bump to 3.1.4

(transplanted from 4020d4286f
)

CTestConfig.cmake

@@ -4,10 +4,10 @@
 ## # The following are required to uses Dart and the Cdash dashboard
 ##   ENABLE_TESTING()
 ##   INCLUDE(CTest)
-set(CTEST_PROJECT_NAME "Eigen")
+set(CTEST_PROJECT_NAME "Eigen3.1")
 set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
 
 set(CTEST_DROP_METHOD "http")
 set(CTEST_DROP_SITE "manao.inria.fr")
-set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
-set(CTEST_DROP_SITE_CDASH TRUE)
+set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen3.1")
+set(CTEST_DROP_SITE_CDASH TRUE)

Eigen/Core

@@ -44,7 +44,7 @@
   #endif
 #else
   // Remember that usage of defined() in a #define is undefined by the standard
-  #if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
+  #if (defined __SSE2__) && ( (!defined __GNUC__) || (defined __INTEL_COMPILER) || EIGEN_GNUC_AT_LEAST(4,2) )
     #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
   #endif
 #endif

Eigen/SparseCholesky

@@ -2,6 +2,7 @@
 #define EIGEN_SPARSECHOLESKY_MODULE_H
 
 #include "SparseCore"
+#include "OrderingMethods"
 
 #include "src/Core/util/DisableStupidWarnings.h"
 

Eigen/src/Cholesky/LDLT.h

@@ -277,15 +277,13 @@ template<> struct ldlt_inplace<Lower>
         // are compared; if any diagonal is negligible compared
         // to the largest overall, the algorithm bails.
         cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
-
-        if(sign)
-          *sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
       }
 
       // Finish early if the matrix is not full rank.
       if(biggest_in_corner < cutoff)
       {
         for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
+        if(sign) *sign = 0;
         break;
       }
 
@@ -326,6 +324,16 @@ template<> struct ldlt_inplace<Lower>
       }
       if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
         A21 /= mat.coeffRef(k,k);
+      
+      if(sign)
+      {
+        // LDLT is not guaranteed to work for indefinite matrices, but let's try to get the sign right
+        int newSign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0;
+        if(k == 0)
+          *sign = newSign;
+        else if(*sign != newSign)
+          *sign = 0;
+      }
     }
 
     return true;
@@ -534,8 +542,7 @@ template<typename Derived>
 bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
 {
   eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  const Index size = m_matrix.rows();
-  eigen_assert(size == bAndX.rows());
+  eigen_assert(m_matrix.rows() == bAndX.rows());
 
   bAndX = this->solve(bAndX);
 

Eigen/src/Core/ArrayWrapper.h

@@ -55,7 +55,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
     inline Index outerStride() const { return m_expression.outerStride(); }
     inline Index innerStride() const { return m_expression.innerStride(); }
 
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
     inline const Scalar* data() const { return m_expression.data(); }
 
     inline CoeffReturnType coeff(Index row, Index col) const
@@ -175,7 +175,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
     inline Index outerStride() const { return m_expression.outerStride(); }
     inline Index innerStride() const { return m_expression.innerStride(); }
 
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
     inline const Scalar* data() const { return m_expression.data(); }
 
     inline CoeffReturnType coeff(Index row, Index col) const

Eigen/src/Core/Assign_MKL.h

@@ -210,7 +210,7 @@ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
 EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
 
 // The vm*powx functions are not avaibale in the windows version of MKL.
-#ifdef _WIN32
+#ifndef _WIN32
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
 EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)

Eigen/src/Core/CwiseUnaryView.h

@@ -44,9 +44,10 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
     // "error: no integral type can represent all of the enumerator values
     InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
                              ? int(Dynamic)
-                             : int(MatrixTypeInnerStride)
-                               * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
-    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
+                             : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
+    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
+                             ? int(Dynamic)
+                             : outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
   };
 };
 }
@@ -55,8 +56,7 @@ template<typename ViewOp, typename MatrixType, typename StorageKind>
 class CwiseUnaryViewImpl;
 
 template<typename ViewOp, typename MatrixType>
-class CwiseUnaryView : internal::no_assignment_operator,
-  public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
+class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
 {
   public:
 
@@ -98,6 +98,10 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
     typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
 
     EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
+    
+    inline Scalar* data() { return &coeffRef(0); }
+    inline const Scalar* data() const { return &coeff(0); }
 
     inline Index innerStride() const
     {
@@ -106,7 +110,7 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
 
     inline Index outerStride() const
     {
-      return derived().nestedExpression().outerStride();
+      return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
     }
 
     EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const

Eigen/src/Core/DenseStorage.h

@@ -39,13 +39,24 @@ struct plain_array
   plain_array(constructor_without_unaligned_array_assert) {}
 };
 
-#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
+#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
   #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
+#elif EIGEN_GNUC_AT_LEAST(4,7) 
+  // GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
+  // See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
+  // Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
+  template<typename PtrType>
+  EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
+  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
+    eigen_assert((reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & sizemask) == 0 \
+              && "this assertion is explained here: " \
+              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
+              " **** READ THIS WEB PAGE !!! ****");
 #else
   #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
     eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
               && "this assertion is explained here: " \
-              "http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
+              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
               " **** READ THIS WEB PAGE !!! ****");
 #endif
 

Eigen/src/Core/Functors.h

@@ -533,8 +533,11 @@ template <typename Scalar, bool RandomAccess> struct linspaced_op_impl;
 // linear access for packet ops:
 // 1) initialization
 //   base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0])
-// 2) each step
+// 2) each step (where size is 1 for coeff access or PacketSize for packet access)
 //   base += [size*step, ..., size*step]
+//
+// TODO: Perhaps it's better to initialize lazily (so not in the constructor but in packetOp)
+//       in order to avoid the padd() in operator() ?
 template <typename Scalar>
 struct linspaced_op_impl<Scalar,false>
 {
@@ -543,10 +546,15 @@ struct linspaced_op_impl<Scalar,false>
   linspaced_op_impl(Scalar low, Scalar step) :
   m_low(low), m_step(step),
   m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)),
-  m_base(padd(pset1<Packet>(low),pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
+  m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
 
   template<typename Index>
-  EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
+  EIGEN_STRONG_INLINE const Scalar operator() (Index i) const 
+  { 
+    m_base = padd(m_base, pset1<Packet>(m_step));
+    return m_low+i*m_step; 
+  }
+
   template<typename Index>
   EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); }
 

Eigen/src/Core/PermutationMatrix.h

@@ -105,13 +105,13 @@ class PermutationBase : public EigenBase<Derived>
     #endif
 
     /** \returns the number of rows */
-    inline Index rows() const { return indices().size(); }
+    inline Index rows() const { return Index(indices().size()); }
 
     /** \returns the number of columns */
-    inline Index cols() const { return indices().size(); }
+    inline Index cols() const { return Index(indices().size()); }
 
     /** \returns the size of a side of the respective square matrix, i.e., the number of indices */
-    inline Index size() const { return indices().size(); }
+    inline Index size() const { return Index(indices().size()); }
 
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename DenseDerived>

Eigen/src/Core/PlainObjectBase.h

@@ -551,6 +551,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
       eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
                  : (rows() == other.rows() && cols() == other.cols())))
         && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
+      EIGEN_ONLY_USED_FOR_DEBUG(other);
       #else
       resizeLike(other);
       #endif

Eigen/src/Core/StableNorm.h

@@ -13,6 +13,7 @@
 namespace Eigen { 
 
 namespace internal {
+
 template<typename ExpressionType, typename Scalar>
 inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
 {
@@ -76,21 +77,20 @@ MatrixBase<Derived>::blueNorm() const
   using std::pow;
   using std::min;
   using std::max;
-  static Index nmax = -1;
+  static bool initialized = false;
   static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr;
-  if(nmax <= 0)
+  if(!initialized)
   {
-    int nbig, ibeta, it, iemin, iemax, iexp;
+    int ibeta, it, iemin, iemax, iexp;
     RealScalar abig, eps;
     // This program calculates the machine-dependent constants
-    // bl, b2, slm, s2m, relerr overfl, nmax
+    // bl, b2, slm, s2m, relerr overfl
     // from the "basic" machine-dependent numbers
-    // nbig, ibeta, it, iemin, iemax, rbig.
+    // ibeta, it, iemin, iemax, rbig.
     // The following define the basic machine-dependent constants.
     // For portability, the PORT subprograms "ilmaeh" and "rlmach"
     // are used. For any specific computer, each of the assignment
     // statements can be replaced
-    nbig  = (std::numeric_limits<Index>::max)();            // largest integer
     ibeta = std::numeric_limits<RealScalar>::radix;         // base for floating-point numbers
     it    = std::numeric_limits<RealScalar>::digits;        // number of base-beta digits in mantissa
     iemin = std::numeric_limits<RealScalar>::min_exponent;  // minimum exponent
@@ -111,8 +111,7 @@ MatrixBase<Derived>::blueNorm() const
     eps     = RealScalar(pow(double(ibeta), 1-it));
     relerr  = internal::sqrt(eps);         // tolerance for neglecting asml
     abig    = RealScalar(1.0/eps - 1.0);
-    if (RealScalar(nbig)>abig)  nmax = int(abig);  // largest safe n
-    else                        nmax = nbig;
+    initialized = true;
   }
   Index n = size();
   RealScalar ab2 = b2 / RealScalar(n);

Eigen/src/Core/Transpose.h

@@ -104,6 +104,7 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
 
     typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
     EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
 
     inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
     inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
@@ -252,7 +253,7 @@ struct inplace_transpose_selector;
 template<typename MatrixType>
 struct inplace_transpose_selector<MatrixType,true> { // square matrix
   static void run(MatrixType& m) {
-    m.template triangularView<StrictlyUpper>().swap(m.transpose());
+    m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
   }
 };
 
@@ -260,7 +261,7 @@ template<typename MatrixType>
 struct inplace_transpose_selector<MatrixType,false> { // non square matrix
   static void run(MatrixType& m) {
     if (m.rows()==m.cols())
-      m.template triangularView<StrictlyUpper>().swap(m.transpose());
+      m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
     else
       m = m.transpose().eval();
   }
@@ -353,7 +354,7 @@ struct check_transpose_aliasing_run_time_selector
 {
   static bool run(const Scalar* dest, const OtherDerived& src)
   {
-    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src));
+    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src));
   }
 };
 
@@ -362,8 +363,8 @@ struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseB
 {
   static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src)
   {
-    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.lhs())))
-        || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.rhs())));
+    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs())))
+        || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs())));
   }
 };
 

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

@@ -31,7 +31,8 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
 
   /* the smallest non denormalized float number */
   _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos,  0x00800000);
-
+  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf,     0xff800000);//-1.f/0.f);
+  
   /* natural logarithm computed for 4 simultaneous float
     return NaN for x <= 0
   */
@@ -51,7 +52,8 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
 
   Packet4i emm0;
 
-  Packet4f invalid_mask = _mm_cmple_ps(x, _mm_setzero_ps());
+  Packet4f invalid_mask = _mm_cmplt_ps(x, _mm_setzero_ps());
+  Packet4f iszero_mask = _mm_cmpeq_ps(x, _mm_setzero_ps());
 
   x = pmax(x, p4f_min_norm_pos);  /* cut off denormalized stuff */
   emm0 = _mm_srli_epi32(_mm_castps_si128(x), 23);
@@ -96,7 +98,9 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
   y2 = pmul(e, p4f_cephes_log_q2);
   x = padd(x, y);
   x = padd(x, y2);
-  return _mm_or_ps(x, invalid_mask); // negative arg will be NAN
+  // negative arg will be NAN, 0 will be -INF
+  return _mm_or_ps(_mm_andnot_ps(iszero_mask, _mm_or_ps(x, invalid_mask)),
+                   _mm_and_ps(iszero_mask, p4f_minus_inf));
 }
 
 template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
@@ -370,7 +374,7 @@ Packet4f psqrt<Packet4f>(const Packet4f& _x)
   Packet4f half = pmul(_x, pset1<Packet4f>(.5f));
 
   /* select only the inverse sqrt of non-zero inputs */
-  Packet4f non_zero_mask = _mm_cmpgt_ps(_x, pset1<Packet4f>(std::numeric_limits<float>::epsilon()));
+  Packet4f non_zero_mask = _mm_cmpgt_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)()));
   Packet4f x = _mm_and_ps(non_zero_mask, _mm_rsqrt_ps(_x));
 
   x = pmul(x, psub(pset1<Packet4f>(1.5f), pmul(half, pmul(x,x))));

Eigen/src/Core/products/GeneralBlockPanelKernel.h

@@ -69,8 +69,8 @@ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdi
   * - the number of scalars that fit into a packet (when vectorization is enabled).
   *
   * \sa setCpuCacheSizes */
-template<typename LhsScalar, typename RhsScalar, int KcFactor>
-void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
+template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType>
+void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
 {
   EIGEN_UNUSED_VARIABLE(n);
   // Explanations:
@@ -91,13 +91,13 @@ void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrd
   };
 
   manage_caching_sizes(GetAction, &l1, &l2);
-  k = std::min<std::ptrdiff_t>(k, l1/kdiv);
-  std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
+  k = std::min<SizeType>(k, l1/kdiv);
+  SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
   if(_m<m) m = _m & mr_mask;
 }
 
-template<typename LhsScalar, typename RhsScalar>
-inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
+template<typename LhsScalar, typename RhsScalar, typename SizeType>
+inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
 {
   computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n);
 }

Eigen/src/Core/util/Macros.h

@@ -13,7 +13,7 @@
 
 #define EIGEN_WORLD_VERSION 3
 #define EIGEN_MAJOR_VERSION 1
-#define EIGEN_MINOR_VERSION 2
+#define EIGEN_MINOR_VERSION 4
 
 #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/src/Core/util/Memory.h

@@ -88,11 +88,11 @@ inline void throw_std_bad_alloc()
 /** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned.
   * Fast, but wastes 16 additional bytes of memory. Does not throw any exception.
   */
-inline void* handmade_aligned_malloc(size_t size)
+inline void* handmade_aligned_malloc(std::size_t size)
 {
   void *original = std::malloc(size+16);
   if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<size_t>(original) & ~(size_t(15))) + 16);
+  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(15))) + 16);
   *(reinterpret_cast<void**>(aligned) - 1) = original;
   return aligned;
 }
@@ -108,13 +108,18 @@ inline void handmade_aligned_free(void *ptr)
   * Since we know that our handmade version is based on std::realloc
   * we can use std::realloc to implement efficient reallocation.
   */
-inline void* handmade_aligned_realloc(void* ptr, size_t size, size_t = 0)
+inline void* handmade_aligned_realloc(void* ptr, std::size_t size, std::size_t = 0)
 {
   if (ptr == 0) return handmade_aligned_malloc(size);
   void *original = *(reinterpret_cast<void**>(ptr) - 1);
+  std::ptrdiff_t previous_offset = static_cast<char *>(ptr)-static_cast<char *>(original);
   original = std::realloc(original,size+16);
   if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<size_t>(original) & ~(size_t(15))) + 16);
+  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(15))) + 16);
+  void *previous_aligned = static_cast<char *>(original)+previous_offset;
+  if(aligned!=previous_aligned)
+    std::memmove(aligned, previous_aligned, size);
+  
   *(reinterpret_cast<void**>(aligned) - 1) = original;
   return aligned;
 }
@@ -123,7 +128,7 @@ inline void* handmade_aligned_realloc(void* ptr, size_t size, size_t = 0)
 *** Implementation of generic aligned realloc (when no realloc can be used)***
 *****************************************************************************/
 
-void* aligned_malloc(size_t size);
+void* aligned_malloc(std::size_t size);
 void  aligned_free(void *ptr);
 
 /** \internal
@@ -227,7 +232,7 @@ inline void aligned_free(void *ptr)
     std::free(ptr);
   #elif EIGEN_HAS_MM_MALLOC
     _mm_free(ptr);
-  #elif defined(_MSC_VER)
+  #elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
     _aligned_free(ptr);
   #else
     handmade_aligned_free(ptr);

Eigen/src/Geometry/Quaternion.h

@@ -193,7 +193,8 @@ public:
   *
   * \brief The quaternion class used to represent 3D orientations and rotations
   *
-  * \param _Scalar the scalar type, i.e., the type of the coefficients
+  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
+  * \tparam _Options controls the memory alignement of the coeffecients. Can be \# AutoAlign or \# DontAlign. Default is AutoAlign.
   *
   * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
   * orientations and rotations of objects in three dimensions. Compared to other representations
@@ -304,41 +305,29 @@ typedef Quaternion<double> Quaterniond;
 
 namespace internal {
   template<typename _Scalar, int _Options>
-  struct traits<Map<Quaternion<_Scalar>, _Options> >:
-  traits<Quaternion<_Scalar, _Options> >
+  struct traits<Map<Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
   {
-    typedef _Scalar Scalar;
     typedef Map<Matrix<_Scalar,4,1>, _Options> Coefficients;
-
-    typedef traits<Quaternion<_Scalar, _Options> > TraitsBase;
-    enum {
-      IsAligned = TraitsBase::IsAligned,
-
-      Flags = TraitsBase::Flags
-    };
   };
 }
 
 namespace internal {
   template<typename _Scalar, int _Options>
-  struct traits<Map<const Quaternion<_Scalar>, _Options> >:
-  traits<Quaternion<_Scalar> >
+  struct traits<Map<const Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
   {
-    typedef _Scalar Scalar;
     typedef Map<const Matrix<_Scalar,4,1>, _Options> Coefficients;
-
-    typedef traits<Quaternion<_Scalar, _Options> > TraitsBase;
+    typedef traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > TraitsBase;
     enum {
-      IsAligned = TraitsBase::IsAligned,
       Flags = TraitsBase::Flags & ~LvalueBit
     };
   };
 }
 
-/** \brief Quaternion expression mapping a constant memory buffer
+/** \ingroup Geometry_Module
+  * \brief Quaternion expression mapping a constant memory buffer
   *
-  * \param _Scalar the type of the Quaternion coefficients
-  * \param _Options see class Map
+  * \tparam _Scalar the type of the Quaternion coefficients
+  * \tparam _Options see class Map
   *
   * This is a specialization of class Map for Quaternion. This class allows to view
   * a 4 scalar memory buffer as an Eigen's Quaternion object.
@@ -371,10 +360,11 @@ class Map<const Quaternion<_Scalar>, _Options >
     const Coefficients m_coeffs;
 };
 
-/** \brief Expression of a quaternion from a memory buffer
+/** \ingroup Geometry_Module
+  * \brief Expression of a quaternion from a memory buffer
   *
-  * \param _Scalar the type of the Quaternion coefficients
-  * \param _Options see class Map
+  * \tparam _Scalar the type of the Quaternion coefficients
+  * \tparam _Options see class Map
   *
   * This is a specialization of class Map for Quaternion. This class allows to view
   * a 4 scalar memory buffer as an Eigen's  Quaternion object.

Eigen/src/LU/FullPivLU.h

@@ -577,7 +577,7 @@ struct kernel_retval<FullPivLU<_MatrixType> >
     RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
     Index p = 0;
     for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
+      if(internal::abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
         pivots.coeffRef(p++) = i;
     eigen_internal_assert(p == rank());
 
@@ -645,7 +645,7 @@ struct image_retval<FullPivLU<_MatrixType> >
     RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
     Index p = 0;
     for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
+      if(internal::abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
         pivots.coeffRef(p++) = i;
     eigen_internal_assert(p == rank());
 

Eigen/src/QR/ColPivHouseholderQR.h

@@ -56,6 +56,12 @@ template<typename _MatrixType> class ColPivHouseholderQR
     typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
     typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
     typedef typename HouseholderSequence<MatrixType,HCoeffsType>::ConjugateReturnType HouseholderSequenceType;
+    
+  private:
+    
+    typedef typename PermutationType::Index PermIndexType;
+    
+  public:
 
     /**
     * \brief Default Constructor.
@@ -81,7 +87,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
     ColPivHouseholderQR(Index rows, Index cols)
       : m_qr(rows, cols),
         m_hCoeffs((std::min)(rows,cols)),
-        m_colsPermutation(cols),
+        m_colsPermutation(PermIndexType(cols)),
         m_colsTranspositions(cols),
         m_temp(cols),
         m_colSqNorms(cols),
@@ -91,7 +97,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
     ColPivHouseholderQR(const MatrixType& matrix)
       : m_qr(matrix.rows(), matrix.cols()),
         m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(matrix.cols()),
+        m_colsPermutation(PermIndexType(matrix.cols())),
         m_colsTranspositions(matrix.cols()),
         m_temp(matrix.cols()),
         m_colSqNorms(matrix.cols()),
@@ -436,9 +442,9 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
     m_colSqNorms.tail(cols-k-1) -= m_qr.row(k).tail(cols-k-1).cwiseAbs2();
   }
 
-  m_colsPermutation.setIdentity(cols);
-  for(Index k = 0; k < m_nonzero_pivots; ++k)
-    m_colsPermutation.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
+  m_colsPermutation.setIdentity(PermIndexType(cols));
+  for(PermIndexType k = 0; k < m_nonzero_pivots; ++k)
+    m_colsPermutation.applyTranspositionOnTheRight(PermIndexType(k), PermIndexType(m_colsTranspositions.coeff(k)));
 
   m_det_pq = (number_of_transpositions%2) ? -1 : 1;
   m_isInitialized = true;

Eigen/src/SVD/JacobiSVD.h

@@ -833,17 +833,13 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
     // A = U S V^*
     // So A^{-1} = V S^{-1} U^*
 
+    Matrix<Scalar, Dynamic, Rhs::ColsAtCompileTime, 0, _MatrixType::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime> tmp;
     Index diagSize = (std::min)(dec().rows(), dec().cols());
-    typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
-
     Index nonzeroSingVals = dec().nonzeroSingularValues();
-    invertedSingVals.head(nonzeroSingVals) = dec().singularValues().head(nonzeroSingVals).array().inverse();
-    invertedSingVals.tail(diagSize - nonzeroSingVals).setZero();
-
-    dst = dec().matrixV().leftCols(diagSize)
-        * invertedSingVals.asDiagonal()
-        * dec().matrixU().leftCols(diagSize).adjoint()
-        * rhs();
+    
+    tmp.noalias() = dec().matrixU().leftCols(nonzeroSingVals).adjoint() * rhs();
+    tmp = dec().singularValues().head(nonzeroSingVals).asDiagonal().inverse() * tmp;
+    dst = dec().matrixV().leftCols(nonzeroSingVals) * tmp;
   }
 };
 } // end namespace internal

Eigen/src/SparseCore/SparseSelfAdjointView.h

@@ -209,6 +209,7 @@ class SparseSelfAdjointTimeDenseProduct
 
     template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
     {
+      EIGEN_ONLY_USED_FOR_DEBUG(alpha);
       // TODO use alpha
       eigen_assert(alpha==Scalar(1) && "alpha != 1 is not implemented yet, sorry");
       typedef typename internal::remove_all<Lhs>::type _Lhs;

Eigen/src/SparseCore/SparseVector.h

@@ -202,7 +202,7 @@ class SparseVector
     }
 
     inline SparseVector(const SparseVector& other)
-      : m_size(0)
+      : SparseBase(other), m_size(0)
     {
       *this = other.derived();
     }
@@ -230,7 +230,8 @@ class SparseVector
     template<typename OtherDerived>
     inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
     {
-      if (int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
+      if ( (bool(OtherDerived::IsVectorAtCompileTime) && int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
+          || ((!bool(OtherDerived::IsVectorAtCompileTime)) && ( bool(IsColVector) ? other.cols()>1 : other.rows()>1 )))
         return assign(other.transpose());
       else
         return assign(other);

cmake/language_support.cmake

@@ -24,6 +24,8 @@ function(workaround_9220 language language_works)
   set(text
     "project(test NONE)
     cmake_minimum_required(VERSION 2.6.0)
+    set (CMAKE_Fortran_FLAGS \"${CMAKE_Fortran_FLAGS}\")
+    set (CMAKE_EXE_LINKER_FLAGS \"${CMAKE_EXE_LINKER_FLAGS}\")
     enable_language(${language} OPTIONAL)
   ")
   file(REMOVE_RECURSE ${CMAKE_BINARY_DIR}/language_tests/${language})

doc/C01_TutorialMatrixClass.dox

@@ -98,8 +98,8 @@ Matrix3f a;
 MatrixXf b;
 \endcode
 Here,
-\li \c a is a 3x3 matrix, with a static float[9] array of uninitialized coefficients,
-\li \c b is a dynamic-size matrix whose size is currently 0x0, and whose array of
+\li \c a is a 3-by-3 matrix, with a plain float[9] array of uninitialized coefficients,
+\li \c b is a dynamic-size matrix whose size is currently 0-by-0, and whose array of
 coefficients hasn't yet been allocated at all.
 
 Constructors taking sizes are also available. For matrices, the number of rows is always passed first.
@@ -216,7 +216,7 @@ The simple answer is: use fixed
 sizes for very small sizes where you can, and use dynamic sizes for larger sizes or where you have to. For small sizes,
 especially for sizes smaller than (roughly) 16, using fixed sizes is hugely beneficial
 to performance, as it allows Eigen to avoid dynamic memory allocation and to unroll
-loops. Internally, a fixed-size Eigen matrix is just a plain static array, i.e. doing
+loops. Internally, a fixed-size Eigen matrix is just a plain array, i.e. doing
 \code Matrix4f mymatrix; \endcode
 really amounts to just doing
 \code float mymatrix[16]; \endcode
@@ -231,8 +231,9 @@ member variables.
 The limitation of using fixed sizes, of course, is that this is only possible
 when you know the sizes at compile time. Also, for large enough sizes, say for sizes
 greater than (roughly) 32, the performance benefit of using fixed sizes becomes negligible.
-Worse, trying to create a very large matrix using fixed sizes could result in a stack overflow,
-since Eigen will try to allocate the array as a static array, which by default goes on the stack.
+Worse, trying to create a very large matrix using fixed sizes inside a function could result in a
+stack overflow, since Eigen will try to allocate the array automatically as a local variable, and
+this is normally done on the stack.
 Finally, depending on circumstances, Eigen can also be more aggressive trying to vectorize
 (use SIMD instructions) when dynamic sizes are used, see \ref TopicVectorization "Vectorization".
 

doc/C08_TutorialGeometry.dox

@@ -178,7 +178,7 @@ matNxN = t.linear();
 \endcode</td></tr>
 <tr><td>
 extract the rotation matrix</td><td>\code
-matNxN = t.extractRotation();
+matNxN = t.rotation();
 \endcode</td></tr>
 </table>
 

doc/C09_TutorialSparse.dox

@@ -130,7 +130,7 @@ Describing the \a buildProblem and \a save functions is out of the scope of this
 
 The SparseMatrix and SparseVector classes take three template arguments:
  * the scalar type (e.g., double)
- * the storage order (ColMajor or RowMajor, the default is RowMajor)
+ * the storage order (ColMajor or RowMajor, the default is ColMajor)
  * the inner index type (default is \c int).
 
 As for dense Matrix objects, constructors takes the size of the object.

doc/QuickReference.dox

@@ -55,7 +55,7 @@ All combinations are allowed: you can have a matrix with a fixed number of rows
 Matrix<double, 6, Dynamic>                  // Dynamic number of columns (heap allocation)
 Matrix<double, Dynamic, 2>                  // Dynamic number of rows (heap allocation)
 Matrix<double, Dynamic, Dynamic, RowMajor>  // Fully dynamic, row major (heap allocation)
-Matrix<double, 13, 3>                       // Fully fixed (static allocation)
+Matrix<double, 13, 3>                       // Fully fixed (usually allocated on stack)
 \endcode
 
 In most cases, you can simply use one of the convenience typedefs for \ref matrixtypedefs "matrices" and \ref arraytypedefs "arrays". Some examples:

doc/TopicLinearAlgebraDecompositions.dox

@@ -6,7 +6,6 @@ namespace Eigen {
 \section TopicLinAlgBigTable Catalogue of decompositions offered by Eigen
 
 <table class="manual-vl">
-
     <tr>
         <th class="meta"></th>
         <th class="meta" colspan="5">Generic information, not Eigen-specific</th>

test/array_for_matrix.cpp

@@ -42,10 +42,10 @@ template<typename MatrixType> void array_for_matrix(const MatrixType& m)
   VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
 
   // reductions
-  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.cwiseAbs().maxCoeff());
-  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.cwiseAbs().maxCoeff());
-  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).cwiseAbs().maxCoeff());
-  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).cwiseAbs().maxCoeff());
+  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm());
+  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
+  VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm());
+  VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
   VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>()));
 
   // vector-wise ops

test/diagonal.cpp

@@ -56,12 +56,12 @@ template<typename MatrixType> void diagonal(const MatrixType& m)
     VERIFY_IS_APPROX(m2.template diagonal<N2>()[0], static_cast<Scalar>(6) * m1.template diagonal<N2>()[0]);
 
     m2.diagonal(N1) = 2 * m1.diagonal(N1);
-    VERIFY_IS_APPROX(m2.diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
+    VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
     m2.diagonal(N1)[0] *= 3;
     VERIFY_IS_APPROX(m2.diagonal(N1)[0], static_cast<Scalar>(6) * m1.diagonal(N1)[0]);
 
     m2.diagonal(N2) = 2 * m1.diagonal(N2);
-    VERIFY_IS_APPROX(m2.diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2));
+    VERIFY_IS_APPROX(m2.template diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2));
     m2.diagonal(N2)[0] *= 3;
     VERIFY_IS_APPROX(m2.diagonal(N2)[0], static_cast<Scalar>(6) * m1.diagonal(N2)[0]);
   }

test/geo_quaternion.cpp

@@ -171,23 +171,36 @@ template<typename Scalar, int Options> void quaternion(void)
 
 template<typename Scalar> void mapQuaternion(void){
   typedef Map<Quaternion<Scalar>, Aligned> MQuaternionA;
+  typedef Map<const Quaternion<Scalar>, Aligned> MCQuaternionA;
   typedef Map<Quaternion<Scalar> > MQuaternionUA;
   typedef Map<const Quaternion<Scalar> > MCQuaternionUA;
   typedef Quaternion<Scalar> Quaternionx;
+  typedef Matrix<Scalar,3,1> Vector3;
+  typedef AngleAxis<Scalar> AngleAxisx;
+  
+  Vector3 v0 = Vector3::Random(),
+          v1 = Vector3::Random();
+  Scalar  a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
 
   EIGEN_ALIGN16 Scalar array1[4];
   EIGEN_ALIGN16 Scalar array2[4];
   EIGEN_ALIGN16 Scalar array3[4+1];
   Scalar* array3unaligned = array3+1;
+  
+  MQuaternionA    mq1(array1);
+  MCQuaternionA   mcq1(array1);
+  MQuaternionA    mq2(array2);
+  MQuaternionUA   mq3(array3unaligned);
+  MCQuaternionUA  mcq3(array3unaligned);
 
 //  std::cerr << array1 << " " << array2 << " " << array3 << "\n";
-  MQuaternionA(array1).coeffs().setRandom();
-  (MQuaternionA(array2)) = MQuaternionA(array1);
-  (MQuaternionUA(array3unaligned)) = MQuaternionA(array1);
+  mq1 = AngleAxisx(a, v0.normalized());
+  mq2 = mq1;
+  mq3 = mq1;
 
-  Quaternionx q1 = MQuaternionA(array1);
-  Quaternionx q2 = MQuaternionA(array2);
-  Quaternionx q3 = MQuaternionUA(array3unaligned);
+  Quaternionx q1 = mq1;
+  Quaternionx q2 = mq2;
+  Quaternionx q3 = mq3;
   Quaternionx q4 = MCQuaternionUA(array3unaligned);
 
   VERIFY_IS_APPROX(q1.coeffs(), q2.coeffs());
@@ -197,6 +210,23 @@ template<typename Scalar> void mapQuaternion(void){
   if(internal::packet_traits<Scalar>::Vectorizable)
     VERIFY_RAISES_ASSERT((MQuaternionA(array3unaligned)));
   #endif
+    
+  VERIFY_IS_APPROX(mq1 * (mq1.inverse() * v1), v1);
+  VERIFY_IS_APPROX(mq1 * (mq1.conjugate() * v1), v1);
+  
+  VERIFY_IS_APPROX(mcq1 * (mcq1.inverse() * v1), v1);
+  VERIFY_IS_APPROX(mcq1 * (mcq1.conjugate() * v1), v1);
+  
+  VERIFY_IS_APPROX(mq3 * (mq3.inverse() * v1), v1);
+  VERIFY_IS_APPROX(mq3 * (mq3.conjugate() * v1), v1);
+  
+  VERIFY_IS_APPROX(mcq3 * (mcq3.inverse() * v1), v1);
+  VERIFY_IS_APPROX(mcq3 * (mcq3.conjugate() * v1), v1);
+  
+  VERIFY_IS_APPROX(mq1*mq2, q1*q2);
+  VERIFY_IS_APPROX(mq3*mq2, q3*q2);
+  VERIFY_IS_APPROX(mcq1*mq2, q1*q2);
+  VERIFY_IS_APPROX(mcq3*mq2, q3*q2);
 }
 
 template<typename Scalar> void quaternionAlignment(void){

test/nullary.cpp

@@ -91,6 +91,12 @@ void testVectorType(const VectorType& base)
   scalar.setLinSpaced(1,low,high);
   VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) );
   VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) );
+
+  // regression test for bug 526 (linear vectorized transversal)
+  if (size > 1) {
+    m.tail(size-1).setLinSpaced(low, high);
+    VERIFY_IS_APPROX(m(size-1), high);
+  }
 }
 
 template<typename MatrixType>

test/packetmath.cpp

@@ -40,7 +40,7 @@ template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int s
 {
   for (int i=0; i<size; ++i)
   {
-    if (!internal::isApprox(a[i],b[i]))
+    if (a[i]!=b[i] && !internal::isApprox(a[i],b[i]))
     {
       std::cout << "[" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != " << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "\n";
       return false;

test/sparse.h

@@ -178,5 +178,30 @@ initSparse(double density,
   }
 }
 
+template<typename Scalar> void
+initSparse(double density,
+           Matrix<Scalar,1,Dynamic>& refVec,
+           SparseVector<Scalar,RowMajor>& sparseVec,
+           std::vector<int>* zeroCoords = 0,
+           std::vector<int>* nonzeroCoords = 0)
+{
+  sparseVec.reserve(int(refVec.size()*density));
+  sparseVec.setZero();
+  for(int i=0; i<refVec.size(); i++)
+  {
+    Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
+    if (v!=Scalar(0))
+    {
+      sparseVec.insertBack(i) = v;
+      if (nonzeroCoords)
+        nonzeroCoords->push_back(i);
+    }
+    else if (zeroCoords)
+        zeroCoords->push_back(i);
+    refVec[i] = v;
+  }
+}
+
+
 #include <unsupported/Eigen/SparseExtra>
 #endif // EIGEN_TESTSPARSE_H

test/sparse_product.cpp

@@ -46,8 +46,10 @@ template<typename SparseMatrixType> void sparse_product()
   double density = (std::max)(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
-
-  Scalar s1 = internal::random<Scalar>();
+  typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
+  typedef SparseVector<Scalar,0,Index> ColSpVector;
+  typedef SparseVector<Scalar,RowMajor,Index> RowSpVector;Scalar s1 = internal::random<Scalar>();
+  
   Scalar s2 = internal::random<Scalar>();
 
   // test matrix-matrix product
@@ -117,6 +119,21 @@ template<typename SparseMatrixType> void sparse_product()
     test_outer<SparseMatrixType,DenseMatrix>::run(m2,m4,refMat2,refMat4);
 
     VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6);
+    
+    // sparse matrix * sparse vector
+    ColSpVector cv0(cols), cv1;
+    DenseVector dcv0(cols), dcv1;
+    initSparse(2*density,dcv0, cv0);
+    
+    RowSpVector rv0(depth), rv1;
+    RowDenseVector drv0(depth), drv1(rv1);
+    initSparse(2*density,drv0, rv0);
+    
+    VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3);
+    VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3);
+    VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);
+    VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0);
+    VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
   }
 
   // test matrix - diagonal product

test/sparse_vector.cpp

@@ -82,6 +82,12 @@ template<typename Scalar> void sparse_vector(int rows, int cols)
   VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
   VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
   VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
+  
+  // sparse matrix to sparse vector
+  SparseMatrixType mv1;
+  VERIFY_IS_APPROX((mv1=v1),v1);
+  VERIFY_IS_APPROX(mv1,(v1=mv1));
+  VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
 
 }