15 files changed, 78 insertions, 22 deletions
diff --git a/eigen/doc/LeastSquares.dox b/eigen/doc/LeastSquares.dox
index e2191a2..24dfe4b 100644
--- a/eigen/doc/LeastSquares.dox
+++ b/eigen/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:
diff --git a/eigen/doc/Pitfalls.dox b/eigen/doc/Pitfalls.dox
index cf42eff..3f39505 100644
--- a/eigen/doc/Pitfalls.dox
+++ b/eigen/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:
diff --git a/eigen/doc/PreprocessorDirectives.dox b/eigen/doc/PreprocessorDirectives.dox
index f01b39a..40cceb9 100644
--- a/eigen/doc/PreprocessorDirectives.dox
+++ b/eigen/doc/PreprocessorDirectives.dox
@@ -120,6 +120,10 @@ 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_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.
diff --git a/eigen/doc/TopicLinearAlgebraDecompositions.dox b/eigen/doc/TopicLinearAlgebraDecompositions.dox
index 4914706..d9db677 100644
--- a/eigen/doc/TopicLinearAlgebraDecompositions.dox
+++ b/eigen/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
 
@@ -114,6 +114,18 @@ 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>
         <td>Slow (but fast for small matrices)</td>
diff --git a/eigen/doc/TutorialLinearAlgebra.dox b/eigen/doc/TutorialLinearAlgebra.dox
index cb92cee..a727241 100644
--- a/eigen/doc/TutorialLinearAlgebra.dox
+++ b/eigen/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>
@@ -86,6 +86,14 @@ depending on your matrix and the trade-off you want to make:
         <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>
         <td>Positive definite</td>
@@ -102,14 +110,23 @@ depending on your matrix and the trade-off you want to make:
         <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">
diff --git a/eigen/doc/UsingIntelMKL.dox b/eigen/doc/UsingIntelMKL.dox
index a1a3a18..6de14af 100644
--- a/eigen/doc/UsingIntelMKL.dox
+++ b/eigen/doc/UsingIntelMKL.dox
@@ -63,6 +63,8 @@ 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 options can be combined with \b MKL_DIRECT_CALL to enable MKL direct call feature. 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. To make it work properly, the macro \c EIGEN_USE_MKL must also be defined in the case none of the other \c EIGEN_USE_MKL_* macros has been defined.
+
 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.
diff --git a/eigen/doc/eigen_navtree_hacks.js b/eigen/doc/eigen_navtree_hacks.js
index bd7e02b..39c59f7 100644
--- a/eigen/doc/eigen_navtree_hacks.js
+++ b/eigen/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();
diff --git a/eigen/doc/eigendoxy.css b/eigen/doc/eigendoxy.css
index 6274e6c..9a036d2 100644
--- a/eigen/doc/eigendoxy.css
+++ b/eigen/doc/eigendoxy.css
@@ -214,3 +214,8 @@ h3.version {
 td.width20em p.endtd {
   width:  20em;
 }
+
+/* needed for huge screens */
+.ui-resizable-e {
+  background-repeat: repeat-y;
+}
+\ No newline at end of file
diff --git a/eigen/doc/eigendoxy_footer.html.in b/eigen/doc/eigendoxy_footer.html.in
index 878244a..9ac0596 100644
--- a/eigen/doc/eigendoxy_footer.html.in
+++ b/eigen/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-->
diff --git a/eigen/doc/eigendoxy_header.html.in b/eigen/doc/eigendoxy_header.html.in
index 0f3859f..bb149f8 100644
--- a/eigen/doc/eigendoxy_header.html.in
+++ b/eigen/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-->
diff --git a/eigen/doc/examples/Cwise_lgamma.cpp b/eigen/doc/examples/Cwise_lgamma.cpp
index f1c4f50..6bfaccb 100644
--- a/eigen/doc/examples/Cwise_lgamma.cpp
+++ b/eigen/doc/examples/Cwise_lgamma.cpp
@@ -6,4 +6,4 @@ int main()
 {
   Array4d v(0.5,10,0,-1);
   std::cout << v.lgamma() << std::endl;
-}
-\ No newline at end of file
+}
diff --git a/eigen/doc/examples/TutorialLinAlgSVDSolve.cpp b/eigen/doc/examples/TutorialLinAlgSVDSolve.cpp
index 9fbc031..f109f04 100644
--- a/eigen/doc/examples/TutorialLinAlgSVDSolve.cpp
+++ b/eigen/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;
 }
diff --git a/eigen/doc/examples/Tutorial_simple_example_dynamic_size.cpp b/eigen/doc/examples/Tutorial_simple_example_dynamic_size.cpp
index 0f0280e..defcb1e 100644
--- a/eigen/doc/examples/Tutorial_simple_example_dynamic_size.cpp
+++ b/eigen/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";
   }
diff --git a/eigen/doc/examples/matrixfree_cg.cpp b/eigen/doc/examples/matrixfree_cg.cpp
index 6a205ae..7469938 100644
--- a/eigen/doc/examples/matrixfree_cg.cpp
+++ b/eigen/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):
diff --git a/eigen/doc/special_examples/Tutorial_sparse_example.cpp b/eigen/doc/special_examples/Tutorial_sparse_example.cpp
index 830e196..c5767a8 100644
--- a/eigen/doc/special_examples/Tutorial_sparse_example.cpp
+++ b/eigen/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;
@@ -9,7 +10,10 @@ 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)