Bump to 3.3.2

A.triangularView() = B*A.sefladjointView()*B.adjoint()
case that used to work in 3.2.
(grafted from 655ba783f8
)

Eigen/src/Core/CwiseBinaryOp.h

@@ -46,7 +46,7 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
   typedef typename remove_reference<LhsNested>::type _LhsNested;
   typedef typename remove_reference<RhsNested>::type _RhsNested;
   enum {
-    Flags = _LhsNested::Flags & RowMajorBit
+    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
   };
 };
 } // end namespace internal

Eigen/src/Core/Dot.h

@@ -51,7 +51,8 @@ struct dot_nocheck<T, U, true>
 
 } // end namespace internal
 
-/** \returns the dot product of *this with other.
+/** \fn MatrixBase::dot
+  * \returns the dot product of *this with other.
   *
   * \only_for_vectors
   *

Eigen/src/Core/GeneralProduct.h

@@ -224,50 +224,65 @@ template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
       // on, the other hand it is good for the cache to pack the vector anyways...
       EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
       ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = (ActualDest::InnerStrideAtCompileTime!=1) || ComplexByReal
+      MightCannotUseDest = (!EvalToDestAtCompileTime) || ComplexByReal
     };
 
-    gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
-
-    const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
-    const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
-
-    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                  evalToDest ? dest.data() : static_dest.data());
-
-    if(!evalToDest)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = dest.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      if(!alphaIsCompatible)
-      {
-        MappedDest(actualDestPtr, dest.size()).setZero();
-        compatibleAlpha = RhsScalar(1);
-      }
-      else
-        MappedDest(actualDestPtr, dest.size()) = dest;
-    }
-
     typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
     typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-    general_matrix_vector_product
-        <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-        actualLhs.rows(), actualLhs.cols(),
-        LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-        RhsMapper(actualRhs.data(), actualRhs.innerStride()),
-        actualDestPtr, 1,
-        compatibleAlpha);
+    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
 
-    if (!evalToDest)
+    if(!MightCannotUseDest)
     {
-      if(!alphaIsCompatible)
-        dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
-      else
-        dest = MappedDest(actualDestPtr, dest.size());
+      // shortcut if we are sure to be able to use dest directly,
+      // this ease the compiler to generate cleaner and more optimzized code for most common cases
+      general_matrix_vector_product
+          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
+          actualLhs.rows(), actualLhs.cols(),
+          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
+          dest.data(), 1,
+          compatibleAlpha);
+    }
+    else
+    {
+      gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
+
+      const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
+      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+
+      ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
+                                                    evalToDest ? dest.data() : static_dest.data());
+
+      if(!evalToDest)
+      {
+        #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+        Index size = dest.size();
+        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+        #endif
+        if(!alphaIsCompatible)
+        {
+          MappedDest(actualDestPtr, dest.size()).setZero();
+          compatibleAlpha = RhsScalar(1);
+        }
+        else
+          MappedDest(actualDestPtr, dest.size()) = dest;
+      }
+
+      general_matrix_vector_product
+          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
+          actualLhs.rows(), actualLhs.cols(),
+          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
+          actualDestPtr, 1,
+          compatibleAlpha);
+
+      if (!evalToDest)
+      {
+        if(!alphaIsCompatible)
+          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
+        else
+          dest = MappedDest(actualDestPtr, dest.size());
+      }
     }
   }
 };

Eigen/src/Core/IO.h

@@ -105,7 +105,7 @@ class WithFormat
     }
 
   protected:
-    const typename ExpressionType::Nested m_matrix;
+    typename ExpressionType::Nested m_matrix;
     IOFormat m_format;
 };
 

Eigen/src/Core/Inverse.h

@@ -45,6 +45,7 @@ class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::S
 public:
   typedef typename XprType::StorageIndex StorageIndex;
   typedef typename XprType::PlainObject                       PlainObject;
+  typedef typename XprType::Scalar                            Scalar;
   typedef typename internal::ref_selector<XprType>::type      XprTypeNested;
   typedef typename internal::remove_all<XprTypeNested>::type  XprTypeNestedCleaned;
   typedef typename internal::ref_selector<Inverse>::type Nested;

Eigen/src/Core/PlainObjectBase.h

@@ -58,6 +58,28 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
 
 } // end namespace internal
 
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+namespace doxygen {
+
+// This is a workaround to doxygen not being able to understand the inheritance logic
+// when it is hidden by the dense_xpr_base helper struct.
+// Moreover, doxygen fails to include members that are not documented in the declaration body of
+// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
+// this is why we simply inherits MatrixBase, though this does not make sense.
+
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename Derived> struct dense_xpr_base_dispatcher;
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
+struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
+    : public MatrixBase {};
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
+struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
+    : public ArrayBase {};
+
+} // namespace doxygen
+
 /** \class PlainObjectBase
   * \ingroup Core_Module
   * \brief %Dense storage base class for matrices and arrays.
@@ -65,26 +87,10 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
   * This class can be extended with the help of the plugin mechanism described on the page
   * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
   *
+  * \tparam Derived is the derived type, e.g., a Matrix or Array
+  *
   * \sa \ref TopicClassHierarchy
   */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-namespace doxygen {
-
-// this is a workaround to doxygen not being able to understand the inheritance logic
-// when it is hidden by the dense_xpr_base helper struct.
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename Derived> struct dense_xpr_base_dispatcher;
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
-
-} // namespace doxygen
-
 template<typename Derived>
 class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
 #else
@@ -554,7 +560,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
 
   public:
 
-    /** \copydoc DenseBase::operator=(const EigenBase<OtherDerived>&)
+    /** \brief Copies the generic expression \a other into *this.
+      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
       */
     template<typename OtherDerived>
     EIGEN_DEVICE_FUNC 

Eigen/src/Core/ProductEvaluators.h

@@ -158,10 +158,7 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<
   static EIGEN_STRONG_INLINE
   void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
   {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
     // FIXME shall we handle nested_eval here?
     generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
   }
@@ -176,10 +173,7 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<
   static EIGEN_STRONG_INLINE
   void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
   {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
     // FIXME shall we handle nested_eval here?
     generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
   }
@@ -377,7 +371,6 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
   {
     LhsNested actual_lhs(lhs);
     RhsNested actual_rhs(rhs);
-
     internal::gemv_dense_selector<Side,
                             (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
                             bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)

Eigen/src/Core/SelfAdjointView.h

@@ -45,7 +45,7 @@ struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
 };
 }
 
-// FIXME could also be called SelfAdjointWrapper to be consistent with DiagonalWrapper ??
+
 template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
   : public TriangularBase<SelfAdjointView<_MatrixType, UpLo> >
 {
@@ -60,10 +60,12 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
     /** \brief The type of coefficients in this matrix */
     typedef typename internal::traits<SelfAdjointView>::Scalar Scalar; 
     typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
 
     enum {
       Mode = internal::traits<SelfAdjointView>::Mode,
-      Flags = internal::traits<SelfAdjointView>::Flags
+      Flags = internal::traits<SelfAdjointView>::Flags,
+      TransposeMode = ((Mode & Upper) ? Lower : 0) | ((Mode & Lower) ? Upper : 0)
     };
     typedef typename MatrixType::PlainObject PlainObject;
 
@@ -187,6 +189,36 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
                                    TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
     }
 
+    typedef SelfAdjointView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
+    /** \sa MatrixBase::conjugate() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConjugateReturnType conjugate() const
+    { return ConjugateReturnType(m_matrix.conjugate()); }
+
+    typedef SelfAdjointView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
+    /** \sa MatrixBase::adjoint() const */
+    EIGEN_DEVICE_FUNC
+    inline const AdjointReturnType adjoint() const
+    { return AdjointReturnType(m_matrix.adjoint()); }
+
+    typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
+     /** \sa MatrixBase::transpose() */
+    EIGEN_DEVICE_FUNC
+    inline TransposeReturnType transpose()
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+      typename MatrixType::TransposeReturnType tmp(m_matrix);
+      return TransposeReturnType(tmp);
+    }
+
+    typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
+    /** \sa MatrixBase::transpose() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConstTransposeReturnType transpose() const
+    {
+      return ConstTransposeReturnType(m_matrix.transpose());
+    }
+
     /** \returns a const expression of the main diagonal of the matrix \c *this
       *
       * This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
@@ -287,6 +319,7 @@ public:
 * Implementation of MatrixBase methods
 ***************************************************************************/
 
+/** This is the const version of MatrixBase::selfadjointView() */
 template<typename Derived>
 template<unsigned int UpLo>
 typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
@@ -295,6 +328,15 @@ MatrixBase<Derived>::selfadjointView() const
   return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
 }
 
+/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the current matrix
+  *
+  * The parameter \a UpLo can be either \c #Upper or \c #Lower
+  *
+  * Example: \include MatrixBase_selfadjointView.cpp
+  * Output: \verbinclude MatrixBase_selfadjointView.out
+  *
+  * \sa class SelfAdjointView
+  */
 template<typename Derived>
 template<unsigned int UpLo>
 typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type

Eigen/src/Core/SolveTriangular.h

@@ -161,6 +161,7 @@ struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
 * TriangularView methods
 ***************************************************************************/
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
 template<typename MatrixType, unsigned int Mode>
 template<int Side, typename OtherDerived>
 void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
@@ -188,6 +189,7 @@ TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) co
 {
   return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
 }
+#endif
 
 namespace internal {
 

Eigen/src/Core/TriangularMatrix.h

@@ -470,6 +470,8 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
       * \a Side==OnTheLeft (the default), or the right-inverse-multiply  \a other * inverse(\c *this) if
       * \a Side==OnTheRight.
       *
+      * Note that the template parameter \c Side can be ommitted, in which case \c Side==OnTheLeft
+      *
       * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
       * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
       * is an upper (resp. lower) triangular matrix.
@@ -495,6 +497,8 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
       * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
       * This function will const_cast it, so constness isn't honored here.
       *
+      * Note that the template parameter \c Side can be ommitted, in which case \c Side==OnTheLeft
+      *
       * See TriangularView:solve() for the details.
       */
     template<int Side, typename OtherDerived>
@@ -539,13 +543,14 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
 
     template<typename ProductType>
     EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha);
+    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha, bool beta);
 };
 
 /***************************************************************************
 * Implementation of triangular evaluation/assignment
 ***************************************************************************/
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
 // FIXME should we keep that possibility
 template<typename MatrixType, unsigned int Mode>
 template<typename OtherDerived>
@@ -583,6 +588,7 @@ void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const TriangularBas
   eigen_assert(Mode == int(OtherDerived::Mode));
   internal::call_assignment_no_alias(derived(), other.derived());
 }
+#endif
 
 /***************************************************************************
 * Implementation of TriangularBase methods
@@ -944,8 +950,7 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_
     if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
       dst.resize(dstRows, dstCols);
 
-    dst.setZero();
-    dst._assignProduct(src, 1);
+    dst._assignProduct(src, 1, 0);
   }
 };
 
@@ -956,7 +961,7 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_ass
   typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
   static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
   {
-    dst._assignProduct(src, 1);
+    dst._assignProduct(src, 1, 1);
   }
 };
 
@@ -967,7 +972,7 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_ass
   typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
   static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
   {
-    dst._assignProduct(src, -1);
+    dst._assignProduct(src, -1, 1);
   }
 };
 

Eigen/src/Core/Visitor.h

@@ -194,7 +194,8 @@ struct functor_traits<max_coeff_visitor<Scalar> > {
 
 } // end namespace internal
 
-/** \returns the minimum of all coefficients of *this and puts in *row and *col its location.
+/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
+  * \returns the minimum of all coefficients of *this and puts in *row and *col its location.
   * \warning the result is undefined if \c *this contains NaN.
   *
   * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
@@ -230,7 +231,8 @@ DenseBase<Derived>::minCoeff(IndexType* index) const
   return minVisitor.res;
 }
 
-/** \returns the maximum of all coefficients of *this and puts in *row and *col its location.
+/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
+  * \returns the maximum of all coefficients of *this and puts in *row and *col its location.
   * \warning the result is undefined if \c *this contains NaN. 
   *
   * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()

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

@@ -395,14 +395,11 @@ template<> EIGEN_STRONG_INLINE Packet4d preduxp<Packet4d>(const Packet4d* vecs)
 
 template<> EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a)
 {
-  Packet8f tmp0 = _mm256_hadd_ps(a,_mm256_permute2f128_ps(a,a,1));
-  tmp0 = _mm256_hadd_ps(tmp0,tmp0);
-  return pfirst(_mm256_hadd_ps(tmp0, tmp0));
+  return predux(Packet4f(_mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1))));
 }
 template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a)
 {
-  Packet4d tmp0 = _mm256_hadd_pd(a,_mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_hadd_pd(tmp0,tmp0));
+  return predux(Packet2d(_mm_add_pd(_mm256_castpd256_pd128(a),_mm256_extractf128_pd(a,1))));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4f predux_downto4<Packet8f>(const Packet8f& a)

Eigen/src/Core/arch/AltiVec/Complex.h

@@ -15,14 +15,14 @@ namespace Eigen {
 
 namespace internal {
 
-static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_ZERO_);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
 #ifdef __VSX__
 #if defined(_BIG_ENDIAN)
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
 #else
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
 #endif
 #endif
 

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

@@ -84,8 +84,10 @@ static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
 static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
 static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
 
+#ifdef __POWER8_VECTOR__
 static Packet2l p2l_1023 = { 1023, 1023 };
 static Packet2ul p2ul_52 = { 52, 52 };
+#endif
 
 #endif
 

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

@@ -72,7 +72,7 @@ static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
 static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
 static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
 static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
-static Packet4f p4f_ZERO_ = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
+static Packet4f p4f_MZERO = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
 #ifndef __VSX__
 static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
 #endif
@@ -358,7 +358,7 @@ template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return p4i_
 template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
 template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
 
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b, p4f_ZERO); }
+template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b, p4f_MZERO); }
 template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return a * b; }
 
 template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
@@ -373,7 +373,7 @@ template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const
   t   = vec_nmsub(y_0, b, p4f_ONE);
   y_1 = vec_madd(y_0, t, y_0);
 
-  return vec_madd(a, y_1, p4f_ZERO);
+  return vec_madd(a, y_1, p4f_MZERO);
 #else
   return vec_div(a, b);
 #endif
@@ -766,7 +766,7 @@ static Packet2l  p2l_ONE  = { 1, 1 };
 static Packet2l  p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
 static Packet2d  p2d_ONE  = { 1.0, 1.0 }; 
 static Packet2d  p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
-static Packet2d  p2d_ZERO_ = { -0.0, -0.0 };
+static Packet2d  p2d_MZERO = { -0.0, -0.0 };
 
 #ifdef _BIG_ENDIAN
 static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
@@ -904,7 +904,7 @@ template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return p2d_
 
 template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
 
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_ZERO); }
+template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_MZERO); }
 template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
 
 // for some weird raisons, it has to be overloaded for packet of integers

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

@@ -28,11 +28,13 @@ namespace internal {
 #define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
 #endif
 
-// FIXME NEON has 16 quad registers, but since the current register allocator
-// is so bad, it is much better to reduce it to 8
 #ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#if EIGEN_ARCH_ARM64
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#else
 #define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16 
 #endif
+#endif
 
 typedef float32x2_t Packet2f;
 typedef float32x4_t Packet4f;

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

@@ -28,7 +28,7 @@ namespace internal {
 #endif
 #endif
 
-#if (defined EIGEN_VECTORIZE_AVX) && EIGEN_COMP_GNUC_STRICT && (__GXX_ABI_VERSION < 1004)
+#if (defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW) && (__GXX_ABI_VERSION < 1004)
 // With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
 // have overloads for both types without linking error.
 // One solution is to increase ABI version using -fabi-version=4 (or greater).
@@ -504,30 +504,13 @@ template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs)
 {
   return _mm_hadd_ps(_mm_hadd_ps(vecs[0], vecs[1]),_mm_hadd_ps(vecs[2], vecs[3]));
 }
+
 template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
 {
   return _mm_hadd_pd(vecs[0], vecs[1]);
 }
 
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp0 = _mm_hadd_ps(a,a);
-  return pfirst<Packet4f>(_mm_hadd_ps(tmp0, tmp0));
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return pfirst<Packet2d>(_mm_hadd_pd(a, a)); }
 #else
-// SSE2 versions
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
-}
-
 template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs)
 {
   Packet4f tmp0, tmp1, tmp2;
@@ -548,6 +531,29 @@ template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
 }
 #endif  // SSE3
 
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
+  // (from Nehalem to Haswell)
+// #ifdef EIGEN_VECTORIZE_SSE3
+//   Packet4f tmp = _mm_add_ps(a, vec4f_swizzle1(a,2,3,2,3));
+//   return pfirst<Packet4f>(_mm_hadd_ps(tmp, tmp));
+// #else
+  Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
+  return pfirst<Packet4f>(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
+// #endif
+}
+
+template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
+{
+  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
+  // (from Nehalem to Haswell)
+// #ifdef EIGEN_VECTORIZE_SSE3
+//   return pfirst<Packet2d>(_mm_hadd_pd(a, a));
+// #else
+  return pfirst<Packet2d>(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
+// #endif
+}
 
 #ifdef EIGEN_VECTORIZE_SSSE3
 template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs)

Eigen/src/Core/functors/StlFunctors.h

@@ -72,7 +72,7 @@ template<typename T>
 struct functor_traits<std::not_equal_to<T> >
 { enum { Cost = 1, PacketAccess = false }; };
 
-#if(__cplusplus < 201103L)
+#if (__cplusplus < 201103L) && (EIGEN_COMP_MSVC <= 1900)
 // std::binder* are deprecated since c++11 and will be removed in c++17
 template<typename T>
 struct functor_traits<std::binder2nd<T> >

Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h

@@ -199,7 +199,7 @@ struct general_product_to_triangular_selector;
 template<typename MatrixType, typename ProductType, int UpLo>
 struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
 {
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha)
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
   {
     typedef typename MatrixType::Scalar Scalar;
     
@@ -217,6 +217,9 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
 
     Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
 
+    if(!beta)
+      mat.template triangularView<UpLo>().setZero();
+
     enum {
       StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
       UseLhsDirectly = _ActualLhs::InnerStrideAtCompileTime==1,
@@ -244,7 +247,7 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
 template<typename MatrixType, typename ProductType, int UpLo>
 struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
 {
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha)
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
   {
     typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
     typedef internal::blas_traits<Lhs> LhsBlasTraits;
@@ -260,6 +263,9 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
 
     typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
 
+    if(!beta)
+      mat.template triangularView<UpLo>().setZero();
+
     enum {
       IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
       LhsIsRowMajor = _ActualLhs::Flags&RowMajorBit ? 1 : 0,
@@ -286,11 +292,11 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
 
 template<typename MatrixType, unsigned int UpLo>
 template<typename ProductType>
-TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha)
+TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
 {
   eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
   
-  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha);
+  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);
   
   return derived();
 }

Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h

@@ -33,7 +33,7 @@
 #ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
 #define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
 
-namespace Eigen { 
+namespace Eigen {
 
 namespace internal {
 
@@ -86,8 +86,8 @@ struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,C
   /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \
 \
    BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'T':'N'; \
-   EIGTYPE beta; \
+   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'T':'N'); \
+   EIGTYPE beta(1); \
    BLASFUNC(&uplo, &trans, &n, &k, &numext::real_ref(alpha), lhs, &lda, &numext::real_ref(beta), res, &ldc); \
   } \
 };
@@ -107,7 +107,7 @@ struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,C
    typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \
 \
    BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'C':'N'; \
+   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'C':'N'); \
    RTYPE alpha_, beta_; \
    const EIGTYPE* a_ptr; \
 \

Eigen/src/Core/products/TriangularSolverMatrix.h

@@ -183,7 +183,7 @@ EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conju
     }
   }
 
-/* Optimized triangular solver with multiple left hand sides and the trinagular matrix on the right
+/* Optimized triangular solver with multiple left hand sides and the triangular matrix on the right
  */
 template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
 struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>
@@ -202,6 +202,7 @@ EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conj
     level3_blocking<Scalar,Scalar>& blocking)
   {
     Index rows = otherSize;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
 
     typedef blas_data_mapper<Scalar, Index, ColMajor> LhsMapper;
     typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
@@ -306,9 +307,9 @@ EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conj
               }
               if((Mode & UnitDiag)==0)
               {
-                Scalar b = conj(rhs(j,j));
+                Scalar inv_rjj = RealScalar(1)/conj(rhs(j,j));
                 for (Index i=0; i<actual_mc; ++i)
-                  r[i] /= b;
+                  r[i] *= inv_rjj;
               }
             }
 

Eigen/src/Core/util/Macros.h

@@ -13,7 +13,7 @@
 
 #define EIGEN_WORLD_VERSION 3
 #define EIGEN_MAJOR_VERSION 3
-#define EIGEN_MINOR_VERSION 1
+#define EIGEN_MINOR_VERSION 2
 
 #define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
                                       (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@@ -356,7 +356,7 @@
 #define EIGEN_MAX_CPP_VER 99
 #endif
 
-#if EIGEN_MAX_CPP_VER>=11 && defined(__cplusplus) && (__cplusplus >= 201103L)
+#if EIGEN_MAX_CPP_VER>=11 && (defined(__cplusplus) && (__cplusplus >= 201103L) || EIGEN_COMP_MSVC >= 1900)
 #define EIGEN_HAS_CXX11 1
 #else
 #define EIGEN_HAS_CXX11 0
@@ -497,10 +497,11 @@
 // attribute to maximize inlining. This should only be used when really necessary: in particular,
 // it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
 // FIXME with the always_inline attribute,
-// gcc 3.4.x reports the following compilation error:
+// gcc 3.4.x and 4.1 reports the following compilation error:
 //   Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
 //    : function body not available
-#if EIGEN_GNUC_AT_LEAST(4,0)
+//   See also bug 1367
+#if EIGEN_GNUC_AT_LEAST(4,2)
 #define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
 #else
 #define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE

Eigen/src/Core/util/XprHelper.h

@@ -532,6 +532,15 @@ template <typename B, typename Functor>                   struct cwise_promote_s
 template <typename Functor>                               struct cwise_promote_storage_type<Sparse,Dense,Functor>                             { typedef Sparse ret; };
 template <typename Functor>                               struct cwise_promote_storage_type<Dense,Sparse,Functor>                             { typedef Sparse ret; };
 
+template <typename LhsKind, typename RhsKind, int LhsOrder, int RhsOrder> struct cwise_promote_storage_order {
+  enum { value = LhsOrder };
+};
+
+template <typename LhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<LhsKind,Sparse,LhsOrder,RhsOrder>                { enum { value = RhsOrder }; };
+template <typename RhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<Sparse,RhsKind,LhsOrder,RhsOrder>                { enum { value = LhsOrder }; };
+template <int Order>                                      struct cwise_promote_storage_order<Sparse,Sparse,Order,Order>                       { enum { value = Order }; };
+
+
 /** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
   * The template parameter ProductTag permits to specialize the resulting storage kind wrt to
   * some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.

Eigen/src/Geometry/Hyperplane.h

@@ -217,7 +217,10 @@ public:
   EIGEN_DEVICE_FUNC inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
   {
     if (traits==Affine)
+    {
       normal() = mat.inverse().transpose() * normal();
+      m_coeffs /= normal().norm();
+    }
     else if (traits==Isometry)
       normal() = mat * normal();
     else

Eigen/src/QR/CompleteOrthogonalDecomposition.h

@@ -138,7 +138,7 @@ class CompleteOrthogonalDecomposition {
    * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
    * which \c *this is the complete orthogonal decomposition.
    *
-   * \param B the right-hand sides of the problem to solve.
+   * \param b the right-hand sides of the problem to solve.
    *
    * \returns a solution.
    *

Eigen/src/SparseCore/SparseCwiseBinaryOp.h

@@ -45,7 +45,7 @@ class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
       EIGEN_STATIC_ASSERT((
                 (!internal::is_same<typename internal::traits<Lhs>::StorageKind,
                                     typename internal::traits<Rhs>::StorageKind>::value)
-            ||  ((Lhs::Flags&RowMajorBit) == (Rhs::Flags&RowMajorBit))),
+            ||  ((internal::evaluator<Lhs>::Flags&RowMajorBit) == (internal::evaluator<Rhs>::Flags&RowMajorBit))),
             THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH);
     }
 };
@@ -110,6 +110,7 @@ public:
     EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
     EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
 
@@ -193,6 +194,7 @@ public:
     EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
     EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
 
@@ -280,6 +282,7 @@ public:
     EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
     EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
 
@@ -432,6 +435,7 @@ public:
     EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
     EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
 
@@ -503,6 +507,7 @@ public:
     { return m_functor(m_lhsEval.coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_rhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
     EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
 
@@ -577,6 +582,7 @@ public:
                        m_rhsEval.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
 
     EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
     EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
     EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
 
@@ -621,6 +627,22 @@ protected:
 * Implementation of SparseMatrixBase and SparseCwise functions/operators
 ***************************************************************************/
 
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
 template<typename Derived>
 template<typename OtherDerived>
 EIGEN_STRONG_INLINE Derived &

Eigen/src/SparseCore/SparseCwiseUnaryOp.h

@@ -123,8 +123,10 @@ template<typename Derived>
 EIGEN_STRONG_INLINE Derived&
 SparseMatrixBase<Derived>::operator*=(const Scalar& other)
 {
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
   for (Index j=0; j<outerSize(); ++j)
-    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+    for (EvalIterator i(thisEval,j); i; ++i)
       i.valueRef() *= other;
   return derived();
 }
@@ -133,8 +135,10 @@ template<typename Derived>
 EIGEN_STRONG_INLINE Derived&
 SparseMatrixBase<Derived>::operator/=(const Scalar& other)
 {
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
   for (Index j=0; j<outerSize(); ++j)
-    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+    for (EvalIterator i(thisEval,j); i; ++i)
       i.valueRef() /= other;
   return derived();
 }

Eigen/src/SparseCore/SparseMatrix.h

@@ -32,18 +32,22 @@ namespace Eigen {
   * \tparam _Scalar the scalar type, i.e. the type of the coefficients
   * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
   *                 is ColMajor or RowMajor. The default is 0 which means column-major.
-  * \tparam _Index the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
+  * \tparam _StorageIndex the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
+  *
+  * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type (e.g., int),
+  *          whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index.
+  *          Codes making use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead.
   *
   * This class can be extended with the help of the plugin mechanism described on the page
   * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
   */
 
 namespace internal {
-template<typename _Scalar, int _Options, typename _Index>
-struct traits<SparseMatrix<_Scalar, _Options, _Index> >
+template<typename _Scalar, int _Options, typename _StorageIndex>
+struct traits<SparseMatrix<_Scalar, _Options, _StorageIndex> >
 {
   typedef _Scalar Scalar;
-  typedef _Index StorageIndex;
+  typedef _StorageIndex StorageIndex;
   typedef Sparse StorageKind;
   typedef MatrixXpr XprKind;
   enum {
@@ -56,16 +60,16 @@ struct traits<SparseMatrix<_Scalar, _Options, _Index> >
   };
 };
 
-template<typename _Scalar, int _Options, typename _Index, int DiagIndex>
-struct traits<Diagonal<SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
+template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
+struct traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
 {
-  typedef SparseMatrix<_Scalar, _Options, _Index> MatrixType;
+  typedef SparseMatrix<_Scalar, _Options, _StorageIndex> MatrixType;
   typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
   typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
 
   typedef _Scalar Scalar;
   typedef Dense StorageKind;
-  typedef _Index StorageIndex;
+  typedef _StorageIndex StorageIndex;
   typedef MatrixXpr XprKind;
 
   enum {
@@ -77,9 +81,9 @@ struct traits<Diagonal<SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
   };
 };
 
-template<typename _Scalar, int _Options, typename _Index, int DiagIndex>
-struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
- : public traits<Diagonal<SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
+template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
+struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
+ : public traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
 {
   enum {
     Flags = 0
@@ -88,13 +92,13 @@ struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _Index>, DiagIndex>
 
 } // end namespace internal
 
-template<typename _Scalar, int _Options, typename _Index>
+template<typename _Scalar, int _Options, typename _StorageIndex>
 class SparseMatrix
-  : public SparseCompressedBase<SparseMatrix<_Scalar, _Options, _Index> >
+  : public SparseCompressedBase<SparseMatrix<_Scalar, _Options, _StorageIndex> >
 {
     typedef SparseCompressedBase<SparseMatrix> Base;
     using Base::convert_index;
-    friend class SparseVector<_Scalar,0,_Index>;
+    friend class SparseVector<_Scalar,0,_StorageIndex>;
   public:
     using Base::isCompressed;
     using Base::nonZeros;
@@ -984,11 +988,11 @@ void set_from_triplets(const InputIterator& begin, const InputIterator& end, Spa
   * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
   * be explicitely stored into a std::vector for instance.
   */
-template<typename Scalar, int _Options, typename _Index>
+template<typename Scalar, int _Options, typename _StorageIndex>
 template<typename InputIterators>
-void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
+void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
 {
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_Index> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
 }
 
 /** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:
@@ -1000,17 +1004,17 @@ void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators&
   * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
   * \endcode
   */
-template<typename Scalar, int _Options, typename _Index>
+template<typename Scalar, int _Options, typename _StorageIndex>
 template<typename InputIterators,typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
+void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
 {
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_Index>, DupFunctor>(begin, end, *this, dup_func);
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex>, DupFunctor>(begin, end, *this, dup_func);
 }
 
 /** \internal */
-template<typename Scalar, int _Options, typename _Index>
+template<typename Scalar, int _Options, typename _StorageIndex>
 template<typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_Index>::collapseDuplicates(DupFunctor dup_func)
+void SparseMatrix<Scalar,_Options,_StorageIndex>::collapseDuplicates(DupFunctor dup_func)
 {
   eigen_assert(!isCompressed());
   // TODO, in practice we should be able to use m_innerNonZeros for that task
@@ -1048,9 +1052,9 @@ void SparseMatrix<Scalar,_Options,_Index>::collapseDuplicates(DupFunctor dup_fun
   m_data.resize(m_outerIndex[m_outerSize]);
 }
 
-template<typename Scalar, int _Options, typename _Index>
+template<typename Scalar, int _Options, typename _StorageIndex>
 template<typename OtherDerived>
-EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_Index>& SparseMatrix<Scalar,_Options,_Index>::operator=(const SparseMatrixBase<OtherDerived>& other)
+EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const SparseMatrixBase<OtherDerived>& other)
 {
   EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
         YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
@@ -1121,8 +1125,8 @@ EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_Index>& SparseMatrix<Scalar,_Opt
   }
 }
 
-template<typename _Scalar, int _Options, typename _Index>
-typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Options,_Index>::insert(Index row, Index col)
+template<typename _Scalar, int _Options, typename _StorageIndex>
+typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insert(Index row, Index col)
 {
   eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
   
@@ -1241,8 +1245,8 @@ typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Op
   return insertUncompressed(row,col);
 }
     
-template<typename _Scalar, int _Options, typename _Index>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Options,_Index>::insertUncompressed(Index row, Index col)
+template<typename _Scalar, int _Options, typename _StorageIndex>
+EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertUncompressed(Index row, Index col)
 {
   eigen_assert(!isCompressed());
 
@@ -1273,8 +1277,8 @@ EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& Sparse
   return (m_data.value(p) = 0);
 }
 
-template<typename _Scalar, int _Options, typename _Index>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Options,_Index>::insertCompressed(Index row, Index col)
+template<typename _Scalar, int _Options, typename _StorageIndex>
+EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertCompressed(Index row, Index col)
 {
   eigen_assert(isCompressed());
 
@@ -1382,12 +1386,12 @@ EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& Sparse
 
 namespace internal {
 
-template<typename _Scalar, int _Options, typename _Index>
-struct evaluator<SparseMatrix<_Scalar,_Options,_Index> >
-  : evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_Index> > >
+template<typename _Scalar, int _Options, typename _StorageIndex>
+struct evaluator<SparseMatrix<_Scalar,_Options,_StorageIndex> >
+  : evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > >
 {
-  typedef evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_Index> > > Base;
-  typedef SparseMatrix<_Scalar,_Options,_Index> SparseMatrixType;
+  typedef evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > > Base;
+  typedef SparseMatrix<_Scalar,_Options,_StorageIndex> SparseMatrixType;
   evaluator() : Base() {}
   explicit evaluator(const SparseMatrixType &mat) : Base(mat) {}
 };

Eigen/src/SparseCore/SparseMatrixBase.h

@@ -37,7 +37,11 @@ template<typename Derived> class SparseMatrixBase
     
     typedef typename internal::packet_traits<Scalar>::type PacketScalar;
     typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+    /** The integer type used to \b store indices within a SparseMatrix.
+      * For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
     typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
     typedef typename internal::add_const_on_value_type_if_arithmetic<
                          typename internal::packet_traits<Scalar>::type
                      >::type PacketReturnType;
@@ -213,7 +217,7 @@ template<typename Derived> class SparseMatrixBase
 
       if (Flags&RowMajorBit)
       {
-        const Nested nm(m.derived());
+        Nested nm(m.derived());
         internal::evaluator<NestedCleaned> thisEval(nm);
         for (Index row=0; row<nm.outerSize(); ++row)
         {
@@ -232,7 +236,7 @@ template<typename Derived> class SparseMatrixBase
       }
       else
       {
-        const Nested nm(m.derived());
+        Nested nm(m.derived());
         internal::evaluator<NestedCleaned> thisEval(nm);
         if (m.cols() == 1) {
           Index row = 0;
@@ -265,6 +269,11 @@ template<typename Derived> class SparseMatrixBase
     template<typename OtherDerived>
     Derived& operator-=(const DiagonalBase<OtherDerived>& other);
 
+    template<typename OtherDerived>
+    Derived& operator+=(const EigenBase<OtherDerived> &other);
+    template<typename OtherDerived>
+    Derived& operator-=(const EigenBase<OtherDerived> &other);
+
     Derived& operator*=(const Scalar& other);
     Derived& operator/=(const Scalar& other);
 

Eigen/src/SparseCore/SparseSelfAdjointView.h

@@ -222,14 +222,43 @@ template< typename DstXprType, typename SrcXprType, typename Functor>
 struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse>
 {
   typedef typename DstXprType::StorageIndex StorageIndex;
+  typedef internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> AssignOpType;
+
   template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignOpType&/*func*/)
   {
     internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), dst);
   }
+
+  // FIXME: the handling of += and -= in sparse matrices should be cleanup so that next two overloads could be reduced to:
+  template<typename DestScalar,int StorageOrder,typename AssignFunc>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignFunc& func)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    call_assignment_no_alias_no_transpose(dst, tmp, func);
+  }
+
+  template<typename DestScalar,int StorageOrder>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
+                  const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    dst += tmp;
+  }
+
+  template<typename DestScalar,int StorageOrder>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
+                  const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    dst -= tmp;
+  }
   
   template<typename DestScalar>
-  static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const AssignOpType&/*func*/)
   {
     // TODO directly evaluate into dst;
     SparseMatrix<DestScalar,ColMajor,StorageIndex> tmp(dst.rows(),dst.cols());

Eigen/src/SparseCore/SparseTriangularView.h

@@ -55,7 +55,10 @@ template<typename MatrixType, unsigned int Mode> class TriangularViewImpl<Matrix
       this->solveInPlace(dst);
     }
 
+    /** Applies the inverse of \c *this to the dense vector or matrix \a other, "in-place" */
     template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
+
+    /** Applies the inverse of \c *this to the sparse vector or matrix \a other, "in-place" */
     template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
   
 };

Eigen/src/SparseCore/SparseView.h

@@ -27,6 +27,20 @@ struct traits<SparseView<MatrixType> > : traits<MatrixType>
 
 } // end namespace internal
 
+/** \ingroup SparseCore_Module
+  * \class SparseView
+  *
+  * \brief Expression of a dense or sparse matrix with zero or too small values removed
+  *
+  * \tparam MatrixType the type of the object of which we are removing the small entries
+  *
+  * This class represents an expression of a given dense or sparse matrix with
+  * entries smaller than \c reference * \c epsilon are removed.
+  * It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
+  */
 template<typename MatrixType>
 class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
 {
@@ -190,6 +204,23 @@ struct unary_evaluator<SparseView<ArgType>, IndexBased>
 
 } // end namespace internal
 
+/** \ingroup SparseCore_Module
+  *
+  * \returns a sparse expression of the dense expression \c *this with values smaller than
+  * \a reference * \a epsilon removed.
+  *
+  * This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c S:
+  * \code
+  * MatrixXd D(n,m);
+  * SparseMatrix<double> S;
+  * S = D.sparseView();             // suppress numerical zeros (exact)
+  * S = D.sparseView(reference);
+  * S = D.sparseView(reference,epsilon);
+  * \endcode
+  * where \a reference is a meaningful non zero reference value,
+  * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
+  *
+  * \sa SparseMatrixBase::pruned(), class SparseView */
 template<typename Derived>
 const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
                                                           const typename NumTraits<Scalar>::Real& epsilon) const
@@ -198,7 +229,7 @@ const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& referenc
 }
 
 /** \returns an expression of \c *this with values smaller than
-  * \a reference * \a epsilon are removed.
+  * \a reference * \a epsilon removed.
   *
   * This method is typically used in conjunction with the product of two sparse matrices
   * to automatically prune the smallest values as follows:

Eigen/src/SparseCore/TriangularSolver.h

@@ -171,6 +171,8 @@ struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
 
 } // end namespace internal
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
 template<typename ExpressionType,unsigned int Mode>
 template<typename OtherDerived>
 void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(MatrixBase<OtherDerived>& other) const
@@ -189,6 +191,7 @@ void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(MatrixBase<Oth
   if (copy)
     other = otherCopy;
 }
+#endif
 
 // pure sparse path
 
@@ -286,6 +289,7 @@ struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
 
 } // end namespace internal
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
 template<typename ExpressionType,unsigned int Mode>
 template<typename OtherDerived>
 void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
@@ -304,6 +308,7 @@ void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(SparseMatrixBa
 //   if (copy)
 //     other = otherCopy;
 }
+#endif
 
 } // end namespace Eigen
 

Eigen/src/SparseLU/SparseLU.h

@@ -748,7 +748,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
       else
       {
         Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
         U = A.template triangularView<Upper>().solve(U);
       }
 

Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h

@@ -239,7 +239,7 @@ void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) co
     Index n    = int(X.rows());
     Index nrhs = Index(X.cols());
     const Scalar * Lval = valuePtr();                 // Nonzero values 
-    Matrix<Scalar,Dynamic,Dynamic, ColMajor> work(n, nrhs);     // working vector
+    Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
     work.setZero();
     for (Index k = 0; k <= nsuper(); k ++)
     {
@@ -271,12 +271,12 @@ void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) co
         
         // Triangular solve 
         Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) ); 
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
         U = A.template triangularView<UnitLower>().solve(U); 
         
         // Matrix-vector product 
         new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-        work.block(0, 0, nrow, nrhs) = A * U; 
+        work.topRows(nrow).noalias() = A * U;
         
         //Begin Scatter 
         for (Index j = 0; j < nrhs; j++)

Eigen/src/SuperLUSupport/SuperLUSupport.h

@@ -967,6 +967,7 @@ void SuperILU<MatrixType>::factorize(const MatrixType& a)
   m_factorizationIsOk = true;
 }
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
 template<typename MatrixType>
 template<typename Rhs,typename Dest>
 void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
@@ -1019,6 +1020,8 @@ void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest
 }
 #endif
 
+#endif
+
 } // end namespace Eigen
 
 #endif // EIGEN_SUPERLUSUPPORT_H

Eigen/src/plugins/BlockMethods.h

@@ -818,7 +818,7 @@ inline typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index sta
   return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
 }
 
-/// This is the const version of block<>(Index, Index, Index, Index). */
+/// This is the const version of block<>(Index, Index, Index, Index).
 template<int NRows, int NCols>
 inline const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
                                                               Index blockRows, Index blockCols) const
@@ -832,15 +832,15 @@ inline const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow
 /// Output: \verbinclude MatrixBase_col.out
 ///
 EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa row(), class Block */
+/**
+  * \sa row(), class Block */
 EIGEN_DEVICE_FUNC
 inline ColXpr col(Index i)
 {
   return ColXpr(derived(), i);
 }
 
-/// This is the const version of col(). */
+/// This is the const version of col().
 EIGEN_DEVICE_FUNC
 inline ConstColXpr col(Index i) const
 {
@@ -853,8 +853,8 @@ inline ConstColXpr col(Index i) const
 /// Output: \verbinclude MatrixBase_row.out
 ///
 EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa col(), class Block */
+/**
+  * \sa col(), class Block */
 EIGEN_DEVICE_FUNC
 inline RowXpr row(Index i)
 {

README.md

@@ -1,3 +1,3 @@
-**Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.**
-
-For more information go to http://eigen.tuxfamily.org/.
+**Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.**
+
+For more information go to http://eigen.tuxfamily.org/.

doc/AsciiQuickReference.txt

@@ -36,7 +36,7 @@ A.fill(10);       // Fill A with all 10's.
 MatrixXd::Identity(rows,cols)               // eye(rows,cols)
 C.setIdentity(rows,cols)                    // C = eye(rows,cols)
 MatrixXd::Zero(rows,cols)                   // zeros(rows,cols)
-C.setZero(rows,cols)                        // C = ones(rows,cols)
+C.setZero(rows,cols)                        // C = zeros(rows,cols)
 MatrixXd::Ones(rows,cols)                   // ones(rows,cols)
 C.setOnes(rows,cols)                        // C = ones(rows,cols)
 MatrixXd::Random(rows,cols)                 // rand(rows,cols)*2-1            // MatrixXd::Random returns uniform random numbers in (-1, 1).
@@ -84,7 +84,7 @@ P.bottomRightCorner<rows,cols>()   // P(end-rows+1:end, end-cols+1:end)
 
 // Of particular note is Eigen's swap function which is highly optimized.
 // Eigen                           // Matlab
-R.row(i) = P.col(j);               // R(i, :) = P(:, i)
+R.row(i) = P.col(j);               // R(i, :) = P(:, j)
 R.col(j1).swap(mat1.col(j2));      // R(:, [j1 j2]) = R(:, [j2, j1])
 
 // Views, transpose, etc;

doc/CoeffwiseMathFunctionsTable.dox

@@ -366,7 +366,7 @@ This also means that, unless specified, if the function \c std::foo is available
 <tr>
   <td class="code">
   \anchor cwisetable_isfinite
-  a.\link ArrayBase::isfinite isfinite\endlink(); \n
+  a.\link ArrayBase::isFinite isFinite\endlink(); \n
   \link Eigen::isfinite isfinite\endlink(a);
   </td>
   <td>checks if the given number has finite value</td>
@@ -377,7 +377,7 @@ This also means that, unless specified, if the function \c std::foo is available
 <tr>
   <td class="code">
   \anchor cwisetable_isinf
-  a.\link ArrayBase::isinf isinf\endlink(); \n
+  a.\link ArrayBase::isInf isInf\endlink(); \n
   \link Eigen::isinf isinf\endlink(a);
   </td>
   <td>checks if the given number is infinite</td>
@@ -388,7 +388,7 @@ This also means that, unless specified, if the function \c std::foo is available
 <tr>
   <td class="code">
   \anchor cwisetable_isnan
-  a.\link ArrayBase::isnan isnan\endlink(); \n
+  a.\link ArrayBase::isNaN isNaN\endlink(); \n
   \link Eigen::isnan isnan\endlink(a);
   </td>
   <td>checks if the given number is not a number</td>
@@ -399,7 +399,7 @@ This also means that, unless specified, if the function \c std::foo is available
 <tr>
 <th colspan="4">Error and gamma functions</th>
 </tr>
-<tr> <td colspan="4">  Require \c #include \c <unsupported/Eigen/SpecialFunctions> </td></tr>
+<tr> <td colspan="4">  Require \c \#include \c <unsupported/Eigen/SpecialFunctions> </td></tr>
 <tr>
   <td class="code">
   \anchor cwisetable_erf
@@ -478,7 +478,7 @@ This also means that, unless specified, if the function \c std::foo is available
 <tr>
 <th colspan="4">Special functions</th>
 </tr>
-<tr> <td colspan="4">  Require \c #include \c <unsupported/Eigen/SpecialFunctions> </td></tr>
+<tr> <td colspan="4">  Require \c \#include \c <unsupported/Eigen/SpecialFunctions> </td></tr>
 <tr>
   <td class="code">
   \anchor cwisetable_polygamma
@@ -522,4 +522,4 @@ This also means that, unless specified, if the function \c std::foo is available
 
 */
 
-}
+}

doc/Doxyfile.in

@@ -727,7 +727,8 @@ RECURSIVE              = YES
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                = "${Eigen_SOURCE_DIR}/Eigen/Eigen2Support" \
+EXCLUDE                = "${Eigen_SOURCE_DIR}/Eigen/src/Core/products" \
+                         "${Eigen_SOURCE_DIR}/Eigen/Eigen2Support" \
                          "${Eigen_SOURCE_DIR}/Eigen/src/Eigen2Support" \
                          "${Eigen_SOURCE_DIR}/doc/examples" \
                          "${Eigen_SOURCE_DIR}/doc/special_examples" \
@@ -1591,9 +1592,13 @@ PREDEFINED             = EIGEN_EMPTY_STRUCT \
                          EIGEN_STRONG_INLINE=inline \
                          EIGEN_DEVICE_FUNC= \
                          "EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR)=template<typename OtherDerived> const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const;" \
-                         "EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp<internal::scalar_product_op<typename LHS::Scalar, typename RHS::Scalar >, const LHS, const RHS>"\
+                         "EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp<internal::scalar_product_op<LHS::Scalar,RHS::Scalar>, const LHS, const RHS>"\
+                         "EIGEN_CAT2(a,b)= a ## b"\
+                         "EIGEN_CAT(a,b)=EIGEN_CAT2(a,b)"\
+                         "EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME)=CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<LHS::Scalar, RHS::Scalar>, const LHS, const RHS>"\
                          DOXCOMMA=,
 
+
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then
 # this tag can be used to specify a list of macro names that should be expanded.
 # The macro definition that is found in the sources will be used.
@@ -1617,6 +1622,7 @@ EXPAND_AS_DEFINED      = EIGEN_MAKE_TYPEDEFS \
                          EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL \
                          EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
 
+
 # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then
 # doxygen's preprocessor will remove all references to function-like macros
 # that are alone on a line, have an all uppercase name, and do not end with a

doc/PreprocessorDirectives.dox

@@ -129,7 +129,7 @@ run time. However, these assertions do cost time and can thus be turned off.
 \section TopicPreprocessorDirectivesPlugins Plugins
 
 It is possible to add new methods to many fundamental classes in %Eigen by writing a plugin. As explained in
-the section \ref ExtendingMatrixBase, the plugin is specified by defining a \c EIGEN_xxx_PLUGIN macro. The
+the section \ref TopicCustomizing_Plugins, the plugin is specified by defining a \c EIGEN_xxx_PLUGIN macro. The
 following macros are supported; none of them are defined by default.
 
  - \b EIGEN_ARRAY_PLUGIN - filename of plugin for extending the Array class.

doc/QuickReference.dox

@@ -340,7 +340,7 @@ mat1 = mat2.adjoint();        mat1.adjointInPlace();
 \endcode
 </td></tr>
 <tr><td>
-\link MatrixBase::dot() dot \endlink product \n inner product \matrixworld</td><td>\code
+\link MatrixBase::dot dot \endlink product \n inner product \matrixworld</td><td>\code
 scalar = vec1.dot(vec2);
 scalar = col1.adjoint() * col2;
 scalar = (col1.adjoint() * col2).value();\endcode

doc/snippets/Cwise_boolean_xor.cpp

@@ -0,0 +1,2 @@
+Array3d v(-1,2,1), w(-3,2,3);
+cout << ((v<w) ^ (v<0)) << endl;

doc/snippets/MatrixBase_selfadjointView.cpp

@@ -0,0 +1,6 @@
+Matrix3i m = Matrix3i::Random();
+cout << "Here is the matrix m:" << endl << m << endl;
+cout << "Here is the symmetric matrix extracted from the upper part of m:" << endl
+     << Matrix3i(m.selfadjointView<Upper>()) << endl;
+cout << "Here is the symmetric matrix extracted from the lower part of m:" << endl
+     << Matrix3i(m.selfadjointView<Lower>()) << endl;

test/geo_hyperplane.cpp

@@ -66,12 +66,15 @@ template<typename HyperplaneType> void hyperplane(const HyperplaneType& _plane)
     VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot,Isometry).absDistance(rot * p1), Scalar(1) );
     pl2 = pl1;
     VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling).absDistance((rot*scaling) * p1), Scalar(1) );
+    VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) );
     pl2 = pl1;
     VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*scaling*translation)
                                   .absDistance((rot*scaling*translation) * p1), Scalar(1) );
+    VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) );
     pl2 = pl1;
     VERIFY_IS_MUCH_SMALLER_THAN( pl2.transform(rot*translation,Isometry)
                                  .absDistance((rot*translation) * p1), Scalar(1) );
+    VERIFY_IS_APPROX( pl2.normal().norm(), RealScalar(1) );
   }
 
   // casting

test/main.h

@@ -372,10 +372,10 @@ inline bool test_isApproxOrLessThan(const half& a, const half& b)
 
 // test_relative_error returns the relative difference between a and b as a real scalar as used in isApprox.
 template<typename T1,typename T2>
-typename T1::RealScalar test_relative_error(const EigenBase<T1> &a, const EigenBase<T2> &b)
+typename NumTraits<typename T1::RealScalar>::NonInteger test_relative_error(const EigenBase<T1> &a, const EigenBase<T2> &b)
 {
   using std::sqrt;
-  typedef typename T1::RealScalar RealScalar;
+  typedef typename NumTraits<typename T1::RealScalar>::NonInteger RealScalar;
   typename internal::nested_eval<T1,2>::type ea(a.derived());
   typename internal::nested_eval<T2,2>::type eb(b.derived());
   return sqrt(RealScalar((ea-eb).cwiseAbs2().sum()) / RealScalar((std::min)(eb.cwiseAbs2().sum(),ea.cwiseAbs2().sum())));
@@ -433,9 +433,9 @@ typename T1::RealScalar test_relative_error(const SparseMatrixBase<T1> &a, const
 }
 
 template<typename T1,typename T2>
-typename NumTraits<T1>::Real test_relative_error(const T1 &a, const T2 &b, typename internal::enable_if<internal::is_arithmetic<typename NumTraits<T1>::Real>::value, T1>::type* = 0)
+typename NumTraits<typename NumTraits<T1>::Real>::NonInteger test_relative_error(const T1 &a, const T2 &b, typename internal::enable_if<internal::is_arithmetic<typename NumTraits<T1>::Real>::value, T1>::type* = 0)
 {
-  typedef typename NumTraits<T1>::Real RealScalar; 
+  typedef typename NumTraits<typename NumTraits<T1>::Real>::NonInteger RealScalar;
   return numext::sqrt(RealScalar(numext::abs2(a-b))/RealScalar((numext::mini)(numext::abs2(a),numext::abs2(b))));
 }
 

test/permutationmatrices.cpp

@@ -108,7 +108,14 @@ template<typename MatrixType> void permutationmatrices(const MatrixType& m)
     rm = rp;
     rm.col(i).swap(rm.col(j));
     VERIFY_IS_APPROX(rm, rp2.toDenseMatrix().template cast<Scalar>());
-  }  
+  }
+
+  {
+    // simple compilation check
+    Matrix<Scalar, Cols, Cols> A = rp;
+    Matrix<Scalar, Cols, Cols> B = rp.transpose();
+    VERIFY_IS_APPROX(A, B.transpose());
+  }
 }
 
 template<typename T>

test/product_mmtr.cpp

@@ -62,6 +62,19 @@ template<typename Scalar> void mmtr(int size)
   CHECK_MMTR(matc, Upper, -= (s*sqc).template triangularView<Upper>()*sqc);
   CHECK_MMTR(matc, Lower, = (s*sqr).template triangularView<Lower>()*sqc);
   CHECK_MMTR(matc, Upper, += (s*sqc).template triangularView<Lower>()*sqc);
+
+  // check aliasing
+  ref2 = ref1 = matc;
+  ref1 = sqc.adjoint() * matc * sqc;
+  ref2.template triangularView<Upper>() = ref1.template triangularView<Upper>();
+  matc.template triangularView<Upper>() = sqc.adjoint() * matc * sqc;
+  VERIFY_IS_APPROX(matc, ref2);
+
+  ref2 = ref1 = matc;
+  ref1 = sqc * matc * sqc.adjoint();
+  ref2.template triangularView<Lower>() = ref1.template triangularView<Lower>();
+  matc.template triangularView<Lower>() = sqc * matc * sqc.adjoint();
+  VERIFY_IS_APPROX(matc, ref2);
 }
 
 void test_product_mmtr()

test/product_symm.cpp

@@ -39,6 +39,24 @@ template<typename Scalar, int Size, int OtherSize> void symm(int size = Size, in
   VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>() * (s2*rhs1),
                    rhs13 = (s1*m1) * (s2*rhs1));
 
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).transpose().template selfadjointView<Upper>() * (s2*rhs1),
+                   rhs13 = (s1*m1.transpose()) * (s2*rhs1));
+
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().transpose() * (s2*rhs1),
+                   rhs13 = (s1*m1.transpose()) * (s2*rhs1));
+
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).conjugate().template selfadjointView<Lower>() * (s2*rhs1),
+                   rhs13 = (s1*m1).conjugate() * (s2*rhs1));
+
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().conjugate() * (s2*rhs1),
+                   rhs13 = (s1*m1).conjugate() * (s2*rhs1));
+
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).adjoint().template selfadjointView<Upper>() * (s2*rhs1),
+                   rhs13 = (s1*m1).adjoint() * (s2*rhs1));
+
+  VERIFY_IS_APPROX(rhs12 = (s1*m2).template selfadjointView<Lower>().adjoint() * (s2*rhs1),
+                   rhs13 = (s1*m1).adjoint() * (s2*rhs1));
+
   m2 = m1.template triangularView<Upper>(); rhs12.setRandom(); rhs13 = rhs12;
   m3 = m2.template selfadjointView<Upper>();
   VERIFY_IS_EQUAL(m1, m3);

test/sparse_basic.cpp

@@ -25,6 +25,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
   //const Index outer = ref.outerSize();
 
   typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::RealScalar RealScalar;
   enum { Flags = SparseMatrixType::Flags };
 
   double density = (std::max)(8./(rows*cols), 0.01);
@@ -193,6 +194,17 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
     VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
     VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3));
+
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+    VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3));
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+    VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+
 
     VERIFY_IS_APPROX(m1.sum(), refM1.sum());
 
@@ -431,6 +443,14 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
     m3 = m2.template selfadjointView<Lower>();
     VERIFY_IS_APPROX(m3, refMat3);
 
+    refMat3 += refMat2.template selfadjointView<Lower>();
+    m3 += m2.template selfadjointView<Lower>();
+    VERIFY_IS_APPROX(m3, refMat3);
+
+    refMat3 -= refMat2.template selfadjointView<Lower>();
+    m3 -= m2.template selfadjointView<Lower>();
+    VERIFY_IS_APPROX(m3, refMat3);
+
     // selfadjointView only works for square matrices:
     SparseMatrixType m4(rows, rows+1);
     VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());

test/sparse_block.cpp

@@ -9,6 +9,20 @@
 
 #include "sparse.h"
 
+template<typename T>
+typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type
+innervec(T& A, Index i)
+{
+  return A.row(i);
+}
+
+template<typename T>
+typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type
+innervec(T& A, Index i)
+{
+  return A.col(i);
+}
+
 template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref)
 {
   const Index rows = ref.rows();
@@ -20,9 +34,10 @@ template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& re
   typedef typename SparseMatrixType::StorageIndex StorageIndex;
 
   double density = (std::max)(8./(rows*cols), 0.01);
-  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+  typedef Matrix<Scalar,Dynamic,Dynamic,SparseMatrixType::IsRowMajor?RowMajor:ColMajor> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
+  typedef SparseVector<Scalar> SparseVectorType;
 
   Scalar s1 = internal::random<Scalar>();
   {
@@ -110,15 +125,35 @@ template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& re
     initSparse<Scalar>(density, refMat2, m2);
     Index j0 = internal::random<Index>(0,outer-1);
     Index j1 = internal::random<Index>(0,outer-1);
-    if(SparseMatrixType::IsRowMajor)
-      VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
-    else
-      VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
+    Index r0 = internal::random<Index>(0,rows-1);
+    Index c0 = internal::random<Index>(0,cols-1);
 
-    if(SparseMatrixType::IsRowMajor)
-      VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1));
-    else
-      VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
+    VERIFY_IS_APPROX(m2.innerVector(j0), innervec(refMat2,j0));
+    VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), innervec(refMat2,j0)+innervec(refMat2,j1));
+
+    m2.innerVector(j0) *= Scalar(2);
+    innervec(refMat2,j0) *= Scalar(2);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.row(r0) *= Scalar(3);
+    refMat2.row(r0) *= Scalar(3);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.col(c0) *= Scalar(4);
+    refMat2.col(c0) *= Scalar(4);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.row(r0) /= Scalar(3);
+    refMat2.row(r0) /= Scalar(3);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    m2.col(c0) /= Scalar(4);
+    refMat2.col(c0) /= Scalar(4);
+    VERIFY_IS_APPROX(m2, refMat2);
+
+    SparseVectorType v1;
+    VERIFY_IS_APPROX(v1 = m2.col(c0) * 4, refMat2.col(c0)*4);
+    VERIFY_IS_APPROX(v1 = m2.row(r0) * 4, refMat2.row(r0).transpose()*4);
 
     SparseMatrixType m3(rows,cols);
     m3.reserve(VectorXi::Constant(outer,int(inner/2)));