalues of U are sorted in non-increasing order.
       *
       * \sa matrixLU(), matrixL()
       */
-    inline const Part<MatrixType, Upper> matrixU() const
+    inline const Part<MatrixType, UpperTriangular> matrixU() const
     {
       return m_lu;
     }
@@ -136,10 +150,10 @@ template<typename MatrixType> class LU
       return m_q;
     }
 
-    /** Computes the kernel of the matrix.
+    /** Computes a basis of the kernel of the matrix, also called the null-space of the matrix.
       *
-      * \note: this method is only allowed on non-invertible matrices, as determined by
-      * isInvertible(). Calling it on an invertible matrice will make an assertion fail.
+      * \note This method is only allowed on non-invertible matrices, as determined by
+      * isInvertible(). Calling it on an invertible matrix will make an assertion fail.
       *
       * \param result a pointer to the matrix in which to store the kernel. The columns of this
       * matrix will be set to form a basis of the kernel (it will be resized
@@ -148,15 +162,32 @@ template<typename MatrixType> class LU
       * Example: \include LU_computeKernel.cpp
       * Output: \verbinclude LU_computeKernel.out
       *
-      * \sa kernel()
+      * \sa kernel(), computeImage(), image()
       */
-    void computeKernel(KernelResultType *result) const;
+    template<typename KernelMatrixType>
+    void computeKernel(KernelMatrixType *result) const;
 
-    /** \returns the kernel of the matrix. The columns of the returned matrix
+    /** Computes a basis of the image of the matrix, also called the column-space or range of he matrix.
+      *
+      * \note Calling this method on the zero matrix will make an assertion fail.
+      *
+      * \param result a pointer to the matrix in which to store the image. The columns of this
+      * matrix will be set to form a basis of the image (it will be resized
+      * if necessary).
+      *
+      * Example: \include LU_computeImage.cpp
+      * Output: \verbinclude LU_computeImage.out
+      *
+      * \sa image(), computeKernel(), kernel()
+      */
+    template<typename ImageMatrixType>
+    void computeImage(ImageMatrixType *result) const;
+
+    /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix
       * will form a basis of the kernel.
       *
       * \note: this method is only allowed on non-invertible matrices, as determined by
-      * isInvertible(). Calling it on an invertible matrice will make an assertion fail.
+      * isInvertible(). Calling it on an invertible matrix will make an assertion fail.
       *
       * \note: this method returns a matrix by value, which induces some inefficiency.
       *        If you prefer to avoid this overhead, use computeKernel() instead.
@@ -164,10 +195,25 @@ template<typename MatrixType> class LU
       * Example: \include LU_kernel.cpp
       * Output: \verbinclude LU_kernel.out
       *
-      * \sa computeKernel()
+      * \sa computeKernel(), image()
       */
     const KernelResultType kernel() const;
 
+    /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix
+      * will form a basis of the kernel.
+      *
+      * \note: Calling this method on the zero matrix will make an assertion fail.
+      *
+      * \note: this method returns a matrix by value, which induces some inefficiency.
+      *        If you prefer to avoid this overhead, use computeImage() instead.
+      *
+      * Example: \include LU_image.cpp
+      * Output: \verbinclude LU_image.out
+      *
+      * \sa computeImage(), kernel()
+      */
+    const ImageResultType image() const;
+
     /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
       * *this is the LU decomposition, if any exists.
       *
@@ -181,7 +227,8 @@ template<typename MatrixType> class LU
       * \returns true if any solution exists, false if no solution exists.
       *
       * \note If there exist more than one solution, this method will arbitrarily choose one.
-      *       If you need a complete analysis of the space of solutions, take the one solution obtained  *       by this method and add to it elements of the kernel, as determined by kernel().
+      *       If you need a complete analysis of the space of solutions, take the one solution obtained
+      *       by this method and add to it elements of the kernel, as determined by kernel().
       *
       * Example: \include LU_solve.cpp
       * Output: \verbinclude LU_solve.out
@@ -189,10 +236,7 @@ template<typename MatrixType> class LU
       * \sa MatrixBase::solveTriangular(), kernel(), computeKernel(), inverse(), computeInverse()
       */
     template<typename OtherDerived, typename ResultType>
-    bool solve(
-    const MatrixBase<OtherDerived>& b,
-    ResultType *result
-    ) const;
+    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const;
 
     /** \returns the determinant of the matrix of which
       * *this is the LU decomposition. It has only linear complexity
@@ -292,6 +336,7 @@ template<typename MatrixType> class LU
     }
 
   protected:
+    const MatrixType& m_originalMatrix;
     MatrixType m_lu;
     IntColVectorType m_p;
     IntRowVectorType m_q;
@@ -301,7 +346,8 @@ template<typename MatrixType> class LU
 
 template<typename MatrixType>
 LU<MatrixType>::LU(const MatrixType& matrix)
-  : m_lu(matrix),
+  : m_originalMatrix(matrix),
+    m_lu(matrix),
     m_p(matrix.rows()),
     m_q(matrix.cols())
 {
@@ -314,7 +360,7 @@ LU<MatrixType>::LU(const MatrixType& matrix)
   int number_of_transpositions = 0;
 
   RealScalar biggest = RealScalar(0);
-  for(int k = 0; k < size; k++)
+  for(int k = 0; k < size; ++k)
   {
     int row_of_biggest_in_corner, col_of_biggest_in_corner;
     RealScalar biggest_in_corner;
@@ -328,11 +374,11 @@ LU<MatrixType>::LU(const MatrixType& matrix)
     cols_transpositions.coeffRef(k) = col_of_biggest_in_corner;
     if(k != row_of_biggest_in_corner) {
       m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
-      number_of_transpositions++;
+      ++number_of_transpositions;
     }
     if(k != col_of_biggest_in_corner) {
       m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
-      number_of_transpositions++;
+      ++number_of_transpositions;
     }
 
     if(k==0) biggest = biggest_in_corner;
@@ -341,21 +387,21 @@ LU<MatrixType>::LU(const MatrixType& matrix)
     if(k<rows-1)
       m_lu.col(k).end(rows-k-1) /= lu_k_k;
     if(k<size-1)
-      for( int col = k + 1; col < cols; col++ )
+      for(int col = k + 1; col < cols; ++col)
         m_lu.col(col).end(rows-k-1) -= m_lu.col(k).end(rows-k-1) * m_lu.coeff(k,col);
   }
 
-  for(int k = 0; k < matrix.rows(); k++) m_p.coeffRef(k) = k;
-  for(int k = size-1; k >= 0; k--)
+  for(int k = 0; k < matrix.rows(); ++k) m_p.coeffRef(k) = k;
+  for(int k = size-1; k >= 0; --k)
     std::swap(m_p.coeffRef(k), m_p.coeffRef(rows_transpositions.coeff(k)));
 
-  for(int k = 0; k < matrix.cols(); k++) m_q.coeffRef(k) = k;
-  for(int k = 0; k < size; k++)
+  for(int k = 0; k < matrix.cols(); ++k) m_q.coeffRef(k) = k;
+  for(int k = 0; k < size; ++k)
     std::swap(m_q.coeffRef(k), m_q.coeffRef(cols_transpositions.coeff(k)));
 
   m_det_pq = (number_of_transpositions%2) ? -1 : 1;
 
-  for(m_rank = 0; m_rank < size; m_rank++)
+  for(m_rank = 0; m_rank < size; ++m_rank)
     if(ei_isMuchSmallerThan(m_lu.diagonal().coeff(m_rank), m_lu.diagonal().coeff(0)))
       break;
 }
@@ -367,7 +413,8 @@ typename ei_traits<MatrixType>::Scalar LU<MatrixType>::determinant() const
 }
 
 template<typename MatrixType>
-void LU<MatrixType>::computeKernel(KernelResultType *result) const
+template<typename KernelMatrixType>
+void LU<MatrixType>::computeKernel(KernelMatrixType *result) const
 {
   ei_assert(!isInvertible());
   const int dimker = dimensionOfKernel(), cols = m_lu.cols();
@@ -376,31 +423,31 @@ void LU<MatrixType>::computeKernel(KernelResultType *result) const
   /* Let us use the following lemma:
     *
     * Lemma: If the matrix A has the LU decomposition PAQ = LU,
-    * then Ker A = Q( Ker U ).
+    * then Ker A = Q(Ker U).
     *
     * Proof: trivial: just keep in mind that P, Q, L are invertible.
     */
 
   /* Thus, all we need to do is to compute Ker U, and then apply Q.
     *
-    * U is upper triangular, with eigenvalues sorted in decreasing order of
-    * absolute value. Thus, the diagonal of U ends with exactly
+    * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
+    * Thus, the diagonal of U ends with exactly
     * m_dimKer zero's. Let us use that to construct m_dimKer linearly
     * independent vectors in Ker U.
     */
 
-  Matrix<Scalar, Dynamic, Dynamic, MatrixType::Flags&RowMajorBit,
-         MatrixType::MaxColsAtCompileTime, MaxSmallDimAtCompileTime>
+  Matrix<Scalar, Dynamic, Dynamic, MatrixType::Options,
+         MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime>
     y(-m_lu.corner(TopRight, m_rank, dimker));
 
   m_lu.corner(TopLeft, m_rank, m_rank)
-      .template marked<Upper>()
+      .template marked<UpperTriangular>()
       .solveTriangularInPlace(y);
 
-  for(int i = 0; i < m_rank; i++)
+  for(int i = 0; i < m_rank; ++i)
     result->row(m_q.coeff(i)) = y.row(i);
-  for(int i = m_rank; i < cols; i++) result->row(m_q.coeff(i)).setZero();
-  for(int k = 0; k < dimker; k++) result->coeffRef(m_q.coeff(m_rank+k), k) = Scalar(1);
+  for(int i = m_rank; i < cols; ++i) result->row(m_q.coeff(i)).setZero();
+  for(int k = 0; k < dimker; ++k) result->coeffRef(m_q.coeff(m_rank+k), k) = Scalar(1);
 }
 
 template<typename MatrixType>
@@ -412,6 +459,25 @@ LU<MatrixType>::kernel() const
   return result;
 }
 
+template<typename MatrixType>
+template<typename ImageMatrixType>
+void LU<MatrixType>::computeImage(ImageMatrixType *result) const
+{
+  ei_assert(m_rank > 0);
+  result->resize(m_originalMatrix.rows(), m_rank);
+  for(int i = 0; i < m_rank; ++i)
+    result->col(i) = m_originalMatrix.col(m_q.coeff(i));
+}
+
+template<typename MatrixType>
+const typename LU<MatrixType>::ImageResultType
+LU<MatrixType>::image() const
+{
+  ImageResultType result(m_originalMatrix.rows(), m_rank);
+  computeImage(&result);
+  return result;
+}
+
 template<typename MatrixType>
 template<typename OtherDerived, typename ResultType>
 bool LU<MatrixType>::solve(
@@ -431,43 +497,43 @@ bool LU<MatrixType>::solve(
   ei_assert(b.rows() == rows);
   const int smalldim = std::min(rows, m_lu.cols());
 
-  typename OtherDerived::Eval c(b.rows(), b.cols());
+  typename OtherDerived::PlainMatrixType c(b.rows(), b.cols());
 
   // Step 1
-  for(int i = 0; i < rows; i++) c.row(m_p.coeff(i)) = b.row(i);
+  for(int i = 0; i < rows; ++i) c.row(m_p.coeff(i)) = b.row(i);
 
   // Step 2
   Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime,
-         MatrixType::Flags&RowMajorBit,
+         MatrixType::Options,
          MatrixType::MaxRowsAtCompileTime,
          MatrixType::MaxRowsAtCompileTime> l(rows, rows);
   l.setZero();
   l.corner(Eigen::TopLeft,rows,smalldim)
     = m_lu.corner(Eigen::TopLeft,rows,smalldim);
-  l.template marked<UnitLower>().solveTriangularInPlace(c);
+  l.template marked<UnitLowerTriangular>().solveTriangularInPlace(c);
 
   // Step 3
   if(!isSurjective())
   {
     // is c is in the image of U ?
     RealScalar biggest_in_c = c.corner(TopLeft, m_rank, c.cols()).cwise().abs().maxCoeff();
-    for(int col = 0; col < c.cols(); col++)
-      for(int row = m_rank; row < c.rows(); row++)
+    for(int col = 0; col < c.cols(); ++col)
+      for(int row = m_rank; row < c.rows(); ++row)
         if(!ei_isMuchSmallerThan(c.coeff(row,col), biggest_in_c))
           return false;
   }
   Matrix<Scalar, Dynamic, OtherDerived::ColsAtCompileTime,
-         MatrixType::Flags&RowMajorBit,
+         MatrixType::Options,
          MatrixType::MaxRowsAtCompileTime, OtherDerived::MaxColsAtCompileTime>
     d(c.corner(TopLeft, m_rank, c.cols()));
   m_lu.corner(TopLeft, m_rank, m_rank)
-      .template marked<Upper>()
+      .template marked<UpperTriangular>()
       .solveTriangularInPlace(d);
 
   // Step 4
   result->resize(m_lu.cols(), b.cols());
-  for(int i = 0; i < m_rank; i++) result->row(m_q.coeff(i)) = d.row(i);
-  for(int i = m_rank; i < m_lu.cols(); i++) result->row(m_q.coeff(i)).setZero();
+  for(int i = 0; i < m_rank; ++i) result->row(m_q.coeff(i)) = d.row(i);
+  for(int i = m_rank; i < m_lu.cols(); ++i) result->row(m_q.coeff(i)).setZero();
   return true;
 }
 
@@ -478,10 +544,10 @@ bool LU<MatrixType>::solve(
   * \sa class LU
   */
 template<typename Derived>
-inline const LU<typename MatrixBase<Derived>::EvalType>
+inline const LU<typename MatrixBase<Derived>::PlainMatrixType>
 MatrixBase<Derived>::lu() const
 {
-  return eval();
+  return LU<PlainMatrixType>(eval());
 }
 
 #endif // EIGEN_LU_H

Eigen/src/QR/EigenSolver.h

@@ -26,6 +26,7 @@
 #define EIGEN_EIGENSOLVER_H
 
 /** \ingroup QR_Module
+  * \nonstableyet
   *
   * \class EigenSolver
   *
@@ -48,6 +49,7 @@ template<typename _MatrixType> class EigenSolver
     typedef typename NumTraits<Scalar>::Real RealScalar;
     typedef std::complex<RealScalar> Complex;
     typedef Matrix<Complex, MatrixType::ColsAtCompileTime, 1> EigenvalueType;
+    typedef Matrix<Complex, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> EigenvectorType;
     typedef Matrix<RealScalar, MatrixType::ColsAtCompileTime, 1> RealVectorType;
     typedef Matrix<RealScalar, Dynamic, 1> RealVectorTypeX;
 
@@ -58,9 +60,46 @@ template<typename _MatrixType> class EigenSolver
       compute(matrix);
     }
 
-    MatrixType eigenvectors(void) const { return m_eivec; }
 
-    EigenvalueType eigenvalues(void) const { return m_eivalues; }
+    EigenvectorType eigenvectors(void) const;
+
+    /** \returns a real matrix V of pseudo eigenvectors.
+      *
+      * Let D be the block diagonal matrix with the real eigenvalues in 1x1 blocks,
+      * and any complex values u+iv in 2x2 blocks [u v ; -v u]. Then, the matrices D
+      * and V satisfy A*V = V*D.
+      *
+      * More precisely, if the diagonal matrix of the eigen values is:\n
+      * \f$
+      * \left[ \begin{array}{cccccc}
+      * u+iv &      &      &      &   &   \\
+      *      & u-iv &      &      &   &   \\
+      *      &      & a+ib &      &   &   \\
+      *      &      &      & a-ib &   &   \\
+      *      &      &      &      & x &   \\
+      *      &      &      &      &   & y \\
+      * \end{array} \right]
+      * \f$ \n
+      * then, we have:\n
+      * \f$
+      * D =\left[ \begin{array}{cccccc}
+      *  u & v &    &   &   &   \\
+      * -v & u &    &   &   &   \\
+      *    &   &  a & b &   &   \\
+      *    &   & -b & a &   &   \\
+      *    &   &    &   & x &   \\
+      *    &   &    &   &   & y \\
+      * \end{array} \right]
+      * \f$
+      *
+      * \sa pseudoEigenvalueMatrix()
+      */
+    const MatrixType& pseudoEigenvectors() const { return m_eivec; }
+
+    MatrixType pseudoEigenvalueMatrix() const;
+
+    /** \returns the eigenvalues as a column vector */
+    EigenvalueType eigenvalues() const { return m_eivalues; }
 
     void compute(const MatrixType& matrix);
 
@@ -74,6 +113,61 @@ template<typename _MatrixType> class EigenSolver
     EigenvalueType m_eivalues;
 };
 
+/** \returns the real block diagonal matrix D of the eigenvalues.
+  *
+  * See pseudoEigenvectors() for the details.
+  */
+template<typename MatrixType>
+MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
+{
+  int n = m_eivec.cols();
+  MatrixType matD = MatrixType::Zero(n,n);
+  for (int i=0; i<n; ++i)
+  {
+    if (ei_isMuchSmallerThan(ei_imag(m_eivalues.coeff(i)), ei_real(m_eivalues.coeff(i))))
+      matD.coeffRef(i,i) = ei_real(m_eivalues.coeff(i));
+    else
+    {
+      matD.template block<2,2>(i,i) <<  ei_real(m_eivalues.coeff(i)), ei_imag(m_eivalues.coeff(i)),
+                                       -ei_imag(m_eivalues.coeff(i)), ei_real(m_eivalues.coeff(i));
+      ++i;
+    }
+  }
+  return matD;
+}
+
+/** \returns the normalized complex eigenvectors as a matrix of column vectors.
+  *
+  * \sa eigenvalues(), pseudoEigenvectors()
+  */
+template<typename MatrixType>
+typename EigenSolver<MatrixType>::EigenvectorType EigenSolver<MatrixType>::eigenvectors(void) const
+{
+  int n = m_eivec.cols();
+  EigenvectorType matV(n,n);
+  for (int j=0; j<n; ++j)
+  {
+    if (ei_isMuchSmallerThan(ei_abs(ei_imag(m_eivalues.coeff(j))), ei_abs(ei_real(m_eivalues.coeff(j)))))
+    {
+      // we have a real eigen value
+      matV.col(j) = m_eivec.col(j).template cast<Complex>();
+    }
+    else
+    {
+      // we have a pair of complex eigen values
+      for (int i=0; i<n; ++i)
+      {
+        matV.coeffRef(i,j)   = Complex(m_eivec.coeff(i,j),  m_eivec.coeff(i,j+1));
+        matV.coeffRef(i,j+1) = Complex(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
+      }
+      matV.col(j).normalize();
+      matV.col(j+1).normalize();
+      ++j;
+    }
+  }
+  return matV;
+}
+
 template<typename MatrixType>
 void EigenSolver<MatrixType>::compute(const MatrixType& matrix)
 {
@@ -104,21 +198,21 @@ void EigenSolver<MatrixType>::orthes(MatrixType& matH, RealVectorType& ort)
   int low = 0;
   int high = n-1;
 
-  for (int m = low+1; m <= high-1; m++)
+  for (int m = low+1; m <= high-1; ++m)
   {
     // Scale column.
-    Scalar scale = matH.block(m, m-1, high-m+1, 1).cwise().abs().sum();
+    RealScalar scale = matH.block(m, m-1, high-m+1, 1).cwise().abs().sum();
     if (scale != 0.0)
     {
       // Compute Householder transformation.
-      Scalar h = 0.0;
+      RealScalar h = 0.0;
       // FIXME could be rewritten, but this one looks better wrt cache
       for (int i = high; i >= m; i--)
       {
         ort.coeffRef(i) = matH.coeff(i,m-1)/scale;
         h += ort.coeff(i) * ort.coeff(i);
       }
-      Scalar g = ei_sqrt(h);
+      RealScalar g = ei_sqrt(h);
       if (ort.coeff(m) > 0)
         g = -g;
       h = h - ort.coeff(m) * g;
@@ -127,11 +221,11 @@ void EigenSolver<MatrixType>::orthes(MatrixType& matH, RealVectorType& ort)
       // Apply Householder similarity transformation
       // H = (I-u*u'/h)*H*(I-u*u')/h)
       int bSize = high-m+1;
-      matH.block(m, m, bSize, n-m) -= ((ort.block(m, bSize)/h)
-        * (ort.block(m, bSize).transpose() *  matH.block(m, m, bSize, n-m)).lazy()).lazy();
+      matH.block(m, m, bSize, n-m) -= ((ort.segment(m, bSize)/h)
+        * (ort.segment(m, bSize).transpose() *  matH.block(m, m, bSize, n-m)).lazy()).lazy();
 
-      matH.block(0, m, high+1, bSize) -= ((matH.block(0, m, high+1, bSize) * ort.block(m, bSize)).lazy()
-        * (ort.block(m, bSize)/h).transpose()).lazy();
+      matH.block(0, m, high+1, bSize) -= ((matH.block(0, m, high+1, bSize) * ort.segment(m, bSize)).lazy()
+        * (ort.segment(m, bSize)/h).transpose()).lazy();
 
       ort.coeffRef(m) = scale*ort.coeff(m);
       matH.coeffRef(m,m-1) = scale*g;
@@ -145,16 +239,15 @@ void EigenSolver<MatrixType>::orthes(MatrixType& matH, RealVectorType& ort)
   {
     if (matH.coeff(m,m-1) != 0.0)
     {
-      ort.block(m+1, high-m) = matH.col(m-1).block(m+1, high-m);
+      ort.segment(m+1, high-m) = matH.col(m-1).segment(m+1, high-m);
 
       int bSize = high-m+1;
-      m_eivec.block(m, m, bSize, bSize) += ( (ort.block(m, bSize) /  (matH.coeff(m,m-1) * ort.coeff(m) ) )
-        * (ort.block(m, bSize).transpose() * m_eivec.block(m, m, bSize, bSize)).lazy());
+      m_eivec.block(m, m, bSize, bSize) += ( (ort.segment(m, bSize) /  (matH.coeff(m,m-1) * ort.coeff(m) ) )
+        * (ort.segment(m, bSize).transpose() * m_eivec.block(m, m, bSize, bSize)).lazy());
     }
   }
 }
 
-
 // Complex scalar division.
 template<typename Scalar>
 std::complex<Scalar> cdiv(Scalar xr, Scalar xi, Scalar yr, Scalar yi)
@@ -197,15 +290,14 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
   // FIXME to be efficient the following would requires a triangular reduxion code
   // Scalar norm = matH.upper().cwise().abs().sum() + matH.corner(BottomLeft,n,n).diagonal().cwise().abs().sum();
   Scalar norm = 0.0;
-  for (int j = 0; j < nn; j++)
+  for (int j = 0; j < nn; ++j)
   {
     // FIXME what's the purpose of the following since the condition is always false
     if ((j < low) || (j > high))
     {
-      m_eivalues.coeffRef(j).real() = matH.coeff(j,j);
-      m_eivalues.coeffRef(j).imag() = 0.0;
+      m_eivalues.coeffRef(j) = Complex(matH.coeff(j,j), 0.0);
     }
-    norm += matH.col(j).start(std::min(j+1,nn)).cwise().abs().sum();
+    norm += matH.row(j).segment(std::max(j-1,0), nn-std::max(j-1,0)).cwise().abs().sum();
   }
 
   // Outer loop over eigenvalue index
@@ -229,8 +321,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
     if (l == n)
     {
       matH.coeffRef(n,n) = matH.coeff(n,n) + exshift;
-      m_eivalues.coeffRef(n).real() = matH.coeff(n,n);
-      m_eivalues.coeffRef(n).imag() = 0.0;
+      m_eivalues.coeffRef(n) = Complex(matH.coeff(n,n), 0.0);
       n--;
       iter = 0;
     }
@@ -252,13 +343,9 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         else
           z = p - z;
 
-        m_eivalues.coeffRef(n-1).real() = x + z;
-        m_eivalues.coeffRef(n).real() = m_eivalues.coeff(n-1).real();
-        if (z != 0.0)
-          m_eivalues.coeffRef(n).real() = x - w / z;
+        m_eivalues.coeffRef(n-1) = Complex(x + z, 0.0);
+        m_eivalues.coeffRef(n) = Complex(z!=0.0 ? x - w / z : m_eivalues.coeff(n-1).real(), 0.0);
 
-        m_eivalues.coeffRef(n-1).imag() = 0.0;
-        m_eivalues.coeffRef(n).imag() = 0.0;
         x = matH.coeff(n,n-1);
         s = ei_abs(x) + ei_abs(z);
         p = x / s;
@@ -268,7 +355,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         q = q / r;
 
         // Row modification
-        for (int j = n-1; j < nn; j++)
+        for (int j = n-1; j < nn; ++j)
         {
           z = matH.coeff(n-1,j);
           matH.coeffRef(n-1,j) = q * z + p * matH.coeff(n,j);
@@ -276,7 +363,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         }
 
         // Column modification
-        for (int i = 0; i <= n; i++)
+        for (int i = 0; i <= n; ++i)
         {
           z = matH.coeff(i,n-1);
           matH.coeffRef(i,n-1) = q * z + p * matH.coeff(i,n);
@@ -284,7 +371,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         }
 
         // Accumulate transformations
-        for (int i = low; i <= high; i++)
+        for (int i = low; i <= high; ++i)
         {
           z = m_eivec.coeff(i,n-1);
           m_eivec.coeffRef(i,n-1) = q * z + p * m_eivec.coeff(i,n);
@@ -293,10 +380,8 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
       }
       else // Complex pair
       {
-        m_eivalues.coeffRef(n-1).real() = x + p;
-        m_eivalues.coeffRef(n).real() = x + p;
-        m_eivalues.coeffRef(n-1).imag() = z;
-        m_eivalues.coeffRef(n).imag() = -z;
+        m_eivalues.coeffRef(n-1) = Complex(x + p, z);
+        m_eivalues.coeffRef(n)   = Complex(x + p, -z);
       }
       n = n - 2;
       iter = 0;
@@ -317,7 +402,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
       if (iter == 10)
       {
         exshift += x;
-        for (int i = low; i <= n; i++)
+        for (int i = low; i <= n; ++i)
           matH.coeffRef(i,i) -= x;
         s = ei_abs(matH.coeff(n,n-1)) + ei_abs(matH.coeff(n-1,n-2));
         x = y = 0.75 * s;
@@ -335,7 +420,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
           if (y < x)
             s = -s;
           s = x - w / ((y - x) / 2.0 + s);
-          for (int i = low; i <= n; i++)
+          for (int i = low; i <= n; ++i)
             matH.coeffRef(i,i) -= s;
           exshift += s;
           x = y = w = 0.964;
@@ -370,7 +455,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
         m--;
       }
 
-      for (int i = m+2; i <= n; i++)
+      for (int i = m+2; i <= n; ++i)
       {
         matH.coeffRef(i,i-2) = 0.0;
         if (i > m+2)
@@ -378,7 +463,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
       }
 
       // Double QR step involving rows l:n and columns m:n
-      for (int k = m; k <= n-1; k++)
+      for (int k = m; k <= n-1; ++k)
       {
         int notlast = (k != n-1);
         if (k != m) {
@@ -417,7 +502,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
           r = r / p;
 
           // Row modification
-          for (int j = k; j < nn; j++)
+          for (int j = k; j < nn; ++j)
           {
             p = matH.coeff(k,j) + q * matH.coeff(k+1,j);
             if (notlast)
@@ -430,7 +515,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
           }
 
           // Column modification
-          for (int i = 0; i <= std::min(n,k+3); i++)
+          for (int i = 0; i <= std::min(n,k+3); ++i)
           {
             p = x * matH.coeff(i,k) + y * matH.coeff(i,k+1);
             if (notlast)
@@ -443,7 +528,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
           }
 
           // Accumulate transformations
-          for (int i = low; i <= high; i++)
+          for (int i = low; i <= high; ++i)
           {
             p = x * m_eivec.coeff(i,k) + y * m_eivec.coeff(i,k+1);
             if (notlast)
@@ -478,7 +563,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
       for (int i = n-1; i >= 0; i--)
       {
         w = matH.coeff(i,i) - p;
-        r = (matH.row(i).end(nn-l) * matH.col(n).end(nn-l))(0,0);
+        r = (matH.row(i).segment(l,n-l+1) * matH.col(n).segment(l, n-l+1))(0,0);
 
         if (m_eivalues.coeff(i).imag() < 0.0)
         {
@@ -537,8 +622,8 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
       for (int i = n-2; i >= 0; i--)
       {
         Scalar ra,sa,vr,vi;
-        ra = (matH.row(i).end(nn-l) * matH.col(n-1).end(nn-l)).lazy()(0,0);
-        sa = (matH.row(i).end(nn-l) * matH.col(n).end(nn-l)).lazy()(0,0);
+        ra = (matH.block(i,l, 1, n-l+1) * matH.block(l,n-1, n-l+1, 1)).lazy()(0,0);
+        sa = (matH.block(i,l, 1, n-l+1) * matH.block(l,n, n-l+1, 1)).lazy()(0,0);
         w = matH.coeff(i,i) - p;
 
         if (m_eivalues.coeff(i).imag() < 0.0)
@@ -593,7 +678,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
   }
 
   // Vectors of isolated roots
-  for (int i = 0; i < nn; i++)
+  for (int i = 0; i < nn; ++i)
   {
     // FIXME again what's the purpose of this test ?
     // in this algo low==0 and high==nn-1 !!
@@ -608,7 +693,7 @@ void EigenSolver<MatrixType>::hqr2(MatrixType& matH)
   for (int j = nn-1; j >= low; j--)
   {
     int bSize = std::min(j,high)-low+1;
-    m_eivec.col(j).block(low, bRows) = (m_eivec.block(low, low, bRows, bSize) * matH.col(j).block(low, bSize));
+    m_eivec.col(j).segment(low, bRows) = (m_eivec.block(low, low, bRows, bSize) * matH.col(j).segment(low, bSize));
   }
 }
 

Eigen/src/QR/HessenbergDecomposition.h

@@ -26,6 +26,7 @@
 #define EIGEN_HESSENBERGDECOMPOSITION_H
 
 /** \ingroup QR_Module
+  * \nonstableyet
   *
   * \class HessenbergDecomposition
   *
@@ -148,7 +149,7 @@ void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVector
 
     // start of the householder transformation
     // squared norm of the vector v skipping the first element
-    RealScalar v1norm2 = matA.col(i).end(n-(i+2)).norm2();
+    RealScalar v1norm2 = matA.col(i).end(n-(i+2)).squaredNorm();
 
     if (ei_isMuchSmallerThan(v1norm2,static_cast<Scalar>(1)))
     {
@@ -242,7 +243,7 @@ HessenbergDecomposition<MatrixType>::matrixH(void) const
   int n = m_matrix.rows();
   MatrixType matH = m_matrix;
   if (n>2)
-    matH.corner(BottomLeft,n-2, n-2).template part<Lower>().setZero();
+    matH.corner(BottomLeft,n-2, n-2).template part<LowerTriangular>().setZero();
   return matH;
 }
 

Eigen/src/QR/QR.h

@@ -26,6 +26,7 @@
 #define EIGEN_QR_H
 
 /** \ingroup QR_Module
+  * \nonstableyet
   *
   * \class QR
   *
@@ -59,11 +60,11 @@ template<typename MatrixType> class QR
     bool isFullRank() const { return ei_isMuchSmallerThan(m_hCoeffs.cwise().abs().minCoeff(), Scalar(1)); }
 
     /** \returns a read-only expression of the matrix R of the actual the QR decomposition */
-    const Part<NestByValue<MatrixRBlockType>, Upper>
+    const Part<NestByValue<MatrixRBlockType>, UpperTriangular>
     matrixR(void) const
     {
       int cols = m_qr.cols();
-      return MatrixRBlockType(m_qr, 0, 0, cols, cols).nestByValue().template part<Upper>();
+      return MatrixRBlockType(m_qr, 0, 0, cols, cols).nestByValue().template part<UpperTriangular>();
     }
 
     MatrixType matrixQ(void) const;
@@ -86,7 +87,7 @@ void QR<MatrixType>::_compute(const MatrixType& matrix)
   int rows = matrix.rows();
   int cols = matrix.cols();
 
-  for (int k = 0; k < cols; k++)
+  for (int k = 0; k < cols; ++k)
   {
     int remainingSize = rows-k;
 
@@ -109,7 +110,7 @@ void QR<MatrixType>::_compute(const MatrixType& matrix)
         m_hCoeffs.coeffRef(k) = 0;
       }
     }
-    else if ( (!ei_isMuchSmallerThan(beta=m_qr.col(k).end(remainingSize-1).norm2(),static_cast<Scalar>(1))) || ei_imag(v0)==0 )
+    else if ( (!ei_isMuchSmallerThan(beta=m_qr.col(k).end(remainingSize-1).squaredNorm(),static_cast<Scalar>(1))) || ei_imag(v0)==0 )
     {
       // form k-th Householder vector
       beta = ei_sqrt(ei_abs2(v0)+beta);
@@ -168,10 +169,10 @@ MatrixType QR<MatrixType>::matrixQ(void) const
   * \sa class QR
   */
 template<typename Derived>
-const QR<typename MatrixBase<Derived>::EvalType>
+const QR<typename MatrixBase<Derived>::PlainMatrixType>
 MatrixBase<Derived>::qr() const
 {
-  return eval();
+  return QR<PlainMatrixType>(eval());
 }
 
 

Eigen/src/QR/QrInstantiations.cpp

@@ -26,8 +26,6 @@
 #define EIGEN_EXTERN_INSTANTIATIONS
 #endif
 #include "../../Core"
-// commented because of -pedantic
-// #include "../../Cholesky"
 #undef EIGEN_EXTERN_INSTANTIATIONS
 
 #include "../../QR"

Eigen/src/QR/SelfAdjointEigenSolver.h

@@ -26,6 +26,7 @@
 #define EIGEN_SELFADJOINTEIGENSOLVER_H
 
 /** \qr_module \ingroup QR_Module
+  * \nonstableyet
   *
   * \class SelfAdjointEigenSolver
   *
@@ -104,6 +105,25 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
     /** \returns the computed eigen values */
     RealVectorType eigenvalues(void) const { return m_eivalues; }
 
+    /** \returns the positive square root of the matrix
+      *
+      * \note the matrix itself must be positive in order for this to make sense.
+      */
+    MatrixType operatorSqrt() const
+    {
+      return m_eivec * m_eivalues.cwise().sqrt().asDiagonal() * m_eivec.adjoint();
+    }
+
+    /** \returns the positive inverse square root of the matrix
+      *
+      * \note the matrix itself must be positive definite in order for this to make sense.
+      */
+    MatrixType operatorInverseSqrt() const
+    {
+      return m_eivec * m_eivalues.cwise().inverse().cwise().sqrt().asDiagonal() * m_eivec.adjoint();
+    }
+
+
   protected:
     MatrixType m_eivec;
     RealVectorType m_eivalues;
@@ -201,10 +221,10 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool
   // Sort eigenvalues and corresponding vectors.
   // TODO make the sort optional ?
   // TODO use a better sort algorithm !!
-  for (int i = 0; i < n-1; i++)
+  for (int i = 0; i < n-1; ++i)
   {
     int k;
-    m_eivalues.block(i,n-i).minCoeff(&k);
+    m_eivalues.segment(i,n-i).minCoeff(&k);
     if (k > 0)
     {
       std::swap(m_eivalues[i], m_eivalues[k+i]);
@@ -225,31 +245,31 @@ void SelfAdjointEigenSolver<MatrixType>::
 compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors)
 {
   ei_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
-  
+
   // Compute the cholesky decomposition of matB = L L'
-  Cholesky<MatrixType> cholB(matB);
+  LLT<MatrixType> cholB(matB);
 
   // compute C = inv(L) A inv(L')
   MatrixType matC = matA;
   cholB.matrixL().solveTriangularInPlace(matC);
   // FIXME since we currently do not support A * inv(L'), let's do (inv(L) A')' :
   matC = matC.adjoint().eval();
-  cholB.matrixL().template marked<Lower>().solveTriangularInPlace(matC);
+  cholB.matrixL().template marked<LowerTriangular>().solveTriangularInPlace(matC);
   matC = matC.adjoint().eval();
   // this version works too:
 //   matC = matC.transpose();
-//   cholB.matrixL().conjugate().template marked<Lower>().solveTriangularInPlace(matC);
+//   cholB.matrixL().conjugate().template marked<LowerTriangular>().solveTriangularInPlace(matC);
 //   matC = matC.transpose();
   // FIXME: this should work: (currently it only does for small matrices)
 //   Transpose<MatrixType> trMatC(matC);
-//   cholB.matrixL().conjugate().eval().template marked<Lower>().solveTriangularInPlace(trMatC);
+//   cholB.matrixL().conjugate().eval().template marked<LowerTriangular>().solveTriangularInPlace(trMatC);
 
   compute(matC, computeEigenvectors);
 
   if (computeEigenvectors)
   {
     // transform back the eigen vectors: evecs = inv(U) * evecs
-    cholB.matrixL().adjoint().template marked<Upper>().solveTriangularInPlace(m_eivec);
+    cholB.matrixL().adjoint().template marked<UpperTriangular>().solveTriangularInPlace(m_eivec);
     for (int i=0; i<m_eivec.cols(); ++i)
       m_eivec.col(i) = m_eivec.col(i).normalized();
   }
@@ -266,7 +286,7 @@ inline Matrix<typename NumTraits<typename ei_traits<Derived>::Scalar>::Real, ei_
 MatrixBase<Derived>::eigenvalues() const
 {
   ei_assert(Flags&SelfAdjointBit);
-  return SelfAdjointEigenSolver<typename Derived::Eval>(eval(),false).eigenvalues();
+  return SelfAdjointEigenSolver<typename Derived::PlainMatrixType>(eval(),false).eigenvalues();
 }
 
 template<typename Derived, bool IsSelfAdjoint>
@@ -286,7 +306,7 @@ template<typename Derived> struct ei_operatorNorm_selector<Derived, false>
   static inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real
   operatorNorm(const MatrixBase<Derived>& m)
   {
-    typename Derived::Eval m_eval(m);
+    typename Derived::PlainMatrixType m_eval(m);
     // FIXME if it is really guaranteed that the eigenvalues are already sorted,
     // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
     return ei_sqrt(

Eigen/src/QR/Tridiagonalization.h

@@ -26,6 +26,7 @@
 #define EIGEN_TRIDIAGONALIZATION_H
 
 /** \ingroup QR_Module
+  * \nonstableyet
   *
   * \class Tridiagonalization
   *
@@ -163,11 +164,11 @@ Tridiagonalization<MatrixType>::matrixT(void) const
   // and fill it ? (to avoid temporaries)
   int n = m_matrix.rows();
   MatrixType matT = m_matrix;
-  matT.corner(TopRight,n-1, n-1).diagonal() = subDiagonal().conjugate();
+  matT.corner(TopRight,n-1, n-1).diagonal() = subDiagonal().template cast<Scalar>().conjugate();
   if (n>2)
   {
-    matT.corner(TopRight,n-2, n-2).template part<Upper>().setZero();
-    matT.corner(BottomLeft,n-2, n-2).template part<Lower>().setZero();
+    matT.corner(TopRight,n-2, n-2).template part<UpperTriangular>().setZero();
+    matT.corner(BottomLeft,n-2, n-2).template part<LowerTriangular>().setZero();
   }
   return matT;
 }
@@ -198,7 +199,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
 
     // start of the householder transformation
     // squared norm of the vector v skipping the first element
-    RealScalar v1norm2 = matA.col(i).end(n-(i+2)).norm2();
+    RealScalar v1norm2 = matA.col(i).end(n-(i+2)).squaredNorm();
 
     if (ei_isMuchSmallerThan(v1norm2,static_cast<Scalar>(1)))
     {
@@ -219,20 +220,20 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
       // i.e., A = H' A H where H = I - h v v' and v = matA.col(i).end(n-i-1)
 
       matA.col(i).coeffRef(i+1) = 1;
-      
+
       /* This is the initial algorithm which minimize operation counts and maximize
        * the use of Eigen's expression. Unfortunately, the first matrix-vector product
-       * using Part<Lower|Selfadjoint>  is very very slow */
+       * using Part<LowerTriangular|Selfadjoint>  is very very slow */
       #ifdef EIGEN_NEVER_DEFINED
       // matrix - vector product
-      hCoeffs.end(n-i-1) = (matA.corner(BottomRight,n-i-1,n-i-1).template part<Lower|SelfAdjoint>()
+      hCoeffs.end(n-i-1) = (matA.corner(BottomRight,n-i-1,n-i-1).template part<LowerTriangular|SelfAdjoint>()
                                 * (h * matA.col(i).end(n-i-1))).lazy();
       // simple axpy
       hCoeffs.end(n-i-1) += (h * Scalar(-0.5) * matA.col(i).end(n-i-1).dot(hCoeffs.end(n-i-1)))
                             * matA.col(i).end(n-i-1);
       // rank-2 update
       //Block<MatrixType,Dynamic,1> B(matA,i+1,i,n-i-1,1);
-      matA.corner(BottomRight,n-i-1,n-i-1).template part<Lower>() -=
+      matA.corner(BottomRight,n-i-1,n-i-1).template part<LowerTriangular>() -=
             (matA.col(i).end(n-i-1) * hCoeffs.end(n-i-1).adjoint()).lazy()
           + (hCoeffs.end(n-i-1) * matA.col(i).end(n-i-1).adjoint()).lazy();
       #endif
@@ -255,7 +256,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
             Block<MatrixType,Dynamic,4>(matA,b+4,b,n-b-4,4).adjoint() * Block<MatrixType,Dynamic,1>(matA,b+4,i,n-b-4,1);
         // the 4x4 block diagonal:
         Block<CoeffVectorType,4,1>(hCoeffs, b, 0, 4,1) +=
-            (Block<MatrixType,4,4>(matA,b,b,4,4).template part<Lower|SelfAdjoint>()
+            (Block<MatrixType,4,4>(matA,b,b,4,4).template part<LowerTriangular|SelfAdjoint>()
              * (h * Block<MatrixType,4,1>(matA,b,i,4,1))).lazy();
       }
       #endif
@@ -267,7 +268,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
        * if we remove the specialization of Block for Matrix then it is even worse, much worse ! */
       #ifdef EIGEN_NEVER_DEFINED
       for (int j1=i+1; j1<n; ++j1)
-      for (int i1=j1;  i1<n; i1++)
+      for (int i1=j1;  i1<n; ++i1)
         matA.coeffRef(i1,j1) -= matA.coeff(i1,i)*ei_conj(hCoeffs.coeff(j1-1))
                               + hCoeffs.coeff(i1-1)*ei_conj(matA.coeff(j1,i));
       #endif
@@ -284,7 +285,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
 
       hCoeffs.end(n-i-1) += (h * Scalar(-0.5) * matA.col(i).end(n-i-1).dot(hCoeffs.end(n-i-1)))
                             * matA.col(i).end(n-i-1);
-      
+
       const Scalar* EIGEN_RESTRICT pb = &matA.coeffRef(0,i);
       const Scalar* EIGEN_RESTRICT pa = (&hCoeffs.coeffRef(0)) - 1;
       for (int j1=i+1; j1<n; ++j1)
@@ -295,11 +296,11 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
         {
           int alignedStart = (starti) + ei_alignmentOffset(&matA.coeffRef(starti,j1), n-starti);
           alignedEnd = alignedStart + ((n-alignedStart)/PacketSize)*PacketSize;
-          
+
           for (int i1=starti; i1<alignedStart; ++i1)
             matA.coeffRef(i1,j1) -= matA.coeff(i1,i)*ei_conj(hCoeffs.coeff(j1-1))
                                   + hCoeffs.coeff(i1-1)*ei_conj(matA.coeff(j1,i));
-          
+
           Packet tmp0 = ei_pset1(hCoeffs.coeff(j1-1));
           Packet tmp1 = ei_pset1(matA.coeff(j1,i));
           Scalar* pc = &matA.coeffRef(0,j1);

Eigen/src/Regression/Regression.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra. Eigen itself is part of the KDE project.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -111,18 +111,18 @@ void linearRegression(int numPoints,
          Dynamic, VectorType::MaxSizeAtCompileTime, RowMajorBit>
     m(numPoints, size);
   if(funcOfOthers>0)
-    for(int i = 0; i < numPoints; i++)
+    for(int i = 0; i < numPoints; ++i)
       m.row(i).start(funcOfOthers) = points[i]->start(funcOfOthers);
   if(funcOfOthers<size-1)
-    for(int i = 0; i < numPoints; i++)
+    for(int i = 0; i < numPoints; ++i)
       m.row(i).block(funcOfOthers, size-funcOfOthers-1)
         = points[i]->end(size-funcOfOthers-1);
-  for(int i = 0; i < numPoints; i++)
+  for(int i = 0; i < numPoints; ++i)
     m.row(i).coeffRef(size-1) = Scalar(1);
 
   VectorType v(size);
   v.setZero();
-  for(int i = 0; i < numPoints; i++)
+  for(int i = 0; i < numPoints; ++i)
     v += m.row(i).adjoint() * points[i]->coeff(funcOfOthers);
 
   ei_assert((m.adjoint()*m).lu().solve(v, result));
@@ -170,14 +170,14 @@ void fitHyperplane(int numPoints,
 
   // compute the mean of the data
   VectorType mean = VectorType::Zero(size);
-  for(int i = 0; i < numPoints; i++)
+  for(int i = 0; i < numPoints; ++i)
     mean += *(points[i]);
   mean /= numPoints;
 
   // compute the covariance matrix
   CovMatrixType covMat = CovMatrixType::Zero(size, size);
   VectorType remean = VectorType::Zero(size);
-  for(int i = 0; i < numPoints; i++)
+  for(int i = 0; i < numPoints; ++i)
   {
     VectorType diff = (*(points[i]) - mean).conjugate();
     covMat += diff * diff.adjoint();

Eigen/src/SVD/SVD.h

@@ -26,6 +26,7 @@
 #define EIGEN_SVD_H
 
 /** \ingroup SVD_Module
+  * \nonstableyet
   *
   * \class SVD
   *
@@ -50,16 +51,16 @@ template<typename MatrixType> class SVD
       AlignmentMask = int(PacketSize)-1,
       MinSize = EIGEN_ENUM_MIN(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime)
     };
-    
+
     typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVector;
     typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> RowVector;
-    
+
     typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MinSize> MatrixUType;
     typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixVType;
     typedef Matrix<Scalar, MinSize, 1> SingularValuesType;
 
   public:
-  
+
     SVD(const MatrixType& matrix)
       : m_matU(matrix.rows(), std::min(matrix.rows(), matrix.cols())),
         m_matV(matrix.cols(),matrix.cols()),
@@ -69,7 +70,7 @@ template<typename MatrixType> class SVD
     }
 
     template<typename OtherDerived, typename ResultType>
-    void solve(const MatrixBase<OtherDerived> &b, ResultType* result) const;
+    bool solve(const MatrixBase<OtherDerived> &b, ResultType* result) const;
 
     const MatrixUType& matrixU() const { return m_matU; }
     const SingularValuesType& singularValues() const { return m_sigma; }
@@ -97,7 +98,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
   const int m = matrix.rows();
   const int n = matrix.cols();
   const int nu = std::min(m,n);
-  
+
   m_matU.resize(m, nu);
   m_matU.setZero();
   m_sigma.resize(std::min(m,n));
@@ -114,7 +115,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
   // in s and the super-diagonal elements in e.
   int nct = std::min(m-1,n);
   int nrt = std::max(0,std::min(n-2,m));
-  for (k = 0; k < std::max(nct,nrt); k++)
+  for (k = 0; k < std::max(nct,nrt); ++k)
   {
     if (k < nct)
     {
@@ -130,8 +131,8 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
       }
       m_sigma[k] = -m_sigma[k];
     }
-    
-    for (j = k+1; j < n; j++)
+
+    for (j = k+1; j < n; ++j)
     {
       if ((k < nct) && (m_sigma[k] != 0.0))
       {
@@ -167,7 +168,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
       {
         // Apply the transformation.
         work.end(m-k-1) = matA.corner(BottomRight,m-k-1,n-k-1) * e.end(n-k-1);
-        for (j = k+1; j < n; j++)
+        for (j = k+1; j < n; ++j)
           matA.col(j).end(m-k-1) += (-e[j]/e[k+1]) * work.end(m-k-1);
       }
 
@@ -191,7 +192,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
   // If required, generate U.
   if (wantu)
   {
-    for (j = nct; j < nu; j++)
+    for (j = nct; j < nu; ++j)
     {
       m_matU.col(j).setZero();
       m_matU(j,j) = 1.0;
@@ -200,7 +201,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
     {
       if (m_sigma[k] != 0.0)
       {
-        for (j = k+1; j < nu; j++)
+        for (j = k+1; j < nu; ++j)
         {
           Scalar t = m_matU.col(k).end(m-k).dot(m_matU.col(j).end(m-k)); // FIXME is it really a dot product we want ?
           t = -t/m_matU(k,k);
@@ -226,7 +227,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
     {
       if ((k < nrt) & (e[k] != 0.0))
       {
-        for (j = k+1; j < nu; j++)
+        for (j = k+1; j < nu; ++j)
         {
           Scalar t = m_matV.col(k).end(n-k-1).dot(m_matV.col(j).end(n-k-1)); // FIXME is it really a dot product we want ?
           t = -t/m_matV(k+1,k);
@@ -301,7 +302,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
         k = ks;
       }
     }
-    k++;
+    ++k;
 
     // Perform the task indicated by kase.
     switch (kase)
@@ -325,7 +326,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
           }
           if (wantv)
           {
-            for (i = 0; i < n; i++)
+            for (i = 0; i < n; ++i)
             {
               t = cs*m_matV(i,j) + sn*m_matV(i,p-1);
               m_matV(i,p-1) = -sn*m_matV(i,j) + cs*m_matV(i,p-1);
@@ -341,7 +342,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
       {
         Scalar f(e[k-1]);
         e[k-1] = 0.0;
-        for (j = k; j < p; j++)
+        for (j = k; j < p; ++j)
         {
           Scalar t(hypot(m_sigma[j],f));
           Scalar cs( m_sigma[j]/t);
@@ -351,7 +352,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
           e[j] = cs*e[j];
           if (wantu)
           {
-            for (i = 0; i < m; i++)
+            for (i = 0; i < m; ++i)
             {
               t = cs*m_matU(i,j) + sn*m_matU(i,k-1);
               m_matU(i,k-1) = -sn*m_matU(i,j) + cs*m_matU(i,k-1);
@@ -389,7 +390,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 
         // Chase zeros.
 
-        for (j = k; j < p-1; j++)
+        for (j = k; j < p-1; ++j)
         {
           Scalar t = hypot(f,g);
           Scalar cs = f/t;
@@ -402,7 +403,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
           m_sigma[j+1] = cs*m_sigma[j+1];
           if (wantv)
           {
-            for (i = 0; i < n; i++)
+            for (i = 0; i < n; ++i)
             {
               t = cs*m_matV(i,j) + sn*m_matV(i,j+1);
               m_matV(i,j+1) = -sn*m_matV(i,j) + cs*m_matV(i,j+1);
@@ -419,7 +420,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
           e[j+1] = cs*e[j+1];
           if (wantu && (j < m-1))
           {
-            for (i = 0; i < m; i++)
+            for (i = 0; i < m; ++i)
             {
               t = cs*m_matU(i,j) + sn*m_matU(i,j+1);
               m_matU(i,j+1) = -sn*m_matU(i,j) + cs*m_matU(i,j+1);
@@ -455,7 +456,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
             m_matV.col(k).swap(m_matV.col(k+1));
           if (wantu && (k < m-1))
             m_matU.col(k).swap(m_matU.col(k+1));
-          k++;
+          ++k;
         }
         iter = 0;
         p--;
@@ -468,18 +469,18 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
 template<typename MatrixType>
 SVD<MatrixType>& SVD<MatrixType>::sort()
 {
-  int mu = m_matU.rows(); 
-  int mv = m_matV.rows(); 
+  int mu = m_matU.rows();
+  int mv = m_matV.rows();
   int n  = m_matU.cols();
 
-  for (int i=0; i<n; i++)
+  for (int i=0; i<n; ++i)
   {
-    int  k = i; 
+    int  k = i;
     Scalar p = m_sigma.coeff(i);
 
-    for (int j=i+1; j<n; j++)
+    for (int j=i+1; j<n; ++j)
     {
-      if (m_sigma.coeff(j) > p) 
+      if (m_sigma.coeff(j) > p)
       {
         k = j;
         p = m_sigma.coeff(j);
@@ -505,11 +506,11 @@ SVD<MatrixType>& SVD<MatrixType>::sort()
 /** \returns the solution of \f$ A x = b \f$ using the current SVD decomposition of A.
   * The parts of the solution corresponding to zero singular values are ignored.
   *
-  * \sa MatrixBase::svd(), LU::solve(), Cholesky::solve()
+  * \sa MatrixBase::svd(), LU::solve(), LLT::solve()
   */
 template<typename MatrixType>
 template<typename OtherDerived, typename ResultType>
-void SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* result) const
+bool SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* result) const
 {
   const int rows = m_matU.rows();
   ei_assert(b.rows() == rows);
@@ -519,7 +520,7 @@ void SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* resul
   {
     Matrix<Scalar,MatrixUType::RowsAtCompileTime,1> aux = m_matU.transpose() * b.col(j);
 
-    for (int i = 0; i <m_matU.cols(); i++)
+    for (int i = 0; i <m_matU.cols(); ++i)
     {
       Scalar si = m_sigma.coeff(i);
       if (ei_isMuchSmallerThan(ei_abs(si),maxVal))
@@ -530,16 +531,17 @@ void SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* resul
 
     result->col(j) = m_matV * aux;
   }
+  return true;
 }
 
 /** \svd_module
   * \returns the SVD decomposition of \c *this
   */
 template<typename Derived>
-inline SVD<typename MatrixBase<Derived>::EvalType>
+inline SVD<typename MatrixBase<Derived>::PlainMatrixType>
 MatrixBase<Derived>::svd() const
 {
-  return SVD<typename ei_eval<Derived>::type>(derived());
+  return SVD<PlainMatrixType>(derived());
 }
 
 #endif // EIGEN_SVD_H

Eigen/src/Sparse/AmbiVector.h

@@ -0,0 +1,371 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+/** \internal
+  * Hybrid sparse/dense vector class designed for intensive read-write operations.
+  *
+  * See BasicSparseLLT and SparseProduct for usage examples.
+  */
+template<typename _Scalar> class AmbiVector
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    AmbiVector(int size)
+      : m_buffer(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
+    {
+      resize(size);
+    }
+
+    void init(RealScalar estimatedDensity);
+    void init(int mode);
+
+    void nonZeros() const;
+
+    /** Specifies a sub-vector to work on */
+    void setBounds(int start, int end) { m_start = start; m_end = end; }
+
+    void setZero();
+
+    void restart();
+    Scalar& coeffRef(int i);
+    Scalar coeff(int i);
+
+    class Iterator;
+
+    ~AmbiVector() { delete[] m_buffer; }
+
+    void resize(int size)
+    {
+      if (m_allocatedSize < size)
+        reallocate(size);
+      m_size = size;
+    }
+
+    int size() const { return m_size; }
+
+  protected:
+
+    void reallocate(int size)
+    {
+      // if the size of the matrix is not too large, let's allocate a bit more than needed such
+      // that we can handle dense vector even in sparse mode.
+      delete[] m_buffer;
+      if (size<1000)
+      {
+        int allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
+        m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[allocSize];
+      }
+      else
+      {
+        m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[size];
+      }
+      m_size = size;
+      m_start = 0;
+      m_end = m_size;
+    }
+
+    void reallocateSparse()
+    {
+      int copyElements = m_allocatedElements;
+      m_allocatedElements = std::min(int(m_allocatedElements*1.5),m_size);
+      int allocSize = m_allocatedElements * sizeof(ListEl);
+      allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
+      Scalar* newBuffer = new Scalar[allocSize];
+      memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
+    }
+
+  protected:
+    // element type of the linked list
+    struct ListEl
+    {
+      int next;
+      int index;
+      Scalar value;
+    };
+
+    // used to store data in both mode
+    Scalar* m_buffer;
+    int m_size;
+    int m_start;
+    int m_end;
+    int m_allocatedSize;
+    int m_allocatedElements;
+    int m_mode;
+
+    // linked list mode
+    int m_llStart;
+    int m_llCurrent;
+    int m_llSize;
+
+  private:
+    AmbiVector(const AmbiVector&);
+
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template<typename Scalar>
+void AmbiVector<Scalar>::nonZeros() const
+{
+  if (m_mode==IsSparse)
+    return m_llSize;
+  else
+    return m_end - m_start;
+}
+
+template<typename Scalar>
+void AmbiVector<Scalar>::init(RealScalar estimatedDensity)
+{
+  if (estimatedDensity>0.1)
+    init(IsDense);
+  else
+    init(IsSparse);
+}
+
+template<typename Scalar>
+void AmbiVector<Scalar>::init(int mode)
+{
+  m_mode = mode;
+  if (m_mode==IsSparse)
+  {
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+/** Must be called whenever we might perform a write access
+  * with an index smaller than the previous one.
+  *
+  * Don't worry, this function is extremely cheap.
+  */
+template<typename Scalar>
+void AmbiVector<Scalar>::restart()
+{
+  m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template<typename Scalar>
+void AmbiVector<Scalar>::setZero()
+{
+  if (m_mode==IsDense)
+  {
+    for (int i=m_start; i<m_end; ++i)
+      m_buffer[i] = Scalar(0);
+  }
+  else
+  {
+    ei_assert(m_mode==IsSparse);
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+template<typename Scalar>
+Scalar& AmbiVector<Scalar>::coeffRef(int i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    // TODO factorize the following code to reduce code generation
+    ei_assert(m_mode==IsSparse);
+    if (m_llSize==0)
+    {
+      // this is the first element
+      m_llStart = 0;
+      m_llCurrent = 0;
+      ++m_llSize;
+      llElements[0].value = Scalar(0);
+      llElements[0].index = i;
+      llElements[0].next = -1;
+      return llElements[0].value;
+    }
+    else if (i<llElements[m_llStart].index)
+    {
+      // this is going to be the new first element of the list
+      ListEl& el = llElements[m_llSize];
+      el.value = Scalar(0);
+      el.index = i;
+      el.next = m_llStart;
+      m_llStart = m_llSize;
+      ++m_llSize;
+      m_llCurrent = m_llStart;
+      return el.value;
+    }
+    else
+    {
+      int nextel = llElements[m_llCurrent].next;
+      ei_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
+      while (nextel >= 0 && llElements[nextel].index<=i)
+      {
+        m_llCurrent = nextel;
+        nextel = llElements[nextel].next;
+      }
+
+      if (llElements[m_llCurrent].index==i)
+      {
+        // the coefficient already exists and we found it !
+        return llElements[m_llCurrent].value;
+      }
+      else
+      {
+        if (m_llSize>=m_allocatedElements)
+          reallocateSparse();
+        ei_internal_assert(m_llSize<m_size && "internal error: overflow in sparse mode");
+        // let's insert a new coefficient
+        ListEl& el = llElements[m_llSize];
+        el.value = Scalar(0);
+        el.index = i;
+        el.next = llElements[m_llCurrent].next;
+        llElements[m_llCurrent].next = m_llSize;
+        ++m_llSize;
+        return el.value;
+      }
+    }
+  }
+}
+
+template<typename Scalar>
+Scalar AmbiVector<Scalar>::coeff(int i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    ei_assert(m_mode==IsSparse);
+    if ((m_llSize==0) || (i<llElements[m_llStart].index))
+    {
+      return Scalar(0);
+    }
+    else
+    {
+      int elid = m_llStart;
+      while (elid >= 0 && llElements[elid].index<i)
+        elid = llElements[elid].next;
+
+      if (llElements[elid].index==i)
+        return llElements[m_llCurrent].value;
+      else
+        return Scalar(0);
+    }
+  }
+}
+
+/** Iterator over the nonzero coefficients */
+template<typename _Scalar>
+class AmbiVector<_Scalar>::Iterator
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** Default constructor
+      * \param vec the vector on which we iterate
+      * \param epsilon the minimal value used to prune zero coefficients.
+      * In practice, all coefficients having a magnitude smaller than \a epsilon
+      * are skipped.
+      */
+    Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*precision<RealScalar>())
+      : m_vector(vec)
+    {
+      m_epsilon = epsilon;
+      m_isDense = m_vector.m_mode==IsDense;
+      if (m_isDense)
+      {
+        m_cachedIndex = m_vector.m_start-1;
+        ++(*this);
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        m_currentEl = m_vector.m_llStart;
+        while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon)
+          m_currentEl = llElements[m_currentEl].next;
+        if (m_currentEl<0)
+        {
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+    }
+
+    int index() const { return m_cachedIndex; }
+    Scalar value() const { return m_cachedValue; }
+
+    operator bool() const { return m_cachedIndex>=0; }
+
+    Iterator& operator++()
+    {
+      if (m_isDense)
+      {
+        do {
+          ++m_cachedIndex;
+        } while (m_cachedIndex<m_vector.m_end && ei_abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
+        if (m_cachedIndex<m_vector.m_end)
+          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+        else
+          m_cachedIndex=-1;
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        do {
+          m_currentEl = llElements[m_currentEl].next;
+        } while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon);
+        if (m_currentEl<0)
+        {
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+      return *this;
+    }
+
+  protected:
+    const AmbiVector& m_vector; // the target vector
+    int m_currentEl;            // the current element in sparse/linked-list mode
+    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
+    int m_cachedIndex;          // current coordinate
+    Scalar m_cachedValue;       // current value
+    bool m_isDense;             // mode of the vector
+};
+
+
+#endif // EIGEN_AMBIVECTOR_H

Eigen/src/Sparse/CholmodSupport.h

@@ -0,0 +1,240 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_CHOLMODSUPPORT_H
+#define EIGEN_CHOLMODSUPPORT_H
+
+template<typename Scalar, typename CholmodType>
+void ei_cholmod_configure_matrix(CholmodType& mat)
+{
+  if (ei_is_same_type<Scalar,float>::ret)
+  {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = 1;
+  }
+  else if (ei_is_same_type<Scalar,double>::ret)
+  {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = 0;
+  }
+  else if (ei_is_same_type<Scalar,std::complex<float> >::ret)
+  {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = 1;
+  }
+  else if (ei_is_same_type<Scalar,std::complex<double> >::ret)
+  {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = 0;
+  }
+  else
+  {
+    ei_assert(false && "Scalar type not supported by CHOLMOD");
+  }
+}
+
+template<typename Scalar, int Flags>
+cholmod_sparse SparseMatrix<Scalar,Flags>::asCholmodMatrix()
+{
+  cholmod_sparse res;
+  res.nzmax   = nonZeros();
+  res.nrow    = rows();;
+  res.ncol    = cols();
+  res.p       = _outerIndexPtr();
+  res.i       = _innerIndexPtr();
+  res.x       = _valuePtr();
+  res.xtype = CHOLMOD_REAL;
+  res.itype = CHOLMOD_INT;
+  res.sorted = 1;
+  res.packed = 1;
+  res.dtype = 0;
+  res.stype = -1;
+
+  ei_cholmod_configure_matrix<Scalar>(res);
+
+  if (Flags & SelfAdjoint)
+  {
+    if (Flags & UpperTriangular)
+      res.stype = 1;
+    else if (Flags & LowerTriangular)
+      res.stype = -1;
+    else
+      res.stype = 0;
+  }
+  else
+    res.stype = 0;
+
+  return res;
+}
+
+template<typename Derived>
+cholmod_dense ei_cholmod_map_eigen_to_dense(MatrixBase<Derived>& mat)
+{
+  EIGEN_STATIC_ASSERT((ei_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      = mat.derived().stride();
+  res.x      = mat.derived().data();
+  res.z      = 0;
+
+  ei_cholmod_configure_matrix<Scalar>(res);
+
+  return res;
+}
+
+template<typename Scalar, int Flags>
+SparseMatrix<Scalar,Flags> SparseMatrix<Scalar,Flags>::Map(cholmod_sparse& cm)
+{
+  SparseMatrix res;
+  res.m_innerSize = cm.nrow;
+  res.m_outerSize = cm.ncol;
+  res.m_outerIndex = reinterpret_cast<int*>(cm.p);
+  SparseArray<Scalar> data = SparseArray<Scalar>::Map(
+                                reinterpret_cast<int*>(cm.i),
+                                reinterpret_cast<Scalar*>(cm.x),
+                                res.m_outerIndex[cm.ncol]);
+  res.m_data.swap(data);
+  res.markAsRValue();
+  return res;
+}
+
+template<typename MatrixType>
+class SparseLLT<MatrixType,Cholmod> : public SparseLLT<MatrixType>
+{
+  protected:
+    typedef SparseLLT<MatrixType> Base;
+    using typename Base::Scalar;
+    using Base::RealScalar;
+    using Base::MatrixLIsDirty;
+    using Base::SupernodalFactorIsDirty;
+    using Base::m_flags;
+    using Base::m_matrix;
+    using Base::m_status;
+
+  public:
+
+    SparseLLT(int flags = 0)
+      : Base(flags), m_cholmodFactor(0)
+    {
+      cholmod_start(&m_cholmod);
+    }
+
+    SparseLLT(const MatrixType& matrix, int flags = 0)
+      : Base(flags), m_cholmodFactor(0)
+    {
+      cholmod_start(&m_cholmod);
+      compute(matrix);
+    }
+
+    ~SparseLLT()
+    {
+      if (m_cholmodFactor)
+        cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
+      cholmod_finish(&m_cholmod);
+    }
+
+    inline const typename Base::CholMatrixType& matrixL(void) const;
+
+    template<typename Derived>
+    void solveInPlace(MatrixBase<Derived> &b) const;
+
+    void compute(const MatrixType& matrix);
+
+  protected:
+    mutable cholmod_common m_cholmod;
+    cholmod_factor* m_cholmodFactor;
+};
+
+template<typename MatrixType>
+void SparseLLT<MatrixType,Cholmod>::compute(const MatrixType& a)
+{
+  if (m_cholmodFactor)
+  {
+    cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
+    m_cholmodFactor = 0;
+  }
+
+  cholmod_sparse A = const_cast<MatrixType&>(a).asCholmodMatrix();
+  m_cholmod.supernodal = CHOLMOD_AUTO;
+  // TODO
+  if (m_flags&IncompleteFactorization)
+  {
+    m_cholmod.nmethods = 1;
+    m_cholmod.method[0].ordering = CHOLMOD_NATURAL;
+    m_cholmod.postorder = 0;
+  }
+  else
+  {
+    m_cholmod.nmethods = 1;
+    m_cholmod.method[0].ordering = CHOLMOD_NATURAL;
+    m_cholmod.postorder = 0;
+  }
+  m_cholmod.final_ll = 1;
+  m_cholmodFactor = cholmod_analyze(&A, &m_cholmod);
+  cholmod_factorize(&A, m_cholmodFactor, &m_cholmod);
+
+  m_status = (m_status & ~SupernodalFactorIsDirty) | MatrixLIsDirty;
+}
+
+template<typename MatrixType>
+inline const typename SparseLLT<MatrixType>::CholMatrixType&
+SparseLLT<MatrixType,Cholmod>::matrixL() const
+{
+  if (m_status & MatrixLIsDirty)
+  {
+    ei_assert(!(m_status & SupernodalFactorIsDirty));
+
+    cholmod_sparse* cmRes = cholmod_factor_to_sparse(m_cholmodFactor, &m_cholmod);
+    const_cast<typename Base::CholMatrixType&>(m_matrix) = Base::CholMatrixType::Map(*cmRes);
+    free(cmRes);
+
+    m_status = (m_status & ~MatrixLIsDirty);
+  }
+  return m_matrix;
+}
+
+template<typename MatrixType>
+template<typename Derived>
+void SparseLLT<MatrixType,Cholmod>::solveInPlace(MatrixBase<Derived> &b) const
+{
+  const int size = m_cholmodFactor->n;
+  ei_assert(size==b.rows());
+
+  // this uses Eigen's triangular sparse solver
+//   if (m_status & MatrixLIsDirty)
+//     matrixL();
+//   Base::solveInPlace(b);
+  // as long as our own triangular sparse solver is not fully optimal,
+  // let's use CHOLMOD's one:
+  cholmod_dense cdb = ei_cholmod_map_eigen_to_dense(b);
+  cholmod_dense* x = cholmod_solve(CHOLMOD_LDLt, m_cholmodFactor, &cdb, &m_cholmod);
+  b = Matrix<typename Base::Scalar,Dynamic,1>::Map(reinterpret_cast<typename Base::Scalar*>(x->x),b.rows());
+  cholmod_free_dense(&x, &m_cholmod);
+}
+
+#endif // EIGEN_CHOLMODSUPPORT_H

Eigen/src/Sparse/RandomSetter.h

@@ -0,0 +1,257 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_RANDOMSETTER_H
+#define EIGEN_RANDOMSETTER_H
+
+template<typename Scalar> struct StdMapTraits
+{
+  typedef int KeyType;
+  typedef std::map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 1
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+
+#ifdef _HASH_MAP
+template<typename Scalar> struct GnuHashMapTraits
+{
+  typedef int KeyType;
+  typedef __gnu_cxx::hash_map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+#endif
+
+#ifdef _DENSE_HASH_MAP_H_
+template<typename Scalar> struct GoogleDenseHashMapTraits
+{
+  typedef int KeyType;
+  typedef google::dense_hash_map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type& map, const KeyType& k)
+  { map.set_empty_key(k); }
+};
+#endif
+
+#ifdef _SPARSE_HASH_MAP_H_
+template<typename Scalar> struct GoogleSparseHashMapTraits
+{
+  typedef int KeyType;
+  typedef google::sparse_hash_map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+#endif
+
+/** \class RandomSetter
+  *
+  * Typical usage:
+  * \code
+  * SparseMatrix<double> m(rows,cols);
+  * {
+  *   RandomSetter<SparseMatrix<double> > w(m);
+  *   // don't use m but w instead with read/write random access to the coefficients:
+  *   for(;;)
+  *     w(rand(),rand()) = rand;
+  * }
+  * // when w is deleted, the data are copied back to m
+  * // and m is ready to use.
+  * \endcode
+  *
+  * \note for performance and memory consumption reasons it is highly recommended to use
+  * Google's hash library. To do so you have two options:
+  *  - include <google/dense_hash_map> yourself \b before Eigen/Sparse header
+  *  - define EIGEN_GOOGLEHASH_SUPPORT
+  * In the later case the inclusion of <google/dense_hash_map> is made for you.
+  */
+template<typename SparseMatrixType,
+         template <typename T> class MapTraits =
+#if defined _DENSE_HASH_MAP_H_
+          GoogleDenseHashMapTraits
+#elif defined _HASH_MAP
+          GnuHashMapTraits
+#else
+          StdMapTraits
+#endif
+         ,int OuterPacketBits = 6>
+class RandomSetter
+{
+    typedef typename ei_traits<SparseMatrixType>::Scalar Scalar;
+    struct ScalarWrapper
+    {
+      ScalarWrapper() : value(0) {}
+      Scalar value;
+    };
+    typedef typename MapTraits<ScalarWrapper>::KeyType KeyType;
+    typedef typename MapTraits<ScalarWrapper>::Type HashMapType;
+    static const int OuterPacketMask = (1 << OuterPacketBits) - 1;
+    enum {
+      SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
+      TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
+      SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor
+    };
+
+  public:
+
+    inline RandomSetter(SparseMatrixType& target)
+      : mp_target(&target)
+    {
+      const int outerSize = SwapStorage ? target.innerSize() : target.outerSize();
+      const int innerSize = SwapStorage ? target.outerSize() : target.innerSize();
+      m_outerPackets = outerSize >> OuterPacketBits;
+      if (outerSize&OuterPacketMask)
+        m_outerPackets += 1;
+      m_hashmaps = new HashMapType[m_outerPackets];
+      // compute number of bits needed to store inner indices
+      int aux = innerSize - 1;
+      m_keyBitsOffset = 0;
+      while (aux)
+      {
+        ++m_keyBitsOffset;
+        aux = aux >> 1;
+      }
+      KeyType ik = (1<<(OuterPacketBits+m_keyBitsOffset));
+      for (int k=0; k<m_outerPackets; ++k)
+        MapTraits<ScalarWrapper>::setInvalidKey(m_hashmaps[k],ik);
+
+      // insert current coeffs
+      for (int j=0; j<mp_target->outerSize(); ++j)
+        for (typename SparseMatrixType::InnerIterator it(*mp_target,j); it; ++it)
+          (*this)(TargetRowMajor?j:it.index(), TargetRowMajor?it.index():j) = it.value();
+    }
+
+    ~RandomSetter()
+    {
+      KeyType keyBitsMask = (1<<m_keyBitsOffset)-1;
+      if (!SwapStorage) // also means the map is sorted
+      {
+        mp_target->startFill(nonZeros());
+        for (int k=0; k<m_outerPackets; ++k)
+        {
+          const int outerOffset = (1<<OuterPacketBits) * k;
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const int outer = (it->first >> m_keyBitsOffset) + outerOffset;
+            const int inner = it->first & keyBitsMask;
+            mp_target->fill(TargetRowMajor ? outer : inner, TargetRowMajor ? inner : outer) = it->second.value;
+          }
+        }
+        mp_target->endFill();
+      }
+      else
+      {
+        VectorXi positions(mp_target->outerSize());
+        positions.setZero();
+        // pass 1
+        for (int k=0; k<m_outerPackets; ++k)
+        {
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const int outer = it->first & keyBitsMask;
+            ++positions[outer];
+          }
+        }
+        // prefix sum
+        int count = 0;
+        for (int j=0; j<mp_target->outerSize(); ++j)
+        {
+          int tmp = positions[j];
+          mp_target->_outerIndexPtr()[j] = count;
+          positions[j] = count;
+          count += tmp;
+        }
+        mp_target->_outerIndexPtr()[mp_target->outerSize()] = count;
+        mp_target->resizeNonZeros(count);
+        // pass 2
+        for (int k=0; k<m_outerPackets; ++k)
+        {
+          const int outerOffset = (1<<OuterPacketBits) * k;
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const int inner = (it->first >> m_keyBitsOffset) + outerOffset;
+            const int outer = it->first & keyBitsMask;
+            // sorted insertion
+            // Note that we have to deal with at most 2^OuterPacketBits unsorted coefficients,
+            // moreover those 2^OuterPacketBits coeffs are likely to be sparse, an so only a
+            // small fraction of them have to be sorted, whence the following simple procedure:
+            int posStart = mp_target->_outerIndexPtr()[outer];
+            int i = (positions[outer]++) - 1;
+            while ( (i >= posStart) && (mp_target->_innerIndexPtr()[i] > inner) )
+            {
+              mp_target->_valuePtr()[i+1] = mp_target->_valuePtr()[i];
+              mp_target->_innerIndexPtr()[i+1] = mp_target->_innerIndexPtr()[i];
+              --i;
+            }
+            mp_target->_innerIndexPtr()[i+1] = inner;
+            mp_target->_valuePtr()[i+1] = it->second.value;
+          }
+        }
+      }
+      delete[] m_hashmaps;
+    }
+
+    Scalar& operator() (int row, int col)
+    {
+      const int outer = SetterRowMajor ? row : col;
+      const int inner = SetterRowMajor ? col : row;
+      const int outerMajor = outer >> OuterPacketBits; // index of the packet/map
+      const int outerMinor = outer & OuterPacketMask;  // index of the inner vector in the packet
+      const KeyType key = (KeyType(outerMinor)<<m_keyBitsOffset) | inner;
+      return m_hashmaps[outerMajor][key].value;
+    }
+
+    // might be slow
+    int nonZeros() const
+    {
+      int nz = 0;
+      for (int k=0; k<m_outerPackets; ++k)
+        nz += m_hashmaps[k].size();
+      return nz;
+    }
+
+
+  protected:
+
+    HashMapType* m_hashmaps;
+    SparseMatrixType* mp_target;
+    int m_outerPackets;
+    unsigned char m_keyBitsOffset;
+};
+
+#endif // EIGEN_RANDOMSETTER_H

Eigen/src/Sparse/SparseArray.h

@@ -28,7 +28,8 @@
 /** Stores a sparse set of values as a list of values and a list of indices.
   *
   */
-template<typename Scalar> class SparseArray
+template<typename Scalar>
+class SparseArray
 {
   public:
     SparseArray()
@@ -105,6 +106,15 @@ template<typename Scalar> class SparseArray
     int& index(int i) { return m_indices[i]; }
     const int& index(int i) const { return m_indices[i]; }
 
+    static SparseArray Map(int* indices, Scalar* values, int size)
+    {
+      SparseArray res;
+      res.m_indices = indices;
+      res.m_values = values;
+      res.m_allocatedSize = res.m_size = size;
+      return res;
+    }
+
   protected:
 
     void reallocate(int size)

Eigen/src/Sparse/SparseBlock.h

@@ -59,7 +59,7 @@ public:
       : m_matrix(matrix), m_startRow(startRow), m_startCol(startCol),
         m_blockRows(matrix.rows()), m_blockCols(matrix.cols())
     {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && RowsAtCompileTime!=Dynamic,this_method_is_only_for_fixed_size);
+      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && RowsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
       ei_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= matrix.rows()
           && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= matrix.cols());
     }

Eigen/src/Sparse/SparseLDLT.h

@@ -0,0 +1,346 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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/>.
+
+/*
+
+NOTE: the _symbolic, and _numeric functions has been adapted from
+      the LDL library:
+
+LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
+
+LDL License:
+
+    Your use or distribution of LDL or any modified version of
+    LDL implies that you agree to this License.
+
+    This library 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 2.1 of the License, or (at your option) any later version.
+
+    This library 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 for more details.
+
+    You should have received a copy of the GNU Lesser General Public
+    License along with this library; if not, write to the Free Software
+    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301
+    USA
+
+    Permission is hereby granted to use or copy this program under the
+    terms of the GNU LGPL, provided that the Copyright, this License,
+    and the Availability of the original version is retained on all copies.
+    User documentation of any code that uses this code or any modified
+    version of this code must cite the Copyright, this License, the
+    Availability note, and "Used by permission." Permission to modify
+    the code and to distribute modified code is granted, provided the
+    Copyright, this License, and the Availability note are retained,
+    and a notice that the code was modified is included.
+ */
+
+#ifndef EIGEN_SPARSELDLT_H
+#define EIGEN_SPARSELDLT_H
+
+/** \ingroup Sparse_Module
+  *
+  * \class SparseLDLT
+  *
+  * \brief LDLT Cholesky decomposition of a sparse matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LDLT Cholesky decomposition
+  *
+  * \sa class LDLT, class LDLT
+  */
+template<typename MatrixType, int Backend = DefaultBackend>
+class SparseLDLT
+{
+  protected:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef SparseMatrix<Scalar,LowerTriangular|UnitDiagBit> CholMatrixType;
+    typedef Matrix<Scalar,MatrixType::ColsAtCompileTime,1> VectorType;
+
+    enum {
+      SupernodalFactorIsDirty      = 0x10000,
+      MatrixLIsDirty               = 0x20000
+    };
+
+  public:
+
+    /** Creates a dummy LDLT factorization object with flags \a flags. */
+    SparseLDLT(int flags = 0)
+      : m_flags(flags), m_status(0)
+    {
+      ei_assert((MatrixType::Flags&RowMajorBit)==0);
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+    }
+
+    /** Creates a LDLT object and compute the respective factorization of \a matrix using
+      * flags \a flags. */
+    SparseLDLT(const MatrixType& matrix, int flags = 0)
+      : m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0)
+    {
+      ei_assert((MatrixType::Flags&RowMajorBit)==0);
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+      compute(matrix);
+    }
+
+    /** Sets the relative threshold value used to prune zero coefficients during the decomposition.
+      *
+      * Setting a value greater than zero speeds up computation, and yields to an imcomplete
+      * factorization with fewer non zero coefficients. Such approximate factors are especially
+      * useful to initialize an iterative solver.
+      *
+      * \warning if precision is greater that zero, the LDLT factorization is not guaranteed to succeed
+      * even if the matrix is positive definite.
+      *
+      * Note that the exact meaning of this parameter might depends on the actual
+      * backend. Moreover, not all backends support this feature.
+      *
+      * \sa precision() */
+    void setPrecision(RealScalar v) { m_precision = v; }
+
+    /** \returns the current precision.
+      *
+      * \sa setPrecision() */
+    RealScalar precision() const { return m_precision; }
+
+    /** Sets the flags. Possible values are:
+      *  - CompleteFactorization
+      *  - IncompleteFactorization
+      *  - MemoryEfficient          (hint to use the memory most efficient method offered by the backend)
+      *  - SupernodalMultifrontal   (implies a complete factorization if supported by the backend,
+      *                              overloads the MemoryEfficient flags)
+      *  - SupernodalLeftLooking    (implies a complete factorization  if supported by the backend,
+      *                              overloads the MemoryEfficient flags)
+      *
+      * \sa flags() */
+    void settagss(int f) { m_flags = f; }
+    /** \returns the current flags */
+    int flags() const { return m_flags; }
+
+    /** Computes/re-computes the LDLT factorization */
+    void compute(const MatrixType& matrix);
+
+    /** Perform a symbolic factorization */
+    void _symbolic(const MatrixType& matrix);
+    /** Perform the actual factorization using the previously
+      * computed symbolic factorization */
+    bool _numeric(const MatrixType& matrix);
+
+    /** \returns the lower triangular matrix L */
+    inline const CholMatrixType& matrixL(void) const { return m_matrix; }
+
+    /** \returns the coefficients of the diagonal matrix D */
+    inline VectorType vectorD(void) const { return m_diag; }
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &b) const;
+
+    /** \returns true if the factorization succeeded */
+    inline bool succeeded(void) const { return m_succeeded; }
+
+  protected:
+    CholMatrixType m_matrix;
+    VectorType m_diag;
+    VectorXi m_parent; // elimination tree
+    VectorXi m_nonZerosPerCol;
+//     VectorXi m_w; // workspace
+    RealScalar m_precision;
+    int m_flags;
+    mutable int m_status;
+    bool m_succeeded;
+};
+
+/** Computes / recomputes the LDLT decomposition of matrix \a a
+  * using the default algorithm.
+  */
+template<typename MatrixType, int Backend>
+void SparseLDLT<MatrixType,Backend>::compute(const MatrixType& a)
+{
+  _symbolic(a);
+  m_succeeded = _numeric(a);
+}
+
+template<typename MatrixType, int Backend>
+void SparseLDLT<MatrixType,Backend>::_symbolic(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
+  const int size = a.rows();
+  m_matrix.resize(size, size);
+  m_parent.resize(size);
+  m_nonZerosPerCol.resize(size);
+  int * tags = ei_aligned_stack_alloc(int, size);
+
+  const int* Ap = a._outerIndexPtr();
+  const int* Ai = a._innerIndexPtr();
+  int* Lp = m_matrix._outerIndexPtr();
+  const int* P = 0;
+  int* Pinv = 0;
+
+  if (P)
+  {
+    /* If P is present then compute Pinv, the inverse of P */
+    for (int k = 0; k < size; ++k)
+      Pinv[P[k]] = k;
+  }
+  for (int k = 0; k < size; ++k)
+  {
+    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
+    m_parent[k] = -1;             /* parent of k is not yet known */
+    tags[k] = k;                  /* mark node k as visited */
+    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
+    int kk = P ? P[k] : k;  /* kth original, or permuted, column */
+    int p2 = Ap[kk+1];
+    for (int p = Ap[kk]; p < p2; ++p)
+    {
+      /* A (i,k) is nonzero (original or permuted A) */
+      int i = Pinv ? Pinv[Ai[p]] : Ai[p];
+      if (i < k)
+      {
+        /* follow path from i to root of etree, stop at flagged node */
+        for (; tags[i] != k; i = m_parent[i])
+        {
+          /* find parent of i if not yet determined */
+          if (m_parent[i] == -1)
+            m_parent[i] = k;
+          ++m_nonZerosPerCol[i];        /* L (k,i) is nonzero */
+          tags[i] = k;                  /* mark i as visited */
+        }
+      }
+    }
+  }
+  /* construct Lp index array from m_nonZerosPerCol column counts */
+  Lp[0] = 0;
+  for (int k = 0; k < size; ++k)
+    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k];
+
+  m_matrix.resizeNonZeros(Lp[size]);
+  ei_aligned_stack_free(tags, int, size);
+}
+
+template<typename MatrixType, int Backend>
+bool SparseLDLT<MatrixType,Backend>::_numeric(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
+  const int size = a.rows();
+  assert(m_parent.size()==size);
+  assert(m_nonZerosPerCol.size()==size);
+
+  const int* Ap = a._outerIndexPtr();
+  const int* Ai = a._innerIndexPtr();
+  const Scalar* Ax = a._valuePtr();
+  const int* Lp = m_matrix._outerIndexPtr();
+  int* Li = m_matrix._innerIndexPtr();
+  Scalar* Lx = m_matrix._valuePtr();
+  m_diag.resize(size);
+
+  Scalar * y = ei_aligned_stack_alloc(Scalar, size);
+  int * pattern = ei_aligned_stack_alloc(int, size);
+  int * tags = ei_aligned_stack_alloc(int, size);
+
+  const int* P = 0;
+  const int* Pinv = 0;
+  bool ok = true;
+
+  for (int k = 0; k < size; ++k)
+  {
+    /* compute nonzero pattern of kth row of L, in topological order */
+    y[k] = 0.0;                   /* Y(0:k) is now all zero */
+    int top = size;               /* stack for pattern is empty */
+    tags[k] = k;                  /* mark node k as visited */
+    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
+    int kk = (P) ? (P[k]) : (k);  /* kth original, or permuted, column */
+    int p2 = Ap[kk+1];
+    for (int p = Ap[kk]; p < p2; ++p)
+    {
+      int i = Pinv ? Pinv[Ai[p]] : Ai[p]; /* get A(i,k) */
+      if (i <= k)
+      {
+        y[i] += Ax[p];            /* scatter A(i,k) into Y (sum duplicates) */
+        int len;
+        for (len = 0; tags[i] != k; i = m_parent[i])
+        {
+          pattern[len++] = i;     /* L(k,i) is nonzero */
+          tags[i] = k;            /* mark i as visited */
+        }
+        while (len > 0)
+          pattern[--top] = pattern[--len];
+      }
+    }
+    /* compute numerical values kth row of L (a sparse triangular solve) */
+    m_diag[k] = y[k];                       /* get D(k,k) and clear Y(k) */
+    y[k] = 0.0;
+    for (; top < size; ++top)
+    {
+      int i = pattern[top];      /* pattern[top:n-1] is pattern of L(:,k) */
+      Scalar yi = y[i];          /* get and clear Y(i) */
+      y[i] = 0.0;
+      int p2 = Lp[i] + m_nonZerosPerCol[i];
+      int p;
+      for (p = Lp[i]; p < p2; ++p)
+        y[Li[p]] -= Lx[p] * yi;
+      Scalar l_ki = yi / m_diag[i];       /* the nonzero entry L(k,i) */
+      m_diag[k] -= l_ki * yi;
+      Li[p] = k;                          /* store L(k,i) in column form of L */
+      Lx[p] = l_ki;
+      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
+    }
+    if (m_diag[k] == 0.0)
+    {
+      ok = false;                         /* failure, D(k,k) is zero */
+      break;
+    }
+  }
+
+  ei_aligned_stack_free(y, Scalar, size);
+  ei_aligned_stack_free(pattern, int, size);
+  ei_aligned_stack_free(tags, int, size);
+
+  return ok;  /* success, diagonal of D is all nonzero */
+}
+
+/** Computes b = L^-T L^-1 b */
+template<typename MatrixType, int Backend>
+template<typename Derived>
+bool SparseLDLT<MatrixType, Backend>::solveInPlace(MatrixBase<Derived> &b) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows());
+  if (!m_succeeded)
+    return false;
+
+  if (m_matrix.nonZeros()>0) // otherwise L==I
+    m_matrix.solveTriangularInPlace(b);
+  b = b.cwise() / m_diag;
+  // FIXME should be .adjoint() but it fails to compile...
+
+  if (m_matrix.nonZeros()>0) // otherwise L==I
+  m_matrix.transpose().solveTriangularInPlace(b);
+
+  return true;
+}
+
+#endif // EIGEN_SPARSELDLT_H

Eigen/src/Sparse/SparseLLT.h

@@ -0,0 +1,205 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_SPARSELLT_H
+#define EIGEN_SPARSELLT_H
+
+/** \ingroup Sparse_Module
+  *
+  * \class SparseLLT
+  *
+  * \brief LLT Cholesky decomposition of a sparse matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LLT Cholesky decomposition
+  *
+  * \sa class LLT, class LDLT
+  */
+template<typename MatrixType, int Backend = DefaultBackend>
+class SparseLLT
+{
+  protected:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef SparseMatrix<Scalar,LowerTriangular> CholMatrixType;
+
+    enum {
+      SupernodalFactorIsDirty      = 0x10000,
+      MatrixLIsDirty               = 0x20000
+    };
+
+  public:
+
+    /** Creates a dummy LLT factorization object with flags \a flags. */
+    SparseLLT(int flags = 0)
+      : m_flags(flags), m_status(0)
+    {
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+    }
+
+    /** Creates a LLT object and compute the respective factorization of \a matrix using
+      * flags \a flags. */
+    SparseLLT(const MatrixType& matrix, int flags = 0)
+      : m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0)
+    {
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+      compute(matrix);
+    }
+
+    /** Sets the relative threshold value used to prune zero coefficients during the decomposition.
+      *
+      * Setting a value greater than zero speeds up computation, and yields to an imcomplete
+      * factorization with fewer non zero coefficients. Such approximate factors are especially
+      * useful to initialize an iterative solver.
+      *
+      * \warning if precision is greater that zero, the LLT factorization is not guaranteed to succeed
+      * even if the matrix is positive definite.
+      *
+      * Note that the exact meaning of this parameter might depends on the actual
+      * backend. Moreover, not all backends support this feature.
+      *
+      * \sa precision() */
+    void setPrecision(RealScalar v) { m_precision = v; }
+
+    /** \returns the current precision.
+      *
+      * \sa setPrecision() */
+    RealScalar precision() const { return m_precision; }
+
+    /** Sets the flags. Possible values are:
+      *  - CompleteFactorization
+      *  - IncompleteFactorization
+      *  - MemoryEfficient          (hint to use the memory most efficient method offered by the backend)
+      *  - SupernodalMultifrontal   (implies a complete factorization if supported by the backend,
+      *                              overloads the MemoryEfficient flags)
+      *  - SupernodalLeftLooking    (implies a complete factorization  if supported by the backend,
+      *                              overloads the MemoryEfficient flags)
+      *
+      * \sa flags() */
+    void setFlags(int f) { m_flags = f; }
+    /** \returns the current flags */
+    int flags() const { return m_flags; }
+
+    /** Computes/re-computes the LLT factorization */
+    void compute(const MatrixType& matrix);
+
+    /** \returns the lower triangular matrix L */
+    inline const CholMatrixType& matrixL(void) const { return m_matrix; }
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &b) const;
+
+    /** \returns true if the factorization succeeded */
+    inline bool succeeded(void) const { return m_succeeded; }
+
+  protected:
+    CholMatrixType m_matrix;
+    RealScalar m_precision;
+    int m_flags;
+    mutable int m_status;
+    bool m_succeeded;
+};
+
+/** Computes / recomputes the LLT decomposition of matrix \a a
+  * using the default algorithm.
+  */
+template<typename MatrixType, int Backend>
+void SparseLLT<MatrixType,Backend>::compute(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
+  const int size = a.rows();
+  m_matrix.resize(size, size);
+
+  // allocate a temporary vector for accumulations
+  AmbiVector<Scalar> tempVector(size);
+  RealScalar density = a.nonZeros()/RealScalar(size*size);
+
+  // TODO estimate the number of non zeros
+  m_matrix.startFill(a.nonZeros()*2);
+  for (int j = 0; j < size; ++j)
+  {
+    Scalar x = ei_real(a.coeff(j,j));
+
+    // TODO better estimate of the density !
+    tempVector.init(density>0.001? IsDense : IsSparse);
+    tempVector.setBounds(j+1,size);
+    tempVector.setZero();
+    // init with current matrix a
+    {
+      typename MatrixType::InnerIterator it(a,j);
+      ++it; // skip diagonal element
+      for (; it; ++it)
+        tempVector.coeffRef(it.index()) = it.value();
+    }
+    for (int k=0; k<j+1; ++k)
+    {
+      typename CholMatrixType::InnerIterator it(m_matrix, k);
+      while (it && it.index()<j)
+        ++it;
+      if (it && it.index()==j)
+      {
+        Scalar y = it.value();
+        x -= ei_abs2(y);
+        ++it; // skip j-th element, and process remaining column coefficients
+        tempVector.restart();
+        for (; it; ++it)
+        {
+          tempVector.coeffRef(it.index()) -= it.value() * y;
+        }
+      }
+    }
+    // copy the temporary vector to the respective m_matrix.col()
+    // while scaling the result by 1/real(x)
+    RealScalar rx = ei_sqrt(ei_real(x));
+    m_matrix.fill(j,j) = rx;
+    Scalar y = Scalar(1)/rx;
+    for (typename AmbiVector<Scalar>::Iterator it(tempVector, m_precision*rx); it; ++it)
+    {
+      m_matrix.fill(it.index(), j) = it.value() * y;
+    }
+  }
+  m_matrix.endFill();
+}
+
+/** Computes b = L^-T L^-1 b */
+template<typename MatrixType, int Backend>
+template<typename Derived>
+bool SparseLLT<MatrixType, Backend>::solveInPlace(MatrixBase<Derived> &b) const
+{
+  const int size = m_matrix.rows();
+  ei_assert(size==b.rows());
+
+  m_matrix.solveTriangularInPlace(b);
+  // FIXME should be simply .adjoint() but it fails to compile...
+  if (NumTraits<Scalar>::IsComplex)
+  {
+    CholMatrixType aux = m_matrix.conjugate();
+    aux.transpose().solveTriangularInPlace(b);
+  }
+  else
+    m_matrix.transpose().solveTriangularInPlace(b);
+
+  return true;
+}
+
+#endif // EIGEN_SPARSELLT_H

Eigen/src/Sparse/SparseLU.h

@@ -0,0 +1,148 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_SPARSELU_H
+#define EIGEN_SPARSELU_H
+
+/** \ingroup Sparse_Module
+  *
+  * \class SparseLU
+  *
+  * \brief LU decomposition of a sparse matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LU factorization
+  *
+  * \sa class LU, class SparseLLT
+  */
+template<typename MatrixType, int Backend = DefaultBackend>
+class SparseLU
+{
+  protected:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef SparseMatrix<Scalar,LowerTriangular> LUMatrixType;
+
+    enum {
+      MatrixLUIsDirty             = 0x10000
+    };
+
+  public:
+
+    /** Creates a dummy LU factorization object with flags \a flags. */
+    SparseLU(int flags = 0)
+      : m_flags(flags), m_status(0)
+    {
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+    }
+
+    /** Creates a LU object and compute the respective factorization of \a matrix using
+      * flags \a flags. */
+    SparseLU(const MatrixType& matrix, int flags = 0)
+      : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0)
+    {
+      m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
+      compute(matrix);
+    }
+
+    /** Sets the relative threshold value used to prune zero coefficients during the decomposition.
+      *
+      * Setting a value greater than zero speeds up computation, and yields to an imcomplete
+      * factorization with fewer non zero coefficients. Such approximate factors are especially
+      * useful to initialize an iterative solver.
+      *
+      * Note that the exact meaning of this parameter might depends on the actual
+      * backend. Moreover, not all backends support this feature.
+      *
+      * \sa precision() */
+    void setPrecision(RealScalar v) { m_precision = v; }
+
+    /** \returns the current precision.
+      *
+      * \sa setPrecision() */
+    RealScalar precision() const { return m_precision; }
+
+    /** Sets the flags. Possible values are:
+      *  - CompleteFactorization
+      *  - IncompleteFactorization
+      *  - MemoryEfficient
+      *  - one of the ordering methods
+      *  - etc...
+      *
+      * \sa flags() */
+    void setFlags(int f) { m_flags = f; }
+    /** \returns the current flags */
+    int flags() const { return m_flags; }
+
+    void setOrderingMethod(int m)
+    {
+      ei_assert(m&~OrderingMask == 0 && m!=0 && "invalid ordering method");
+      m_flags = m_flags&~OrderingMask | m&OrderingMask;
+    }
+
+    int orderingMethod() const
+    {
+      return m_flags&OrderingMask;
+    }
+
+    /** Computes/re-computes the LU factorization */
+    void compute(const MatrixType& matrix);
+
+    /** \returns the lower triangular matrix L */
+    //inline const MatrixType& matrixL() const { return m_matrixL; }
+
+    /** \returns the upper triangular matrix U */
+    //inline const MatrixType& matrixU() const { return m_matrixU; }
+
+    template<typename BDerived, typename XDerived>
+    bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x) const;
+
+    /** \returns true if the factorization succeeded */
+    inline bool succeeded(void) const { return m_succeeded; }
+
+  protected:
+    RealScalar m_precision;
+    int m_flags;
+    mutable int m_status;
+    bool m_succeeded;
+};
+
+/** Computes / recomputes the LU decomposition of matrix \a a
+  * using the default algorithm.
+  */
+template<typename MatrixType, int Backend>
+void SparseLU<MatrixType,Backend>::compute(const MatrixType& a)
+{
+  ei_assert(false && "not implemented yet");
+}
+
+/** Computes *x = U^-1 L^-1 b */
+template<typename MatrixType, int Backend>
+template<typename BDerived, typename XDerived>
+bool SparseLU<MatrixType,Backend>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x) const
+{
+  ei_assert(false && "not implemented yet");
+  return false;
+}
+
+#endif // EIGEN_SPARSELU_H

Eigen/src/Sparse/SparseMatrix.h

@@ -65,6 +65,7 @@ class SparseMatrix
     enum {
       RowMajor = SparseBase::RowMajor
     };
+    typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(RowMajor?RowMajorBit:0)> TransposedSparseMatrix;
 
     int m_outerSize;
     int m_innerSize;
@@ -80,6 +81,15 @@ class SparseMatrix
     inline int outerSize() const { return m_outerSize; }
     inline int innerNonZeros(int j) const { return m_outerIndex[j+1]-m_outerIndex[j]; }
 
+    inline const Scalar* _valuePtr() const { return &m_data.value(0); }
+    inline Scalar* _valuePtr() { return &m_data.value(0); }
+
+    inline const int* _innerIndexPtr() const { return &m_data.index(0); }
+    inline int* _innerIndexPtr() { return &m_data.index(0); }
+
+    inline const int* _outerIndexPtr() const { return m_outerIndex; }
+    inline int* _outerIndexPtr() { return m_outerIndex; }
+
     inline Scalar coeff(int row, int col) const
     {
       const int outer = RowMajor ? row : col;
@@ -118,17 +128,33 @@ class SparseMatrix
 
     class InnerIterator;
 
+    inline void setZero()
+    {
+      m_data.clear();
+      //if (m_outerSize)
+      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(int));
+//       for (int i=0; i<m_outerSize; ++i)
+//         m_outerIndex[i] = 0;
+//       if (m_outerSize)
+//         m_outerIndex[i] = 0;
+    }
+
     /** \returns the number of non zero coefficients */
     inline int nonZeros() const  { return m_data.size(); }
 
+    /** Initializes the filling process of \c *this.
+      * \param reserveSize approximate number of nonzeros
+      * Note that the matrix \c *this is zero-ed.
+      */
     inline void startFill(int reserveSize = 1000)
     {
-      m_data.clear();
+//       std::cerr << this << " startFill\n";
+      setZero();
       m_data.reserve(reserveSize);
-      for (int i=0; i<=m_outerSize; ++i)
-        m_outerIndex[i] = 0;
     }
 
+    /**
+      */
     inline Scalar& fill(int row, int col)
     {
       const int outer = RowMajor ? row : col;
@@ -136,7 +162,8 @@ class SparseMatrix
 
       if (m_outerIndex[outer+1]==0)
       {
-        int i=col;
+        // we start a new inner vector
+        int i = outer;
         while (i>=0 && m_outerIndex[i]==0)
         {
           m_outerIndex[i] = m_data.size();
@@ -146,20 +173,62 @@ class SparseMatrix
       }
       assert(m_outerIndex[outer+1] == m_data.size());
       int id = m_outerIndex[outer+1];
-      m_outerIndex[outer+1]++;
+      ++m_outerIndex[outer+1];
 
       m_data.append(0, inner);
       return m_data.value(id);
     }
 
+    /** Like fill() but with random inner coordinates.
+      */
+    inline Scalar& fillrand(int row, int col)
+    {
+      const int outer = RowMajor ? row : col;
+      const int inner = RowMajor ? col : row;
+
+      if (m_outerIndex[outer+1]==0)
+      {
+        // we start a new inner vector
+        // nothing special to do here
+        int i = outer;
+        while (i>=0 && m_outerIndex[i]==0)
+        {
+          m_outerIndex[i] = m_data.size();
+          --i;
+        }
+        m_outerIndex[outer+1] = m_outerIndex[outer];
+      }
+//       std::cerr << this << "  " << outer << " " << inner << " - " << m_outerIndex[outer] << " " << m_outerIndex[outer+1] << "\n";
+      assert(m_outerIndex[outer+1] == m_data.size() && "invalid outer index");
+      int startId = m_outerIndex[outer];
+      int id = m_outerIndex[outer+1]-1;
+      ++m_outerIndex[outer+1];
+      m_data.resize(id+2);
+
+      while ( (id >= startId) && (m_data.index(id) > inner) )
+      {
+        m_data.index(id+1) = m_data.index(id);
+        m_data.value(id+1) = m_data.value(id);
+        --id;
+      }
+      m_data.index(id+1) = inner;
+      //return (m_data.value(id+1) = 0);
+      m_data.value(id+1) = 0;
+//       std::cerr << m_outerIndex[outer] << " " << m_outerIndex[outer+1] << "\n";
+      return m_data.value(id+1);
+    }
+
+//     inline void
+
     inline void endFill()
     {
+//       std::cerr << this << " endFill\n";
       int size = m_data.size();
       int i = m_outerSize;
       // find the last filled column
       while (i>=0 && m_outerIndex[i]==0)
         --i;
-      i++;
+      ++i;
       while (i<=m_outerSize)
       {
         m_outerIndex[i] = size;
@@ -169,6 +238,7 @@ class SparseMatrix
 
     void resize(int rows, int cols)
     {
+//       std::cerr << this << " resize " << rows << "x" << cols << "\n";
       const int outerSize = RowMajor ? rows : cols;
       m_innerSize = RowMajor ? cols : rows;
       m_data.clear();
@@ -179,6 +249,16 @@ class SparseMatrix
         m_outerSize = outerSize;
       }
     }
+    void resizeNonZeros(int size)
+    {
+      m_data.resize(size);
+    }
+
+    inline SparseMatrix()
+      : m_outerSize(-1), m_innerSize(0), m_outerIndex(0)
+    {
+      resize(0, 0);
+    }
 
     inline SparseMatrix(int rows, int cols)
       : m_outerSize(0), m_innerSize(0), m_outerIndex(0)
@@ -193,9 +273,15 @@ class SparseMatrix
       *this = other.derived();
     }
 
+    inline SparseMatrix(const SparseMatrix& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0)
+    {
+      *this = other.derived();
+    }
+
     inline void swap(SparseMatrix& other)
     {
-      EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
       std::swap(m_outerIndex, other.m_outerIndex);
       std::swap(m_innerSize, other.m_innerSize);
       std::swap(m_outerSize, other.m_outerSize);
@@ -204,6 +290,7 @@ class SparseMatrix
 
     inline SparseMatrix& operator=(const SparseMatrix& other)
     {
+//       std::cout << "SparseMatrix& operator=(const SparseMatrix& other)\n";
       if (other.isRValue())
       {
         swap(other.const_cast_derived());
@@ -211,8 +298,7 @@ class SparseMatrix
       else
       {
         resize(other.rows(), other.cols());
-        for (int j=0; j<=m_outerSize; ++j)
-          m_outerIndex[j] = other.m_outerIndex[j];
+        memcpy(m_outerIndex, other.m_outerIndex, (m_outerSize+1)*sizeof(int));
         m_data = other.m_data;
       }
       return *this;
@@ -221,21 +307,68 @@ class SparseMatrix
     template<typename OtherDerived>
     inline SparseMatrix& operator=(const MatrixBase<OtherDerived>& other)
     {
-      return SparseMatrixBase<SparseMatrix>::operator=(other.derived());
+      const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      if (needToTranspose)
+      {
+        // two passes algorithm:
+        //  1 - compute the number of coeffs per dest inner vector
+        //  2 - do the actual copy/eval
+        // Since each coeff of the rhs has to be evaluated twice, let's evauluate it if needed
+        typedef typename ei_nested<OtherDerived,2>::type OtherCopy;
+        OtherCopy otherCopy(other.derived());
+        typedef typename ei_cleantype<OtherCopy>::type _OtherCopy;
+
+        resize(other.rows(), other.cols());
+        Eigen::Map<VectorXi>(m_outerIndex,outerSize()).setZero();
+        // pass 1
+        // FIXME the above copy could be merged with that pass
+        for (int j=0; j<otherCopy.outerSize(); ++j)
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+            ++m_outerIndex[it.index()];
+
+        // prefix sum
+        int count = 0;
+        VectorXi positions(outerSize());
+        for (int j=0; j<outerSize(); ++j)
+        {
+          int tmp = m_outerIndex[j];
+          m_outerIndex[j] = count;
+          positions[j] = count;
+          count += tmp;
+        }
+        m_outerIndex[outerSize()] = count;
+        // alloc
+        m_data.resize(count);
+        // pass 2
+        for (int j=0; j<otherCopy.outerSize(); ++j)
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+          {
+            int pos = positions[it.index()]++;
+            m_data.index(pos) = j;
+            m_data.value(pos) = it.value();
+          }
+
+        return *this;
+      }
+      else
+      {
+        // there is no special optimization
+        return SparseMatrixBase<SparseMatrix>::operator=(other.derived());
+      }
     }
 
     friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
     {
       EIGEN_DBG_SPARSE(
         s << "Nonzero entries:\n";
-        for (uint i=0; i<m.nonZeros(); ++i)
+        for (unsigned int i=0; i<m.nonZeros(); ++i)
         {
           s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
         }
         s << std::endl;
         s << std::endl;
         s << "Column pointers:\n";
-        for (uint i=0; i<m.cols(); ++i)
+        for (unsigned int i=0; i<m.cols(); ++i)
         {
           s << m.m_outerIndex[i] << " ";
         }
@@ -246,6 +379,21 @@ class SparseMatrix
       return s;
     }
 
+    #ifdef EIGEN_TAUCS_SUPPORT
+    static SparseMatrix Map(taucs_ccs_matrix& taucsMatrix);
+    taucs_ccs_matrix asTaucsMatrix();
+    #endif
+
+    #ifdef EIGEN_CHOLMOD_SUPPORT
+    static SparseMatrix Map(cholmod_sparse& cholmodMatrix);
+    cholmod_sparse asCholmodMatrix();
+    #endif
+
+    #ifdef EIGEN_SUPERLU_SUPPORT
+    static SparseMatrix Map(SluMatrix& sluMatrix);
+    SluMatrix asSluMatrix();
+    #endif
+
     /** Destructor */
     inline ~SparseMatrix()
     {
@@ -267,13 +415,13 @@ class SparseMatrix<Scalar,_Flags>::InnerIterator
         m_start(m_id), m_end(m_matrix.m_outerIndex[outer+1])
     {}
 
-    InnerIterator& operator++() { m_id++; return *this; }
+    inline InnerIterator& operator++() { m_id++; return *this; }
 
-    Scalar value() const { return m_matrix.m_data.value(m_id); }
+    inline Scalar value() const { return m_matrix.m_data.value(m_id); }
 
-    int index() const { return m_matrix.m_data.index(m_id); }
+    inline int index() const { return m_matrix.m_data.index(m_id); }
 
-    operator bool() const { return (m_id < m_end) && (m_id>=m_start); }
+    inline operator bool() const { return (m_id < m_end) && (m_id>=m_start); }
 
   protected:
     const SparseMatrix& m_matrix;

Eigen/src/Sparse/SparseMatrixBase.h

@@ -32,6 +32,7 @@ class SparseMatrixBase : public MatrixBase<Derived>
 
     typedef MatrixBase<Derived> Base;
     typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
     enum {
       Flags = Base::Flags,
       RowMajor = ei_traits<Derived>::Flags&RowMajorBit ? 1 : 0
@@ -51,6 +52,7 @@ class SparseMatrixBase : public MatrixBase<Derived>
 
     inline Derived& operator=(const Derived& other)
     {
+//       std::cout << "Derived& operator=(const Derived& other)\n";
       if (other.isRValue())
         derived().swap(other.const_cast_derived());
       else
@@ -61,10 +63,13 @@ class SparseMatrixBase : public MatrixBase<Derived>
     template<typename OtherDerived>
     inline Derived& operator=(const MatrixBase<OtherDerived>& other)
     {
-      const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+//       std::cout << "Derived& operator=(const MatrixBase<OtherDerived>& other)\n";
+      //const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      ei_assert((!((Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit))) && "the transpose operation is supposed to be handled in SparseMatrix::operator=");
       const int outerSize = other.outerSize();
-      typedef typename ei_meta_if<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::ret TempType;
-      TempType temp(other.rows(), other.cols());
+      //typedef typename ei_meta_if<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::ret TempType;
+      // thanks to shallow copies, we always eval to a tempary
+      Derived temp(other.rows(), other.cols());
 
       temp.startFill(std::max(this->rows(),this->cols())*2);
       for (int j=0; j<outerSize; ++j)
@@ -88,6 +93,8 @@ class SparseMatrixBase : public MatrixBase<Derived>
     template<typename OtherDerived>
     inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other)
     {
+//       std::cout << typeid(OtherDerived).name() << "\n";
+//       std::cout << Flags << " " << OtherDerived::Flags << "\n";
       const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
 //       std::cout << "eval transpose = " << transpose << "\n";
       const int outerSize = (int(OtherDerived::Flags) & RowMajorBit) ? other.rows() : other.cols();
@@ -153,7 +160,7 @@ class SparseMatrixBase : public MatrixBase<Derived>
         }
         else
         {
-          LinkedVectorMatrix<Scalar, RowMajorBit> trans = m.derived();
+          SparseMatrix<Scalar, RowMajorBit> trans = m.derived();
           s << trans;
         }
       }

Eigen/src/Sparse/SparseProduct.h

@@ -41,22 +41,22 @@ struct ProductReturnType<Lhs,Rhs,SparseProduct>
   // type of the temporary to perform the transpose op
   typedef typename ei_meta_if<TransposeLhs,
     SparseMatrix<Scalar,0>,
-    typename ei_nested<Lhs,Rhs::RowsAtCompileTime>::type>::ret LhsNested;
+    const typename ei_nested<Lhs,Rhs::RowsAtCompileTime>::type>::ret LhsNested;
 
   typedef typename ei_meta_if<TransposeRhs,
     SparseMatrix<Scalar,0>,
-    typename ei_nested<Rhs,Lhs::RowsAtCompileTime>::type>::ret RhsNested;
+    const typename ei_nested<Rhs,Lhs::RowsAtCompileTime>::type>::ret RhsNested;
 
-  typedef Product<typename ei_unconst<LhsNested>::type,
-                  typename ei_unconst<RhsNested>::type, SparseProduct> Type;
+  typedef Product<LhsNested,
+                  RhsNested, SparseProduct> Type;
 };
 
 template<typename LhsNested, typename RhsNested>
 struct ei_traits<Product<LhsNested, RhsNested, SparseProduct> >
 {
   // clean the nested types:
-  typedef typename ei_unconst<typename ei_unref<LhsNested>::type>::type _LhsNested;
-  typedef typename ei_unconst<typename ei_unref<RhsNested>::type>::type _RhsNested;
+  typedef typename ei_cleantype<LhsNested>::type _LhsNested;
+  typedef typename ei_cleantype<RhsNested>::type _RhsNested;
   typedef typename _LhsNested::Scalar Scalar;
 
   enum {
@@ -118,8 +118,8 @@ template<typename LhsNested, typename RhsNested> class Product<LhsNested,RhsNest
     const _LhsNested& rhs() const { return m_rhs; }
 
   protected:
-    const LhsNested m_lhs;
-    const RhsNested m_rhs;
+    LhsNested m_lhs;
+    RhsNested m_rhs;
 };
 
 template<typename Lhs, typename Rhs, typename ResultType,
@@ -133,23 +133,16 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
 {
   typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
 
-  struct ListEl
-  {
-    int next;
-    int index;
-    Scalar value;
-  };
-
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
     // make sure to call innerSize/outerSize since we fake the storage order.
     int rows = lhs.innerSize();
     int cols = rhs.outerSize();
-    int size = lhs.outerSize();
-    ei_assert(size == rhs.rows());
+    //int size = lhs.outerSize();
+    ei_assert(lhs.outerSize() == rhs.innerSize());
 
     // allocate a temporary buffer
-    Scalar* buffer = new Scalar[rows];
+    AmbiVector<Scalar> tempVector(rows);
 
     // estimate the number of non zero entries
     float ratioLhs = float(lhs.nonZeros())/float(lhs.rows()*lhs.cols());
@@ -164,89 +157,23 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
       //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
       // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
       float ratioColRes = ratioRes;
-      if (ratioColRes>0.1)
+      tempVector.init(ratioColRes);
+      tempVector.setZero();
+      for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
       {
-        // dense path, the scalar * columns products are accumulated into a dense column
-        Scalar* __restrict__ tmp = buffer;
-        // set to zero
-        for (int k=0; k<rows; ++k)
-          tmp[k] = 0;
-        for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+        // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+        tempVector.restart();
+        Scalar x = rhsIt.value();
+        for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
         {
-          // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
-          Scalar x = rhsIt.value();
-          for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
-          {
-            tmp[lhsIt.index()] += lhsIt.value() * x;
-          }
-        }
-        // copy the temporary to the respective res.col()
-        for (int k=0; k<rows; ++k)
-          if (tmp[k]!=0)
-            res.fill(k, j) = tmp[k];
-      }
-      else
-      {
-        ListEl* __restrict__ tmp = reinterpret_cast<ListEl*>(buffer);
-        // sparse path, the scalar * columns products are accumulated into a linked list
-        int tmp_size = 0;
-        int tmp_start = -1;
-        for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
-        {
-          int tmp_el = tmp_start;
-          for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
-          {
-            Scalar v = lhsIt.value() * rhsIt.value();
-            int id = lhsIt.index();
-            if (tmp_size==0)
-            {
-              tmp_start = 0;
-              tmp_el = 0;
-              tmp_size++;
-              tmp[0].value = v;
-              tmp[0].index = id;
-              tmp[0].next = -1;
-            }
-            else if (id<tmp[tmp_start].index)
-            {
-              tmp[tmp_size].value = v;
-              tmp[tmp_size].index = id;
-              tmp[tmp_size].next = tmp_start;
-              tmp_start = tmp_size;
-              tmp_size++;
-            }
-            else
-            {
-              int nextel = tmp[tmp_el].next;
-              while (nextel >= 0 && tmp[nextel].index<=id)
-              {
-                tmp_el = nextel;
-                nextel = tmp[nextel].next;
-              }
-
-              if (tmp[tmp_el].index==id)
-              {
-                tmp[tmp_el].value += v;
-              }
-              else
-              {
-                tmp[tmp_size].value = v;
-                tmp[tmp_size].index = id;
-                tmp[tmp_size].next = tmp[tmp_el].next;
-                tmp[tmp_el].next = tmp_size;
-                tmp_size++;
-              }
-            }
-          }
-        }
-        int k = tmp_start;
-        while (k>=0)
-        {
-          if (tmp[k].value!=0)
-            res.fill(tmp[k].index, j) = tmp[k].value;
-          k = tmp[k].next;
+          tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
         }
       }
+      for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
+        if (ResultType::Flags&RowMajorBit)
+          res.fill(j,it.index()) = it.value();
+        else
+          res.fill(it.index(), j) = it.value();
     }
     res.endFill();
   }
@@ -269,7 +196,7 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
 {
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
-    // let's transpose the product and fake the matrices are column major
+    // let's transpose the product to get a column x column product
     ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,ColMajor>::run(rhs, lhs, res);
   }
 };
@@ -280,8 +207,11 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
   typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
-    // let's transpose the product and fake the matrices are column major
-    ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,RowMajor>::run(rhs, lhs, res);
+    // let's transpose the product to get a column x column product
+    SparseTemporaryType _res(res.cols(), res.rows());
+    ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,ColMajor>
+      ::run(rhs, lhs, _res);
+    res = _res.transpose();
   }
 };
 

Eigen/src/Sparse/SparseUtil.h

@@ -31,10 +31,38 @@
 #define EIGEN_DBG_SPARSE(X) X
 #endif
 
+enum SparseBackend {
+  DefaultBackend,
+  Taucs,
+  Cholmod,
+  SuperLU,
+  UmfPack
+};
+
+// solver flags
+enum {
+  CompleteFactorization       = 0x0000,  // the default
+  IncompleteFactorization     = 0x0001,
+  MemoryEfficient             = 0x0002,
+
+  // For LLT Cholesky:
+  SupernodalMultifrontal      = 0x0010,
+  SupernodalLeftLooking       = 0x0020,
+
+  // Ordering methods:
+  NaturalOrdering             = 0x0100, // the default
+  MinimumDegree_AT_PLUS_A     = 0x0200,
+  MinimumDegree_ATA           = 0x0300,
+  ColApproxMinimumDegree      = 0x0400,
+  Metis                       = 0x0500,
+  Scotch                      = 0x0600,
+  Chaco                       = 0x0700,
+  OrderingMask                = 0x0f00
+};
+
 template<typename Derived> class SparseMatrixBase;
 template<typename _Scalar, int _Flags = 0> class SparseMatrix;
-template<typename _Scalar, int _Flags = 0> class HashMatrix;
-template<typename _Scalar, int _Flags = 0> class LinkedVectorMatrix;
+template<typename _Scalar, int _Flags = 0> class SparseVector;
 
 const int AccessPatternNotSupported = 0x0;
 const int AccessPatternSupported    = 0x1;