merge with default Eigen

doc/AsciiQuickReference.txt

@@ -140,7 +140,7 @@ R.array().abs()                // abs(P)
 R.cwiseAbs2()                  // abs(P.^2)
 R.array().abs2()               // abs(P.^2)
 (R.array() < s).select(P,Q );  // (R < s ? P : Q)
-R = (Q.array()==0).select(P,A) // R(Q==0) = P(Q==0)
+R = (Q.array()==0).select(P,R) // R(Q==0) = P(Q==0)
 R = P.unaryExpr(ptr_fun(func)) // R = arrayfun(func, P)   // with: scalar func(const scalar &x);
 
 

doc/Doxyfile.in

@@ -1764,7 +1764,7 @@ UML_LOOK               = YES
 # the class node. If there are many fields or methods and many nodes the
 # graph may become too big to be useful. The UML_LIMIT_NUM_FIELDS
 # threshold limits the number of items for each type to make the size more
-# managable. Set this to 0 for no limit. Note that the threshold may be
+# manageable. Set this to 0 for no limit. Note that the threshold may be
 # exceeded by 50% before the limit is enforced.
 
 UML_LIMIT_NUM_FIELDS   = 10

doc/FunctionsTakingEigenTypes.dox

@@ -79,7 +79,7 @@ These examples are just intended to give the reader a first impression of how fu
 
 \section TopicUsingRefClass How to write generic, but non-templated function?
 
-In all the previous examples, the functions had to be template functions. This approach allows to write very generic code, but it is often desirable to write non templated function and still keep some level of genericity to avoid stupid copies of the arguments. The typical example is to write functions accepting both a MatrixXf or a block of a MatrixXf. This exactly the purpose of the Ref class. Here is a simple example:
+In all the previous examples, the functions had to be template functions. This approach allows to write very generic code, but it is often desirable to write non templated functions and still keep some level of genericity to avoid stupid copies of the arguments. The typical example is to write functions accepting both a MatrixXf or a block of a MatrixXf. This is exactly the purpose of the Ref class. Here is a simple example:
 
 <table class="example">
 <tr><th>Example:</th><th>Output:</th></tr>
@@ -133,7 +133,7 @@ In this special case, the example is fine and will be working because both param
 
 \section TopicPlainFunctionsFailing In which cases do functions taking a plain Matrix or Array argument fail?
 
-Here, we consider a slightly modified version of the function given above. This time, we do not want to return the result but pass an additional non-const paramter which allows us to store the result. A first naive implementation might look as follows.
+Here, we consider a slightly modified version of the function given above. This time, we do not want to return the result but pass an additional non-const parameter which allows us to store the result. A first naive implementation might look as follows.
 \code
 // Note: This code is flawed!
 void cov(const MatrixXf& x, const MatrixXf& y, MatrixXf& C)
@@ -176,7 +176,7 @@ The implementation above does now not only work with temporary expressions but i
 
 \section TopicResizingInGenericImplementations How to resize matrices in generic implementations?
 
-One might think we are done now, right? This is not completely true because in order for our covariance function to be generically applicable, we want the follwing code to work
+One might think we are done now, right? This is not completely true because in order for our covariance function to be generically applicable, we want the following code to work
 \code
 MatrixXf x = MatrixXf::Random(100,3);
 MatrixXf y = MatrixXf::Random(100,3);

doc/LeastSquares.dox

@@ -16,7 +16,7 @@ equations is the fastest but least accurate, and the QR decomposition is in betw
 
 \section LeastSquaresSVD Using the SVD decomposition
 
-The \link JacobiSVD::solve() solve() \endlink method in the JacobiSVD class can be directly used to
+The \link BDCSVD::solve() solve() \endlink method in the BDCSVD class can be directly used to
 solve linear squares systems. It is not enough to compute only the singular values (the default for
 this class); you also need the singular vectors but the thin SVD decomposition suffices for
 computing least squares solutions:

doc/Pitfalls.dox

@@ -2,10 +2,16 @@ namespace Eigen {
 
 /** \page TopicPitfalls Common pitfalls
 
+
 \section TopicPitfalls_template_keyword Compilation error with template methods
 
 See this \link TopicTemplateKeyword page \endlink.
 
+\section TopicPitfalls_aliasing Aliasing
+
+Don't miss this \link TopicAliasing page \endlink on aliasing,
+especially if you got wrong results in statements where the destination appears on the right hand side of the expression.
+
 \section TopicPitfalls_auto_keyword C++11 and the auto keyword
 
 In short: do not use the auto keywords with Eigen's expressions, unless you are 100% sure about what you are doing. In particular, do not use the auto keyword as a replacement for a Matrix<> type. Here is an example:

doc/PreprocessorDirectives.dox

@@ -51,7 +51,7 @@ are doing.
 
 \section TopicPreprocessorDirectivesCppVersion C++ standard features
 
-By default, %Eigen strive to automatically detect and enable langage features at compile-time based on
+By default, %Eigen strive to automatically detect and enable language features at compile-time based on
 the information provided by the compiler.
 
  - \b EIGEN_MAX_CPP_VER - disables usage of C++ features requiring a version greater than EIGEN_MAX_CPP_VER.
@@ -66,7 +66,7 @@ functions by defining EIGEN_HAS_C99_MATH=1.
    Automatic detection disabled if EIGEN_MAX_CPP_VER<11.
  - \b EIGEN_HAS_CXX11_MATH - controls the implementation of some functions such as round, logp1, isinf, isnan, etc.
    Automatic detection disabled if EIGEN_MAX_CPP_VER<11.
- - \b EIGEN_HAS_RVALUE_REFERENCES - defines whetehr rvalue references are supported
+ - \b EIGEN_HAS_RVALUE_REFERENCES - defines whether rvalue references are supported
    Automatic detection disabled if EIGEN_MAX_CPP_VER<11.
  - \b EIGEN_HAS_STD_RESULT_OF - defines whether std::result_of is supported
    Automatic detection disabled if EIGEN_MAX_CPP_VER<11.
@@ -120,6 +120,12 @@ run time. However, these assertions do cost time and can thus be turned off.
  - \b \c EIGEN_STACK_ALLOCATION_LIMIT - defines the maximum bytes for a buffer to be allocated on the stack. For internal
    temporary buffers, dynamic memory allocation is employed as a fall back. For fixed-size matrices or arrays, exceeding
    this threshold raises a compile time assertion. Use 0 to set no limit. Default is 128 KB.
+ - \b \c EIGEN_NO_CUDA - disables CUDA support when defined. Might be useful in .cu files for which Eigen is used on the host only,
+   and never called from device code.
+ - \b \c EIGEN_STRONG_INLINE - This macro is used to qualify critical functions and methods that we expect the compiler to inline.
+   By default it is defined to \c __forceinline for MSVC and ICC, and to \c inline for other compilers. A tipical usage is to
+   define it to \c inline for MSVC users wanting faster compilation times, at the risk of performance degradations in some rare
+   cases for which MSVC inliner fails to do a good job.
 
 
  - \c EIGEN_DONT_ALIGN - Deprecated, it is a synonym for \c EIGEN_MAX_ALIGN_BYTES=0. It disables alignment completely. %Eigen will not try to align its objects and does not expect that any objects passed to it are aligned. This will turn off vectorization if \b EIGEN_UNALIGNED_VECTORIZE=1. Not defined by default.

doc/QuickReference.dox

@@ -68,7 +68,7 @@ Array<float,4,1>                <=>   Array4f
 
 Conversion between the matrix and array worlds:
 \code
-Array44f a1, a1;
+Array44f a1, a2;
 Matrix4f m1, m2;
 m1 = a1 * a2;                     // coeffwise product, implicit conversion from array to matrix.
 a1 = m1 * m2;                     // matrix product, implicit conversion from matrix to array.
@@ -261,6 +261,8 @@ x.setIdentity();
 Vector3f::UnitX() // 1 0 0
 Vector3f::UnitY() // 0 1 0
 Vector3f::UnitZ() // 0 0 1
+Vector4f::Unit(i)
+x.setUnit(i);
 \endcode
   </td>
   <td>
@@ -278,6 +280,7 @@ N/A
 
 
 VectorXf::Unit(size,i)
+x.setUnit(size,i);
 VectorXf::Unit(4,1) == Vector4f(0,1,0,0)
                     == Vector4f::UnitY()
 \endcode
@@ -285,7 +288,12 @@ VectorXf::Unit(4,1) == Vector4f(0,1,0,0)
 </tr>
 </table>
 
-
+Note that it is allowed to call any of the \c set* functions to a dynamic-sized vector or matrix without passing new sizes.
+For instance:
+\code
+MatrixXi M(3,3);
+M.setIdentity();
+\endcode
 
 \subsection QuickRef_Map Mapping external arrays
 

doc/QuickStartGuide.dox

@@ -68,7 +68,7 @@ The output is as follows:
 
 The second example starts by declaring a 3-by-3 matrix \c m which is initialized using the \link DenseBase::Random(Index,Index) Random() \endlink method with random values between -1 and 1. The next line applies a linear mapping such that the values are between 10 and 110. The function call \link DenseBase::Constant(Index,Index,const Scalar&) MatrixXd::Constant\endlink(3,3,1.2) returns a 3-by-3 matrix expression having all coefficients equal to 1.2. The rest is standard arithmetics.
 
-The next line of the \c main function introduces a new type: \c VectorXd. This represents a (column) vector of arbitrary size. Here, the vector \c v is created to contain \c 3 coefficients which are left unitialized. The one but last line uses the so-called comma-initializer, explained in \ref TutorialAdvancedInitialization, to set all coefficients of the vector \c v to be as follows:
+The next line of the \c main function introduces a new type: \c VectorXd. This represents a (column) vector of arbitrary size. Here, the vector \c v is created to contain \c 3 coefficients which are left uninitialized. The one but last line uses the so-called comma-initializer, explained in \ref TutorialAdvancedInitialization, to set all coefficients of the vector \c v to be as follows:
 
 \f[
 v =

doc/SparseLinearSystems.dox

@@ -70,6 +70,9 @@ They are summarized in the following tables:
 <tr><td>UmfPackLU</td><td>\link UmfPackSupport_Module UmfPackSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td>
     <td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td>
     <td></td></tr>
+<tr><td>KLU</td><td>\link KLUSupport_Module KLUSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, suitted for circuit simulation</td>
+    <td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td>
+    <td></td></tr>
 <tr><td>SuperLU</td><td>\link SuperLUSupport_Module SuperLUSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td>
     <td>Requires the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library, (BSD-like)</td>
     <td></td></tr>

doc/SparseQuickReference.dox

@@ -80,7 +80,7 @@ sm1.setZero();
 
 
 \section SparseBasicInfos Matrix properties
-Beyond the basic functions rows() and cols(), there are some useful functions that are available to easily get some informations from the matrix. 
+Beyond the basic functions rows() and cols(), there are some useful functions that are available to easily get some information from the matrix. 
 <table class="manual">
 <tr>
   <td> \code

doc/TemplateKeyword.dox

@@ -76,7 +76,7 @@ point where the template is defined, without knowing the actual value of the tem
 and \c Derived2 in the example). That means that the compiler cannot know that <tt>dst.triangularView</tt> is
 a member template and that the following &lt; symbol is part of the delimiter for the template
 parameter. Another possibility would be that <tt>dst.triangularView</tt> is a member variable with the &lt;
-symbol refering to the <tt>operator&lt;()</tt> function. In fact, the compiler should choose the second
+symbol referring to the <tt>operator&lt;()</tt> function. In fact, the compiler should choose the second
 possibility, according to the standard. If <tt>dst.triangularView</tt> is a member template (as in our case),
 the programmer should specify this explicitly with the \c template keyword and write <tt>dst.template
 triangularView</tt>.

doc/TopicLazyEvaluation.dox

@@ -58,7 +58,7 @@ the product <tt>matrix3 * matrix4</tt> gets evaluated immediately into a tempora
 
 \code matrix1 = matrix2 * (matrix3 + matrix4); \endcode
 
-Here, provided the matrices have at least 2 rows and 2 columns, each coefficienct of the expression <tt>matrix3 + matrix4</tt> is going to be used several times in the matrix product. Instead of computing the sum everytime, it is much better to compute it once and store it in a temporary variable. Eigen understands this and evaluates <tt>matrix3 + matrix4</tt> into a temporary variable before evaluating the product.
+Here, provided the matrices have at least 2 rows and 2 columns, each coefficienct of the expression <tt>matrix3 + matrix4</tt> is going to be used several times in the matrix product. Instead of computing the sum every time, it is much better to compute it once and store it in a temporary variable. Eigen understands this and evaluates <tt>matrix3 + matrix4</tt> into a temporary variable before evaluating the product.
 
 */
 

doc/TopicLinearAlgebraDecompositions.dox

@@ -4,7 +4,7 @@ namespace Eigen {
 
 This page presents a catalogue of the dense matrix decompositions offered by Eigen.
 For an introduction on linear solvers and decompositions, check this \link TutorialLinearAlgebra page \endlink.
-To get an overview of the true relative speed of the different decomposition, check this \link DenseDecompositionBenchmark benchmark \endlink.
+To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink.
 
 \section TopicLinAlgBigTable Catalogue of decompositions offered by Eigen
 
@@ -113,6 +113,18 @@ To get an overview of the true relative speed of the different decomposition, ch
 
     <tr><th class="inter" colspan="9">\n Singular values and eigenvalues decompositions</th></tr>
 
+    <tr>
+        <td>BDCSVD (divide \& conquer)</td>
+        <td>-</td>
+        <td>One of the fastest SVD algorithms</td>
+        <td>Excellent</td>
+        <td>Yes</td>
+        <td>Singular values/vectors, least squares</td>
+        <td>Yes (and does least squares)</td>
+        <td>Excellent</td>
+        <td>Blocked bidiagonalization</td>
+    </tr>
+
     <tr>
         <td>JacobiSVD (two-sided)</td>
         <td>-</td>
@@ -248,7 +260,7 @@ To get an overview of the true relative speed of the different decomposition, ch
   <dt><b>Blocking</b></dt>
     <dd>Means the algorithm can work per block, whence guaranteeing a good scaling of the performance for large matrices.</dd>
   <dt><b>Implicit Multi Threading (MT)</b></dt>
-    <dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product rountines.</dd>
+    <dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product routines.</dd>
   <dt><b>Explicit Multi Threading (MT)</b></dt>
     <dd>Means the algorithm is explicitly parallelized to take advantage of multicore processors via OpenMP.</dd>
   <dt><b>Meta-unroller</b></dt>

doc/TopicMultithreading.dox

@@ -47,7 +47,7 @@ int main(int argc, char** argv)
 
 \warning note that all functions generating random matrices are \b not re-entrant nor thread-safe. Those include DenseBase::Random(), and DenseBase::setRandom() despite a call to Eigen::initParallel(). This is because these functions are based on std::rand which is not re-entrant. For thread-safe random generator, we recommend the use of boost::random or c++11 random feature.
 
-In the case your application is parallelized with OpenMP, you might want to disable Eigen's own parallization as detailed in the previous section.
+In the case your application is parallelized with OpenMP, you might want to disable Eigen's own parallelization as detailed in the previous section.
 
 */
 

doc/TutorialGeometry.dox

@@ -111,7 +111,7 @@ rot3 = rot1.slerp(alpha,rot2);\endcode</td></tr>
 
 
 <a href="#" class="top">top</a>\section TutorialGeoTransform Affine transformations
-Generic affine transformations are represented by the Transform class which internaly
+Generic affine transformations are represented by the Transform class which internally
 is a (Dim+1)^2 matrix. In Eigen we have chosen to not distinghish between points and
 vectors such that all points are actually represented by displacement vectors from the
 origin ( \f$ \mathbf{p} \equiv \mathbf{p}-0 \f$ ). With that in mind, real points and

doc/TutorialLinearAlgebra.dox

@@ -73,7 +73,7 @@ depending on your matrix and the trade-off you want to make:
         <td>ColPivHouseholderQR</td>
         <td>colPivHouseholderQr()</td>
         <td>None</td>
-        <td>++</td>
+        <td>+</td>
         <td>-</td>
         <td>+++</td>
     </tr>
@@ -85,6 +85,14 @@ depending on your matrix and the trade-off you want to make:
         <td>- -</td>
         <td>+++</td>
     </tr>
+    <tr class="alt">
+        <td>CompleteOrthogonalDecomposition</td>
+        <td>completeOrthogonalDecomposition()</td>
+        <td>None</td>
+        <td>+</td>
+        <td>-</td>
+        <td>+++</td>
+    </tr>
     <tr class="alt">
         <td>LLT</td>
         <td>llt()</td>
@@ -101,15 +109,24 @@ depending on your matrix and the trade-off you want to make:
         <td>+</td>
         <td>++</td>
     </tr>
+    <tr class="alt">
+        <td>BDCSVD</td>
+        <td>bdcSvd()</td>
+        <td>None</td>
+        <td>-</td>
+        <td>-</td>
+        <td>+++</td>
+    </tr>
     <tr class="alt">
         <td>JacobiSVD</td>
         <td>jacobiSvd()</td>
         <td>None</td>
-        <td>- -</td>
+        <td>-</td>
         <td>- - -</td>
         <td>+++</td>
     </tr>
 </table>
+To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink.
 
 All of these decompositions offer a solve() method that works as in the above example.
 
@@ -183,8 +200,11 @@ Here is an example:
 
 \section TutorialLinAlgLeastsquares Least squares solving
 
-The most accurate method to do least squares solving is with a SVD decomposition. Eigen provides one
-as the JacobiSVD class, and its solve() is doing least-squares solving.
+The most accurate method to do least squares solving is with a SVD decomposition.
+Eigen provides two implementations.
+The recommended one is the BDCSVD class, which scale well for large problems
+and automatically fall-back to the JacobiSVD class for smaller problems.
+For both classes, their solve() method is doing least-squares solving.
 
 Here is an example:
 <table class="example">

doc/TutorialMapClass.dox

@@ -29,9 +29,9 @@ Map<const Vector4i> mi(pi);
 \endcode
 where \c pi is an \c int \c *. In this case the size does not have to be passed to the constructor, because it is already specified by the Matrix/Array type.
 
-Note that Map does not have a default constructor; you \em must pass a pointer to intialize the object. However, you can work around this requirement (see \ref TutorialMapPlacementNew).
+Note that Map does not have a default constructor; you \em must pass a pointer to initialize the object. However, you can work around this requirement (see \ref TutorialMapPlacementNew).
 
-Map is flexible enough to accomodate a variety of different data representations.  There are two other (optional) template parameters:
+Map is flexible enough to accommodate a variety of different data representations.  There are two other (optional) template parameters:
 \code
 Map<typename MatrixType,
     int MapOptions,

doc/TutorialSparse.dox

@@ -57,7 +57,7 @@ The \c "_" indicates available free space to quickly insert new elements.
 Assuming no reallocation is needed, the insertion of a random element is therefore in O(nnz_j) where nnz_j is the number of nonzeros of the respective inner vector.
 On the other hand, inserting elements with increasing inner indices in a given inner vector is much more efficient since this only requires to increase the respective \c InnerNNZs entry that is a O(1) operation.
 
-The case where no empty space is available is a special case, and is refered as the \em compressed mode.
+The case where no empty space is available is a special case, and is referred as the \em compressed mode.
 It corresponds to the widely used Compressed Column (or Row) Storage schemes (CCS or CRS).
 Any SparseMatrix can be turned to this form by calling the SparseMatrix::makeCompressed() function.
 In this case, one can remark that the \c InnerNNZs array is redundant with \c OuterStarts because we the equality: \c InnerNNZs[j] = \c OuterStarts[j+1]-\c OuterStarts[j].
@@ -212,7 +212,7 @@ See the SparseMatrix::setFromTriplets() function and class Triplet for more deta
 
 
 In some cases, however, slightly higher performance, and lower memory consumption can be reached by directly inserting the non-zeros into the destination matrix.
-A typical scenario of this approach is illustrated bellow:
+A typical scenario of this approach is illustrated below:
 \code
 1: SparseMatrix<double> mat(rows,cols);         // default is column major
 2: mat.reserve(VectorXi::Constant(cols,6));

doc/UnalignedArrayAssert.dox

@@ -117,8 +117,8 @@ It doesn't disable 16-byte alignment, because that would mean that vectorized an
 
 \section checkmycode How can I check my code is safe regarding alignment issues?
 
-Unfortunately, there is no possibility in C++ to detect any of the aformentioned shortcoming at compile time (though static analysers are becoming more and more powerful and could detect some of them).
-Even at runtime, all we can do is to catch invalid unaligned allocation and trigger the explicit assertion mentioned at the begining of this page.
+Unfortunately, there is no possibility in C++ to detect any of the aforementioned shortcoming at compile time (though static analysers are becoming more and more powerful and could detect some of them).
+Even at runtime, all we can do is to catch invalid unaligned allocation and trigger the explicit assertion mentioned at the beginning of this page.
 Therefore, if your program runs fine on a given system with some given compilation flags, then this does not guarantee that your code is safe. For instance, on most 64 bits systems buffer are aligned on 16 bytes boundary and so, if you do not enable AVX instruction set, then your code will run fine. On the other hand, the same code may assert if moving to a more exotic platform, or enabling AVX instructions that required 32 bytes alignment by default.
 
 The situation is not hopeless though. Assuming your code is well covered by unit test, then you can check its alignment safety by linking it to a custom malloc library returning 8 bytes aligned buffers only. This way all alignment shortcomings should pop-up. To this end, you must also compile your program with \link TopicPreprocessorDirectivesPerformance EIGEN_MALLOC_ALREADY_ALIGNED=0 \endlink.

doc/UsingIntelMKL.dox

@@ -63,6 +63,12 @@ In addition you can choose which parts will be substituted by defining one or mu
 <tr><td>\c EIGEN_USE_MKL_ALL </td><td>Defines \c EIGEN_USE_BLAS, \c EIGEN_USE_LAPACKE, and \c EIGEN_USE_MKL_VML </td></tr>
 </table>
 
+The \c EIGEN_USE_BLAS and \c EIGEN_USE_LAPACKE* macros can be combined with \c EIGEN_USE_MKL to explicitly tell Eigen that the underlying BLAS/Lapack implementation is Intel MKL.
+The main effect is to enable MKL direct call feature (\c MKL_DIRECT_CALL).
+This may help to increase performance of some MKL BLAS (?GEMM, ?GEMV, ?TRSM, ?AXPY and ?DOT) and LAPACK (LU, Cholesky and QR) routines for very small matrices.
+MKL direct call can be disabled by defining \c EIGEN_MKL_NO_DIRECT_CALL.
+
+
 Note that the BLAS and LAPACKE backends can be enabled for any F77 compatible BLAS and LAPACK libraries. See this \link TopicUsingBlasLapack page \endlink for the details.
 
 Finally, the PARDISO sparse solver shipped with Intel MKL can be used through the \ref PardisoLU, \ref PardisoLLT and \ref PardisoLDLT classes of the \ref PardisoSupport_Module.

doc/UsingNVCC.dox

@@ -3,18 +3,16 @@ namespace Eigen {
 
 /** \page TopicCUDA Using Eigen in CUDA kernels
 
-\b Disclaimer: this page is about an \b experimental feature in %Eigen.
-
-Staring from CUDA 5.0, the CUDA compiler, \c nvcc, is able to properly parse %Eigen's code (almost).
-A few adaptations of the %Eigen's code already allows to use some parts of %Eigen in your own CUDA kernels.
-To this end you need the devel branch of %Eigen, CUDA 5.0 or greater with GCC.
+Staring from CUDA 5.5 and Eigen 3.3, it is possible to use Eigen's matrices, vectors, and arrays for fixed size within CUDA kernels. This is especially useful when working on numerous but small problems. By default, when Eigen's headers are included within a .cu file compiled by nvcc most Eigen's functions and methods are prefixed by the \c __device__ \c __host__ keywords making them callable from both host and device code.
+This support can be disabled by defining \c EIGEN_NO_CUDA before including any Eigen's header.
+This might be useful to disable some warnings when a .cu file makes use of Eigen on the host side only.
+However, in both cases, host's SIMD vectorization has to be disabled in .cu files.
+It is thus \b strongly \b recommended to properly move all costly host computation from your .cu files to regular .cpp files.
 
 Known issues:
 
  - \c nvcc with MS Visual Studio does not work (patch welcome)
  
- - \c nvcc with \c clang does not work (patch welcome)
- 
  - \c nvcc 5.5 with gcc-4.7 (or greater) has issues with the standard \c \<limits\> header file. To workaround this, you can add the following before including any other files:
    \code
     // workaround issue between gcc >= 4.7 and cuda 5.5

doc/eigen_navtree_hacks.js

@@ -65,6 +65,10 @@ function getNode(o, po)
 function resizeHeight() 
 {
   var toc = $("#nav-toc");
+  var header = $("#header");
+  var content = $("#doc-content");
+  var navtree = $("#nav-path");
+  var sidenav = $("#side-nav");
   var tocHeight = toc.height();  // <- we added this line
   var headerHeight = header.height();
   var footerHeight = footer.height();

doc/eigendoxy.css

@@ -93,7 +93,7 @@ table th.inter {
 	border-color: #cccccc;
 }
 
-/** class for exemple / output tables **/
+/** class for example / output tables **/
 
 table.example {
 }
@@ -219,3 +219,8 @@ h3.version {
 td.width20em p.endtd {
   width:  20em;
 }
+
+/* needed for huge screens */
+.ui-resizable-e {
+  background-repeat: repeat-y;
+}

doc/eigendoxy_footer.html.in

@@ -5,14 +5,14 @@
     $navpath
     <li class="footer">$generatedby
     <a href="http://www.doxygen.org/index.html">
-    <img class="footer" src="$relpath$doxygen.png" alt="doxygen"/></a> $doxygenversion </li>
+    <img class="footer" src="$relpath^doxygen.png" alt="doxygen"/></a> $doxygenversion </li>
   </ul>
 </div>
 <!--END GENERATE_TREEVIEW-->
 <!--BEGIN !GENERATE_TREEVIEW-->
 <hr class="footer"/><address class="footer"><small>
 $generatedby &#160;<a href="http://www.doxygen.org/index.html">
-<img class="footer" src="$relpath$doxygen.png" alt="doxygen"/>
+<img class="footer" src="$relpath^doxygen.png" alt="doxygen"/>
 </a> $doxygenversion
 </small></address>
 <!--END !GENERATE_TREEVIEW-->

doc/eigendoxy_header.html.in

@@ -4,25 +4,23 @@
 <meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
 <meta http-equiv="X-UA-Compatible" content="IE=9"/>
 <meta name="generator" content="Doxygen $doxygenversion"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
 <!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
 <!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
-<link href="$relpath$tabs.css" rel="stylesheet" type="text/css"/>
-<script type="text/javascript" src="$relpath$jquery.js"></script>
-<script type="text/javascript" src="$relpath$dynsections.js"></script>
+<link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="$relpath^jquery.js"></script>
+<script type="text/javascript" src="$relpath^dynsections.js"></script>
 $treeview
 $search
 $mathjax
-<link href="$relpath$$stylesheet"   rel="stylesheet" type="text/css" />
+<link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" />
 <link href="$relpath$eigendoxy.css" rel="stylesheet" type="text/css">
 <!-- $extrastylesheet -->
 <script type="text/javascript" src="$relpath$eigen_navtree_hacks.js"></script>
-<!-- <script type="text/javascript"> -->
-<!-- </script> -->
 
 </head>
 <body>
 <div id="top"><!-- do not remove this div, it is closed by doxygen! -->
-<!-- <a name="top"></a> -->
 
 <!--BEGIN TITLEAREA-->
 <div id="titlearea">
@@ -30,10 +28,10 @@ $mathjax
  <tbody>
  <tr style="height: 56px;">
   <!--BEGIN PROJECT_LOGO-->
-  <td id="projectlogo"><img alt="Logo" src="$relpath$$projectlogo"/></td>
+  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
   <!--END PROJECT_LOGO-->
   <!--BEGIN PROJECT_NAME-->
-  <td style="padding-left: 0.5em;">
+  <td id="projectalign" style="padding-left: 0.5em;">
    <div id="projectname"><a href="http://eigen.tuxfamily.org">$projectname</a>
    <!--BEGIN PROJECT_NUMBER-->&#160;<span id="projectnumber">$projectnumber</span><!--END PROJECT_NUMBER-->
    </div>
@@ -42,7 +40,7 @@ $mathjax
   <!--END PROJECT_NAME-->
   <!--BEGIN !PROJECT_NAME-->
    <!--BEGIN PROJECT_BRIEF-->
-    <td style="padding-left: 0.5em;">
+    <td id="projectalign" style="padding-left: 0.5em;">
     <div id="projectbrief">$projectbrief</div>
     </td>
    <!--END PROJECT_BRIEF-->

doc/examples/Cwise_lgamma.cpp

@@ -6,4 +6,4 @@ int main()
 {
   Array4d v(0.5,10,0,-1);
   std::cout << v.lgamma() << std::endl;
-}
+}

doc/examples/TutorialLinAlgSVDSolve.cpp

@@ -11,5 +11,5 @@ int main()
    VectorXf b = VectorXf::Random(3);
    cout << "Here is the right hand side b:\n" << b << endl;
    cout << "The least-squares solution is:\n"
-        << A.jacobiSvd(ComputeThinU | ComputeThinV).solve(b) << endl;
+        << A.bdcSvd(ComputeThinU | ComputeThinV).solve(b) << endl;
 }

doc/examples/Tutorial_simple_example_dynamic_size.cpp

@@ -10,7 +10,7 @@ int main()
     MatrixXi m(size,size+1);         // a (size)x(size+1)-matrix of int's
     for (int j=0; j<m.cols(); ++j)   // loop over columns
       for (int i=0; i<m.rows(); ++i) // loop over rows
-        m(i,j) = i+j*m.rows();       // to access matrix coefficients,
+        m(i,j) = i+j*size;           // to access matrix coefficients,
                                      // use operator()(int,int)
     std::cout << m << "\n\n";
   }

doc/examples/matrixfree_cg.cpp

@@ -67,6 +67,7 @@ namespace internal {
       // This method should implement "dst += alpha * lhs * rhs" inplace,
       // however, for iterative solvers, alpha is always equal to 1, so let's not bother about it.
       assert(alpha==Scalar(1) && "scaling is not implemented");
+      EIGEN_ONLY_USED_FOR_DEBUG(alpha);
 
       // Here we could simply call dst.noalias() += lhs.my_matrix() * rhs,
       // but let's do something fancier (and less efficient):

doc/examples/nullary_indexing.cpp

@@ -30,7 +30,7 @@ public:
 // [function]
 template <class ArgType, class RowIndexType, class ColIndexType>
 CwiseNullaryOp<indexing_functor<ArgType,RowIndexType,ColIndexType>, typename indexing_functor<ArgType,RowIndexType,ColIndexType>::MatrixType>
-indexing(const Eigen::MatrixBase<ArgType>& arg, const RowIndexType& row_indices, const ColIndexType& col_indices)
+mat_indexing(const Eigen::MatrixBase<ArgType>& arg, const RowIndexType& row_indices, const ColIndexType& col_indices)
 {
   typedef indexing_functor<ArgType,RowIndexType,ColIndexType> Func;
   typedef typename Func::MatrixType MatrixType;
@@ -45,7 +45,7 @@ int main()
   Eigen::MatrixXi A = Eigen::MatrixXi::Random(4,4);
   Array3i ri(1,2,1);
   ArrayXi ci(6); ci << 3,2,1,0,0,2;
-  Eigen::MatrixXi B = indexing(A, ri, ci);
+  Eigen::MatrixXi B = mat_indexing(A, ri, ci);
   std::cout << "A =" << std::endl;
   std::cout << A << std::endl << std::endl;
   std::cout << "A([" << ri.transpose() << "], [" << ci.transpose() << "]) =" << std::endl;
@@ -53,11 +53,11 @@ int main()
   std::cout << "[main1]\n";
 
   std::cout << "[main2]\n";
-  B =  indexing(A, ri+1, ci);
+  B =  mat_indexing(A, ri+1, ci);
   std::cout << "A(ri+1,ci) =" << std::endl;
   std::cout << B << std::endl << std::endl;
 #if __cplusplus >= 201103L
-  B =  indexing(A, ArrayXi::LinSpaced(13,0,12).unaryExpr([](int x){return x%4;}), ArrayXi::LinSpaced(4,0,3));
+  B =  mat_indexing(A, ArrayXi::LinSpaced(13,0,12).unaryExpr([](int x){return x%4;}), ArrayXi::LinSpaced(4,0,3));
   std::cout << "A(ArrayXi::LinSpaced(13,0,12).unaryExpr([](int x){return x%4;}), ArrayXi::LinSpaced(4,0,3)) =" << std::endl;
   std::cout << B << std::endl << std::endl;
 #endif

doc/snippets/DirectionWise_hnormalized.cpp

@@ -1,7 +1,6 @@
-typedef Matrix<double,4,Dynamic> Matrix4Xd;
 Matrix4Xd M = Matrix4Xd::Random(4,5);
 Projective3d P(Matrix4d::Random());
 cout << "The matrix M is:" << endl << M << endl << endl;
 cout << "M.colwise().hnormalized():" << endl << M.colwise().hnormalized() << endl << endl;
 cout << "P*M:" << endl << P*M << endl << endl;
-cout << "(P*M).colwise().hnormalized():" << endl << (P*M).colwise().hnormalized() << endl << endl;
+cout << "(P*M).colwise().hnormalized():" << endl << (P*M).colwise().hnormalized() << endl << endl;

doc/snippets/MatrixBase_cwiseEqual.cpp

@@ -3,5 +3,5 @@ m << 1, 0,
      1, 1;
 cout << "Comparing m with identity matrix:" << endl;
 cout << m.cwiseEqual(MatrixXi::Identity(2,2)) << endl;
-int count = m.cwiseEqual(MatrixXi::Identity(2,2)).count();
+Index count = m.cwiseEqual(MatrixXi::Identity(2,2)).count();
 cout << "Number of coefficients that are equal: " << count << endl;

doc/snippets/MatrixBase_cwiseNotEqual.cpp

@@ -3,5 +3,5 @@ m << 1, 0,
      1, 1;
 cout << "Comparing m with identity matrix:" << endl;
 cout << m.cwiseNotEqual(MatrixXi::Identity(2,2)) << endl;
-int count = m.cwiseNotEqual(MatrixXi::Identity(2,2)).count();
+Index count = m.cwiseNotEqual(MatrixXi::Identity(2,2)).count();
 cout << "Number of coefficients that are not equal: " << count << endl;

doc/snippets/MatrixBase_reshaped_all.cpp

@@ -1,4 +1,3 @@
-using Eigen::placeholders::all;
 Matrix4i m = Matrix4i::Random();
 cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is m(all).transpose():" << endl << m(all).transpose() << endl;

doc/snippets/Matrix_Map_stride.cpp

@@ -0,0 +1,7 @@
+Matrix4i A;
+A << 1,  2,  3,  4,
+     5,  6,  7,  8,
+     9, 10, 11, 12,
+    13, 14, 15, 16;
+
+std::cout << Matrix2i::Map(&A(1,1),Stride<8,2>()) << std::endl;

doc/snippets/VectorwiseOp_homogeneous.cpp

@@ -1,7 +1,6 @@
-typedef Matrix<double,3,Dynamic> Matrix3Xd;
 Matrix3Xd M = Matrix3Xd::Random(3,5);
 Projective3d P(Matrix4d::Random());
 cout << "The matrix M is:" << endl << M << endl << endl;
 cout << "M.colwise().homogeneous():" << endl << M.colwise().homogeneous() << endl << endl;
 cout << "P * M.colwise().homogeneous():" << endl << P * M.colwise().homogeneous() << endl << endl;
-cout << "P * M.colwise().homogeneous().hnormalized(): " << endl << (P * M.colwise().homogeneous()).colwise().hnormalized() << endl << endl;
+cout << "P * M.colwise().homogeneous().hnormalized(): " << endl << (P * M.colwise().homogeneous()).colwise().hnormalized() << endl << endl;

doc/special_examples/Tutorial_sparse_example.cpp

@@ -1,5 +1,6 @@
 #include <Eigen/Sparse>
 #include <vector>
+#include <iostream>
 
 typedef Eigen::SparseMatrix<double> SpMat; // declares a column-major sparse matrix type of double
 typedef Eigen::Triplet<double> T;
@@ -9,10 +10,13 @@ void saveAsBitmap(const Eigen::VectorXd& x, int n, const char* filename);
 
 int main(int argc, char** argv)
 {
-  assert(argc==2);
+  if(argc!=2) {
+    std::cerr << "Error: expected one and only one argument.\n";
+    return -1;
+  }
   
   int n = 300;  // size of the image
-  int m = n*n;  // number of unknows (=number of pixels)
+  int m = n*n;  // number of unknowns (=number of pixels)
 
   // Assembly:
   std::vector<T> coefficients;            // list of non-zeros coefficients