PR 567: makes all dense solvers inherit SoverBase (LU,Cholesky,QR,SVD).

This changeset also includes:
 * add HouseholderSequence::conjugateIf
 * define int as the StorageIndex type for all dense solvers
 * dedicated unit tests, including assertion checking
 * _check_solve_assertion(): this method can be implemented in derived solver classes to implement custom checks
 * CompleteOrthogonalDecompositions: add applyZOnTheLeftInPlace, fix scalar type in applyZAdjointOnTheLeftInPlace(), add missing assertions
 * Cholesky: add missing assertions
 * FullPivHouseholderQR: Corrected Scalar type in _solve_impl()
 * BDCSVD: Unambiguous return type for ternary operator
 * SVDBase: Corrected Scalar type in _solve_impl()

Eigen/src/Cholesky/LDLT.h

@@ -16,6 +16,15 @@
 namespace Eigen {
 
 namespace internal {
+  template<typename _MatrixType, int _UpLo> struct traits<LDLT<_MatrixType, _UpLo> >
+   : traits<_MatrixType>
+  {
+    typedef MatrixXpr XprKind;
+    typedef SolverStorage StorageKind;
+    typedef int StorageIndex;
+    enum { Flags = 0 };
+  };
+
   template<typename MatrixType, int UpLo> struct LDLT_Traits;
 
   // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
@@ -48,20 +57,19 @@ namespace internal {
   * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
   */
 template<typename _MatrixType, int _UpLo> class LDLT
+        : public SolverBase<LDLT<_MatrixType, _UpLo> >
 {
   public:
     typedef _MatrixType MatrixType;
+    typedef SolverBase<LDLT> Base;
+    friend class SolverBase<LDLT>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
     enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
       UpLo = _UpLo
     };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef typename MatrixType::StorageIndex StorageIndex;
     typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
 
     typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
@@ -180,6 +188,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
       return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
       *
       * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
@@ -197,13 +206,8 @@ template<typename _MatrixType, int _UpLo> class LDLT
       */
     template<typename Rhs>
     inline const Solve<LDLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      eigen_assert(m_matrix.rows()==b.rows()
-                && "LDLT::solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<LDLT, Rhs>(*this, b.derived());
-    }
 
     template<typename Derived>
     bool solveInPlace(MatrixBase<Derived> &bAndX) const;
@@ -259,6 +263,9 @@ template<typename _MatrixType, int _UpLo> class LDLT
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
     #endif
 
   protected:
@@ -559,14 +566,22 @@ template<typename _MatrixType, int _UpLo>
 template<typename RhsType, typename DstType>
 void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
 {
-  eigen_assert(rhs.rows() == rows());
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template<typename _MatrixType,int _UpLo>
+template<bool Conjugate, typename RhsType, typename DstType>
+void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
   // dst = P b
   dst = m_transpositions * rhs;
 
   // dst = L^-1 (P b)
-  matrixL().solveInPlace(dst);
+  // dst = L^-*T (P b)
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
 
-  // dst = D^-1 (L^-1 P b)
+  // dst = D^-* (L^-1 P b)
+  // dst = D^-1 (L^-*T P b)
   // more precisely, use pseudo-inverse of D (see bug 241)
   using std::abs;
   const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
@@ -578,7 +593,6 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) cons
   // Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
   // Using numeric_limits::min() gives us more robustness to denormals.
   RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
-
   for (Index i = 0; i < vecD.size(); ++i)
   {
     if(abs(vecD(i)) > tolerance)
@@ -587,10 +601,12 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) cons
       dst.row(i).setZero();
   }
 
-  // dst = L^-T (D^-1 L^-1 P b)
+  // dst = L^-* (D^-* L^-1 P b)
-  matrixU().solveInPlace(dst);
+  // dst = L^-T (D^-1 L^-*T P b)
+  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
 
-  // dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
+  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
+  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
   dst = m_transpositions.transpose() * dst;
 }
 #endif

Eigen/src/Cholesky/LLT.h

@@ -13,6 +13,16 @@
 namespace Eigen {
 
 namespace internal{
+
+template<typename _MatrixType, int _UpLo> struct traits<LLT<_MatrixType, _UpLo> >
+ : traits<_MatrixType>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
 template<typename MatrixType, int UpLo> struct LLT_Traits;
 }
 
@@ -54,18 +64,17 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
   * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
   */
 template<typename _MatrixType, int _UpLo> class LLT
+        : public SolverBase<LLT<_MatrixType, _UpLo> >
 {
   public:
     typedef _MatrixType MatrixType;
+    typedef SolverBase<LLT> Base;
+    friend class SolverBase<LLT>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
     enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef typename MatrixType::StorageIndex StorageIndex;
 
     enum {
       PacketSize = internal::packet_traits<Scalar>::size,
@@ -129,6 +138,7 @@ template<typename _MatrixType, int _UpLo> class LLT
       return Traits::getL(m_matrix);
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
       *
       * Since this LLT class assumes anyway that the matrix A is invertible, the solution
@@ -141,13 +151,8 @@ template<typename _MatrixType, int _UpLo> class LLT
       */
     template<typename Rhs>
     inline const Solve<LLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      eigen_assert(m_matrix.rows()==b.rows()
-                && "LLT::solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<LLT, Rhs>(*this, b.derived());
-    }
 
     template<typename Derived>
     void solveInPlace(const MatrixBase<Derived> &bAndX) const;
@@ -205,6 +210,9 @@ template<typename _MatrixType, int _UpLo> class LLT
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
     #endif
 
   protected:
@@ -475,9 +483,18 @@ LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v,
 template<typename _MatrixType,int _UpLo>
 template<typename RhsType, typename DstType>
 void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template<typename _MatrixType,int _UpLo>
+template<bool Conjugate, typename RhsType, typename DstType>
+void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
 {
     dst = rhs;
-  solveInPlace(dst);
+
+    matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+    matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
 }
 #endif
 

Eigen/src/Core/SolverBase.h

@@ -14,8 +14,35 @@ namespace Eigen {
 
 namespace internal {
 
+template<typename Derived>
+struct solve_assertion {
+    template<bool Transpose_, typename Rhs>
+    static void run(const Derived& solver, const Rhs& b) { solver.template _check_solve_assertion<Transpose_>(b); }
+};
 
+template<typename Derived>
+struct solve_assertion<Transpose<Derived> >
+{
+    typedef Transpose<Derived> type;
 
+    template<bool Transpose_, typename Rhs>
+    static void run(const type& transpose, const Rhs& b)
+    {
+        internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<true>(transpose.nestedExpression(), b);
+    }
+};
+
+template<typename Scalar, typename Derived>
+struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > >
+{
+    typedef CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > type;
+
+    template<bool Transpose_, typename Rhs>
+    static void run(const type& adjoint, const Rhs& b)
+    {
+        internal::solve_assertion<typename internal::remove_all<Transpose<Derived> >::type>::template run<true>(adjoint.nestedExpression(), b);
+    }
+};
 } // end namespace internal
 
 /** \class SolverBase
@@ -35,7 +62,7 @@ namespace internal {
   *
   * \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation errors.
   *
-  * \sa class PartialPivLU, class FullPivLU
+  * \sa class PartialPivLU, class FullPivLU, class HouseholderQR, class ColPivHouseholderQR, class FullPivHouseholderQR, class CompleteOrthogonalDecomposition, class LLT, class LDLT, class SVDBase
   */
 template<typename Derived>
 class SolverBase : public EigenBase<Derived>
@@ -46,6 +73,9 @@ class SolverBase : public EigenBase<Derived>
     typedef typename internal::traits<Derived>::Scalar Scalar;
     typedef Scalar CoeffReturnType;
 
+    template<typename Derived_>
+    friend struct internal::solve_assertion;
+
     enum {
       RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
       ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
@@ -75,7 +105,7 @@ class SolverBase : public EigenBase<Derived>
     inline const Solve<Derived, Rhs>
     solve(const MatrixBase<Rhs>& b) const
     {
-      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+      internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<false>(derived(), b);
       return Solve<Derived, Rhs>(derived(), b.derived());
     }
 
@@ -113,6 +143,12 @@ class SolverBase : public EigenBase<Derived>
     }
 
   protected:
+
+    template<bool Transpose_, typename Rhs>
+    void _check_solve_assertion(const Rhs& b) const {
+        eigen_assert(derived().m_isInitialized && "Solver is not initialized.");
+        eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b");
+    }
 };
 
 namespace internal {

Eigen/src/Core/util/ForwardDeclarations.h

@@ -260,6 +260,7 @@ template<typename MatrixType> class HouseholderQR;
 template<typename MatrixType> class ColPivHouseholderQR;
 template<typename MatrixType> class FullPivHouseholderQR;
 template<typename MatrixType> class CompleteOrthogonalDecomposition;
+template<typename MatrixType> class SVDBase;
 template<typename MatrixType, int QRPreconditioner = ColPivHouseholderQRPreconditioner> class JacobiSVD;
 template<typename MatrixType> class BDCSVD;
 template<typename MatrixType, int UpLo = Lower> class LLT;

Eigen/src/Householder/HouseholderSequence.h

@@ -156,6 +156,12 @@ template<typename VectorsType, typename CoeffsType, int Side> class HouseholderS
       Side
     > TransposeReturnType;
 
+    typedef HouseholderSequence<
+      typename internal::add_const<VectorsType>::type,
+      typename internal::add_const<CoeffsType>::type,
+      Side
+    > ConstHouseholderSequence;
+
     /** \brief Constructor.
       * \param[in]  v      %Matrix containing the essential parts of the Householder vectors
       * \param[in]  h      Vector containing the Householder coefficients
@@ -244,6 +250,18 @@ template<typename VectorsType, typename CoeffsType, int Side> class HouseholderS
              .setShift(m_shift);
     }
 
+    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+     *           returns \c *this otherwise.
+     */
+    template<bool Cond>
+    EIGEN_DEVICE_FUNC
+    inline typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type
+    conjugateIf() const
+    {
+      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type ReturnType;
+      return ReturnType(m_vectors.template conjugateIf<Cond>(), m_coeffs.template conjugateIf<Cond>());
+    }
+
     /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
     AdjointReturnType adjoint() const
     {

Eigen/src/LU/FullPivLU.h

@@ -63,6 +63,7 @@ template<typename _MatrixType> class FullPivLU
   public:
     typedef _MatrixType MatrixType;
     typedef SolverBase<FullPivLU> Base;
+    friend class SolverBase<FullPivLU>;
 
     EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
     enum {
@@ -218,6 +219,7 @@ template<typename _MatrixType> class FullPivLU
       return internal::image_retval<FullPivLU>(*this, originalMatrix);
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** \return a solution x to the equation Ax=b, where A is the matrix of which
       * *this is the LU decomposition.
       *
@@ -237,14 +239,10 @@ template<typename _MatrixType> class FullPivLU
       *
       * \sa TriangularView::solve(), kernel(), inverse()
       */
-    // FIXME this is a copy-paste of the base-class member to add the isInitialized assertion.
     template<typename Rhs>
     inline const Solve<FullPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return Solve<FullPivLU, Rhs>(*this, b.derived());
-    }
 
     /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
         the LU decomposition.
@@ -755,7 +753,6 @@ void FullPivLU<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
   const Index rows = this->rows(),
               cols = this->cols(),
               nonzero_pivots = this->rank();
-  eigen_assert(rhs.rows() == rows);
   const Index smalldim = (std::min)(rows, cols);
 
   if(nonzero_pivots == 0)
@@ -805,7 +802,6 @@ void FullPivLU<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType
 
   const Index rows = this->rows(), cols = this->cols(),
     nonzero_pivots = this->rank();
-   eigen_assert(rhs.rows() == cols);
   const Index smalldim = (std::min)(rows, cols);
 
   if(nonzero_pivots == 0)

Eigen/src/LU/PartialPivLU.h

@@ -80,6 +80,8 @@ template<typename _MatrixType> class PartialPivLU
 
     typedef _MatrixType MatrixType;
     typedef SolverBase<PartialPivLU> Base;
+    friend class SolverBase<PartialPivLU>;
+
     EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
     enum {
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
@@ -152,6 +154,7 @@ template<typename _MatrixType> class PartialPivLU
       return m_p;
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** This method returns the solution x to the equation Ax=b, where A is the matrix of which
       * *this is the LU decomposition.
       *
@@ -169,14 +172,10 @@ template<typename _MatrixType> class PartialPivLU
       *
       * \sa TriangularView::solve(), inverse(), computeInverse()
       */
-    // FIXME this is a copy-paste of the base-class member to add the isInitialized assertion.
     template<typename Rhs>
     inline const Solve<PartialPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return Solve<PartialPivLU, Rhs>(*this, b.derived());
-    }
 
     /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
         the LU decomposition.
@@ -231,8 +230,6 @@ template<typename _MatrixType> class PartialPivLU
       * Step 3: replace c by the solution x to Ux = c.
       */
 
-      eigen_assert(rhs.rows() == m_lu.rows());
-
       // Step 1
       dst = permutationP() * rhs;
 

Eigen/src/QR/ColPivHouseholderQR.h

@@ -17,6 +17,9 @@ namespace internal {
 template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> >
  : traits<_MatrixType>
 {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
   enum { Flags = 0 };
 };
 
@@ -46,20 +49,19 @@ template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> >
   * \sa MatrixBase::colPivHouseholderQr()
   */
 template<typename _MatrixType> class ColPivHouseholderQR
+        : public SolverBase<ColPivHouseholderQR<_MatrixType> >
 {
   public:
 
     typedef _MatrixType MatrixType;
+    typedef SolverBase<ColPivHouseholderQR> Base;
+    friend class SolverBase<ColPivHouseholderQR>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(ColPivHouseholderQR)
     enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    // FIXME should be int
-    typedef typename MatrixType::StorageIndex StorageIndex;
     typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
     typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
     typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
@@ -156,6 +158,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
       computeInPlace();
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
       * *this is the QR decomposition, if any exists.
       *
@@ -172,11 +175,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
       */
     template<typename Rhs>
     inline const Solve<ColPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return Solve<ColPivHouseholderQR, Rhs>(*this, b.derived());
-    }
 
     HouseholderSequenceType householderQ() const;
     HouseholderSequenceType matrixQ() const
@@ -417,6 +417,9 @@ template<typename _MatrixType> class ColPivHouseholderQR
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
     #endif
 
   protected:
@@ -583,8 +586,6 @@ template<typename _MatrixType>
 template<typename RhsType, typename DstType>
 void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
 {
-  eigen_assert(rhs.rows() == rows());
-
   const Index nonzero_pivots = nonzeroPivots();
 
   if(nonzero_pivots == 0)
@@ -604,6 +605,31 @@ void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &
   for(Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
   for(Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
 }
+
+template<typename _MatrixType>
+template<bool Conjugate, typename RhsType, typename DstType>
+void ColPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index nonzero_pivots = nonzeroPivots();
+
+  if(nonzero_pivots == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_colsPermutation.transpose()*rhs);
+
+  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
+        .template triangularView<Upper>()
+        .transpose().template conjugateIf<Conjugate>()
+        .solveInPlace(c.topRows(nonzero_pivots));
+
+  dst.topRows(nonzero_pivots) = c.topRows(nonzero_pivots);
+  dst.bottomRows(rows()-nonzero_pivots).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(nonzero_pivots).template conjugateIf<!Conjugate>() );
+}
 #endif
 
 namespace internal {

Eigen/src/QR/CompleteOrthogonalDecomposition.h

@@ -16,6 +16,9 @@ namespace internal {
 template <typename _MatrixType>
 struct traits<CompleteOrthogonalDecomposition<_MatrixType> >
     : traits<_MatrixType> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
   enum { Flags = 0 };
 };
 
@@ -44,19 +47,21 @@ struct traits<CompleteOrthogonalDecomposition<_MatrixType> >
   * 
   * \sa MatrixBase::completeOrthogonalDecomposition()
   */
-template <typename _MatrixType>
+template <typename _MatrixType> class CompleteOrthogonalDecomposition
-class CompleteOrthogonalDecomposition {
+          : public SolverBase<CompleteOrthogonalDecomposition<_MatrixType> >
+{
  public:
   typedef _MatrixType MatrixType;
+  typedef SolverBase<CompleteOrthogonalDecomposition> Base;
+
+  template<typename Derived>
+  friend struct internal::solve_assertion;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CompleteOrthogonalDecomposition)
   enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
     MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
     MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
   };
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
   typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
   typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime>
       PermutationType;
@@ -133,7 +138,7 @@ class CompleteOrthogonalDecomposition {
     computeInPlace();
   } 
 
-
+  #ifdef EIGEN_PARSED_BY_DOXYGEN
   /** This method computes the minimum-norm solution X to a least squares
    * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
    * which \c *this is the complete orthogonal decomposition.
@@ -145,11 +150,8 @@ class CompleteOrthogonalDecomposition {
    */
   template <typename Rhs>
   inline const Solve<CompleteOrthogonalDecomposition, Rhs> solve(
-      const MatrixBase<Rhs>& b) const {
+      const MatrixBase<Rhs>& b) const;
-    eigen_assert(m_cpqr.m_isInitialized &&
+  #endif
-                 "CompleteOrthogonalDecomposition is not initialized.");
-    return Solve<CompleteOrthogonalDecomposition, Rhs>(*this, b.derived());
-  }
 
   HouseholderSequenceType householderQ(void) const;
   HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); }
@@ -158,8 +160,8 @@ class CompleteOrthogonalDecomposition {
    */
   MatrixType matrixZ() const {
     MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols());
-    applyZAdjointOnTheLeftInPlace(Z);
+    applyZOnTheLeftInPlace<false>(Z);
-    return Z.adjoint();
+    return Z;
   }
 
   /** \returns a reference to the matrix where the complete orthogonal
@@ -275,6 +277,7 @@ class CompleteOrthogonalDecomposition {
    */
   inline const Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const
   {
+    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
     return Inverse<CompleteOrthogonalDecomposition>(*this);
   }
 
@@ -368,6 +371,9 @@ class CompleteOrthogonalDecomposition {
 #ifndef EIGEN_PARSED_BY_DOXYGEN
   template <typename RhsType, typename DstType>
   void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template<bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
 #endif
 
  protected:
@@ -375,8 +381,21 @@ class CompleteOrthogonalDecomposition {
     EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
   }
 
+  template<bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+      eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
+      eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "CompleteOrthogonalDecomposition::solve(): invalid number of rows of the right hand side matrix b");
+  }
+
   void computeInPlace();
 
+  /** Overwrites \b rhs with \f$ \mathbf{Z} * \mathbf{rhs} \f$ or
+   *  \f$ \mathbf{\overline Z} * \mathbf{rhs} \f$ if \c Conjugate 
+   *  is set to \c true.
+   */
+  template <bool Conjugate, typename Rhs>
+  void applyZOnTheLeftInPlace(Rhs& rhs) const;
+
   /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
    */
   template <typename Rhs>
@@ -464,6 +483,28 @@ void CompleteOrthogonalDecomposition<MatrixType>::computeInPlace()
   }
 }
 
+template <typename MatrixType>
+template <bool Conjugate, typename Rhs>
+void CompleteOrthogonalDecomposition<MatrixType>::applyZOnTheLeftInPlace(
+    Rhs& rhs) const {
+  const Index cols = this->cols();
+  const Index nrhs = rhs.cols();
+  const Index rank = this->rank();
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  for (Index k = rank-1; k >= 0; --k) {
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+    rhs.middleRows(rank - 1, cols - rank + 1)
+        .applyHouseholderOnTheLeft(
+            matrixQTZ().row(k).tail(cols - rank).transpose().template conjugateIf<!Conjugate>(), zCoeffs().template conjugateIf<Conjugate>()(k),
+            &temp(0));
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+  }
+}
+
 template <typename MatrixType>
 template <typename Rhs>
 void CompleteOrthogonalDecomposition<MatrixType>::applyZAdjointOnTheLeftInPlace(
@@ -471,7 +512,7 @@ void CompleteOrthogonalDecomposition<MatrixType>::applyZAdjointOnTheLeftInPlace(
   const Index cols = this->cols();
   const Index nrhs = rhs.cols();
   const Index rank = this->rank();
-  Matrix<typename MatrixType::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
   for (Index k = 0; k < rank; ++k) {
     if (k != rank - 1) {
       rhs.row(k).swap(rhs.row(rank - 1));
@@ -491,8 +532,6 @@ template <typename _MatrixType>
 template <typename RhsType, typename DstType>
 void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl(
     const RhsType& rhs, DstType& dst) const {
-  eigen_assert(rhs.rows() == this->rows());
-
   const Index rank = this->rank();
   if (rank == 0) {
     dst.setZero();
@@ -520,6 +559,34 @@ void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl(
   // Undo permutation to get x = P^{-1} * y.
   dst = colsPermutation() * dst;
 }
+
+template<typename _MatrixType>
+template<bool Conjugate, typename RhsType, typename DstType>
+void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index rank = this->rank();
+
+  if (rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(colsPermutation().transpose()*rhs);
+
+  if (rank < cols()) {
+    applyZOnTheLeftInPlace<!Conjugate>(c);
+  }
+
+  matrixT().topLeftCorner(rank, rank)
+           .template triangularView<Upper>()
+           .transpose().template conjugateIf<Conjugate>()
+           .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows()-rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
+}
 #endif
 
 namespace internal {

Eigen/src/QR/FullPivHouseholderQR.h

@@ -18,6 +18,9 @@ namespace internal {
 template<typename _MatrixType> struct traits<FullPivHouseholderQR<_MatrixType> >
  : traits<_MatrixType>
 {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
   enum { Flags = 0 };
 };
 
@@ -55,20 +58,19 @@ struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
   * \sa MatrixBase::fullPivHouseholderQr()
   */
 template<typename _MatrixType> class FullPivHouseholderQR
+        : public SolverBase<FullPivHouseholderQR<_MatrixType> >
 {
   public:
 
     typedef _MatrixType MatrixType;
+    typedef SolverBase<FullPivHouseholderQR> Base;
+    friend class SolverBase<FullPivHouseholderQR>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR)
     enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    // FIXME should be int
-    typedef typename MatrixType::StorageIndex StorageIndex;
     typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType;
     typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
     typedef Matrix<StorageIndex, 1,
@@ -156,6 +158,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
       computeInPlace();
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
       * \c *this is the QR decomposition.
       *
@@ -173,11 +176,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
       */
     template<typename Rhs>
     inline const Solve<FullPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return Solve<FullPivHouseholderQR, Rhs>(*this, b.derived());
-    }
 
     /** \returns Expression object representing the matrix Q
       */
@@ -396,6 +396,9 @@ template<typename _MatrixType> class FullPivHouseholderQR
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
     #endif
 
   protected:
@@ -498,15 +501,15 @@ void FullPivHouseholderQR<MatrixType>::computeInPlace()
       m_nonzero_pivots = k;
       for(Index i = k; i < size; i++)
       {
-        m_rows_transpositions.coeffRef(i) = i;
+        m_rows_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_cols_transpositions.coeffRef(i) = i;
+        m_cols_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
         m_hCoeffs.coeffRef(i) = Scalar(0);
       }
       break;
     }
 
-    m_rows_transpositions.coeffRef(k) = row_of_biggest_in_corner;
+    m_rows_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
-    m_cols_transpositions.coeffRef(k) = col_of_biggest_in_corner;
+    m_cols_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
     if(k != row_of_biggest_in_corner) {
       m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k));
       ++number_of_transpositions;
@@ -540,7 +543,6 @@ template<typename _MatrixType>
 template<typename RhsType, typename DstType>
 void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
 {
-  eigen_assert(rhs.rows() == rows());
   const Index l_rank = rank();
 
   // FIXME introduce nonzeroPivots() and use it here. and more generally,
@@ -553,7 +555,7 @@ void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType
 
   typename RhsType::PlainObject c(rhs);
 
-  Matrix<Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols());
+  Matrix<typename RhsType::Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols());
   for (Index k = 0; k < l_rank; ++k)
   {
     Index remainingSize = rows()-k;
@@ -570,6 +572,42 @@ void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType
   for(Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i);
   for(Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero();
 }
+
+template<typename _MatrixType>
+template<bool Conjugate, typename RhsType, typename DstType>
+void FullPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index l_rank = rank();
+
+  if(l_rank == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_cols_permutation.transpose()*rhs);
+
+  m_qr.topLeftCorner(l_rank, l_rank)
+         .template triangularView<Upper>()
+         .transpose().template conjugateIf<Conjugate>()
+         .solveInPlace(c.topRows(l_rank));
+
+  dst.topRows(l_rank) = c.topRows(l_rank);
+  dst.bottomRows(rows()-l_rank).setZero();
+
+  Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols());
+  const Index size = (std::min)(rows(), cols());
+  for (Index k = size-1; k >= 0; --k)
+  {
+    Index remainingSize = rows()-k;
+
+    dst.bottomRightCorner(remainingSize, dst.cols())
+       .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1).template conjugateIf<!Conjugate>(),
+                                  m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0));
+
+    dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k)));
+  }
+}
 #endif
 
 namespace internal {

Eigen/src/QR/HouseholderQR.h

@@ -14,6 +14,18 @@
 
 namespace Eigen { 
 
+namespace internal {
+template<typename _MatrixType> struct traits<HouseholderQR<_MatrixType> >
+ : traits<_MatrixType>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+} // end namespace internal
+
 /** \ingroup QR_Module
   *
   *
@@ -42,20 +54,19 @@ namespace Eigen {
   * \sa MatrixBase::householderQr()
   */
 template<typename _MatrixType> class HouseholderQR
+        : public SolverBase<HouseholderQR<_MatrixType> >
 {
   public:
 
     typedef _MatrixType MatrixType;
+    typedef SolverBase<HouseholderQR> Base;
+    friend class SolverBase<HouseholderQR>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(HouseholderQR)
     enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    // FIXME should be int
-    typedef typename MatrixType::StorageIndex StorageIndex;
     typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
     typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
     typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
@@ -121,6 +132,7 @@ template<typename _MatrixType> class HouseholderQR
       computeInPlace();
     }
 
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
     /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
       * *this is the QR decomposition, if any exists.
       *
@@ -137,11 +149,8 @@ template<typename _MatrixType> class HouseholderQR
       */
     template<typename Rhs>
     inline const Solve<HouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
+    solve(const MatrixBase<Rhs>& b) const;
-    {
+    #endif
-      eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-      return Solve<HouseholderQR, Rhs>(*this, b.derived());
-    }
 
     /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations.
       *
@@ -214,6 +223,9 @@ template<typename _MatrixType> class HouseholderQR
     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename RhsType, typename DstType>
     void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
     #endif
 
   protected:
@@ -349,7 +361,6 @@ template<typename RhsType, typename DstType>
 void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
 {
   const Index rank = (std::min)(rows(), cols());
-  eigen_assert(rhs.rows() == rows());
 
   typename RhsType::PlainObject c(rhs);
 
@@ -362,6 +373,25 @@ void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) c
   dst.topRows(rank) = c.topRows(rank);
   dst.bottomRows(cols()-rank).setZero();
 }
+
+template<typename _MatrixType>
+template<bool Conjugate, typename RhsType, typename DstType>
+void HouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index rank = (std::min)(rows(), cols());
+
+  typename RhsType::PlainObject c(rhs);
+
+  m_qr.topLeftCorner(rank, rank)
+      .template triangularView<Upper>()
+      .transpose().template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows()-rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
+}
 #endif
 
 /** Performs the QR factorization of the given matrix \a matrix. The result of

Eigen/src/SVD/BDCSVD.h

@@ -39,6 +39,7 @@ namespace internal {
 
 template<typename _MatrixType> 
 struct traits<BDCSVD<_MatrixType> >
+        : traits<_MatrixType>
 {
   typedef _MatrixType MatrixType;
 };  
@@ -1006,7 +1007,7 @@ void BDCSVD<MatrixType>::perturbCol0
 #ifdef EIGEN_BDCSVD_SANITY_CHECKS
       assert((std::isfinite)(tmp));
 #endif
-      zhat(k) = col0(k) > Literal(0) ? tmp : -tmp;
+      zhat(k) = col0(k) > Literal(0) ? RealScalar(tmp) : RealScalar(-tmp);
     }
   }
 }

Eigen/src/SVD/JacobiSVD.h

@@ -425,6 +425,7 @@ struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
 
 template<typename _MatrixType, int QRPreconditioner> 
 struct traits<JacobiSVD<_MatrixType,QRPreconditioner> >
+        : traits<_MatrixType>
 {
   typedef _MatrixType MatrixType;
 };

Eigen/src/SVD/SVDBase.h

@@ -17,6 +17,18 @@
 #define EIGEN_SVDBASE_H
 
 namespace Eigen {
+
+namespace internal {
+template<typename Derived> struct traits<SVDBase<Derived> >
+ : traits<Derived>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+}
+
 /** \ingroup SVD_Module
  *
  *
@@ -44,15 +56,18 @@ namespace Eigen {
  * terminate in finite (and reasonable) time.
  * \sa class BDCSVD, class JacobiSVD
  */
-template<typename Derived>
+template<typename Derived> class SVDBase
-class SVDBase
+ : public SolverBase<SVDBase<Derived> >
 {
-
 public: 
+   
+  template<typename Derived_>
+  friend struct internal::solve_assertion;
+
   typedef typename internal::traits<Derived>::MatrixType MatrixType;
   typedef typename MatrixType::Scalar Scalar;
   typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename Eigen::internal::traits<SVDBase>::StorageIndex StorageIndex;
   typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
   enum {
     RowsAtCompileTime = MatrixType::RowsAtCompileTime,
@@ -194,6 +209,7 @@ public:
   inline Index rows() const { return m_rows; }
   inline Index cols() const { return m_cols; }
   
+  #ifdef EIGEN_PARSED_BY_DOXYGEN
   /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
     *
     * \param b the right-hand-side of the equation to solve.
@@ -205,16 +221,15 @@ public:
     */
   template<typename Rhs>
   inline const Solve<Derived, Rhs>
-  solve(const MatrixBase<Rhs>& b) const
+  solve(const MatrixBase<Rhs>& b) const;
-  {
+  #endif
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-    eigen_assert(computeU() && computeV() && "SVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
-    return Solve<Derived, Rhs>(derived(), b.derived());
-  }
 
   #ifndef EIGEN_PARSED_BY_DOXYGEN
   template<typename RhsType, typename DstType>
   void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+  template<bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
   #endif
 
 protected:
@@ -224,6 +239,13 @@ protected:
     EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
   }
 
+  template<bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+      eigen_assert(m_isInitialized && "SVD is not initialized.");
+      eigen_assert(computeU() && computeV() && "SVDBase::solve(): Both unitaries U and V are required to be computed (thin unitaries suffice).");
+      eigen_assert((Transpose_?cols():rows())==b.rows() && "SVDBase::solve(): invalid number of rows of the right hand side matrix b");
+  }
+  
   // return true if already allocated
   bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
 
@@ -263,17 +285,30 @@ template<typename Derived>
 template<typename RhsType, typename DstType>
 void SVDBase<Derived>::_solve_impl(const RhsType &rhs, DstType &dst) const
 {
-  eigen_assert(rhs.rows() == rows());
-
   // A = U S V^*
   // So A^{-1} = V S^{-1} U^*
 
-  Matrix<Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
   Index l_rank = rank();
   tmp.noalias() =  m_matrixU.leftCols(l_rank).adjoint() * rhs;
   tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
   dst = m_matrixV.leftCols(l_rank) * tmp;
 }
+
+template<typename Derived>
+template<bool Conjugate, typename RhsType, typename DstType>
+void SVDBase<Derived>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  // A = U S V^*
+  // So  A^{-*} = U S^{-1} V^*
+  // And A^{-T} = U_conj S^{-1} V^T
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
+  Index l_rank = rank();
+
+  tmp.noalias() =  m_matrixV.leftCols(l_rank).transpose().template conjugateIf<Conjugate>() * rhs;
+  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
+  dst = m_matrixU.template conjugateIf<!Conjugate>().leftCols(l_rank) * tmp;
+}
 #endif
 
 template<typename MatrixType>

test/bdcsvd.cpp

@@ -46,6 +46,8 @@ void bdcsvd_method()
   VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU());
   VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV());
   VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m);
+  VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).transpose().solve(m), m);
+  VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).adjoint().solve(m), m);
 }
 
 // compare the Singular values returned with Jacobi and Bdc

test/cholesky.cpp

@@ -7,15 +7,12 @@
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
-#ifndef EIGEN_NO_ASSERTION_CHECKING
-#define EIGEN_NO_ASSERTION_CHECKING
-#endif
-
 #define TEST_ENABLE_TEMPORARY_TRACKING
 
 #include "main.h"
 #include <Eigen/Cholesky>
 #include <Eigen/QR>
+#include "solverbase.h"
 
 template<typename MatrixType, int UpLo>
 typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
@@ -81,15 +78,17 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
   }
 
   {
+    STATIC_CHECK(( internal::is_same<typename LLT<MatrixType,Lower>::StorageIndex,int>::value ));
+    STATIC_CHECK(( internal::is_same<typename LLT<MatrixType,Upper>::StorageIndex,int>::value ));
+
     SquareMatrixType symmUp = symm.template triangularView<Upper>();
     SquareMatrixType symmLo = symm.template triangularView<Lower>();
 
     LLT<SquareMatrixType,Lower> chollo(symmLo);
     VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
-    vecX = chollo.solve(vecB);
+
-    VERIFY_IS_APPROX(symm * vecX, vecB);
+    check_solverbase<VectorType, VectorType>(symm, chollo, rows, rows, 1);
-    matX = chollo.solve(matB);
+    check_solverbase<MatrixType, MatrixType>(symm, chollo, rows, cols, rows);
-    VERIFY_IS_APPROX(symm * matX, matB);
 
     const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows,cols));
     RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
@@ -143,6 +142,9 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
 
   // LDLT
   {
+    STATIC_CHECK(( internal::is_same<typename LDLT<MatrixType,Lower>::StorageIndex,int>::value ));
+    STATIC_CHECK(( internal::is_same<typename LDLT<MatrixType,Upper>::StorageIndex,int>::value ));
+
     int sign = internal::random<int>()%2 ? 1 : -1;
 
     if(sign == -1)
@@ -156,10 +158,9 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
     LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
     VERIFY(ldltlo.info()==Success);
     VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
-    vecX = ldltlo.solve(vecB);
+
-    VERIFY_IS_APPROX(symm * vecX, vecB);
+    check_solverbase<VectorType, VectorType>(symm, ldltlo, rows, rows, 1);
-    matX = ldltlo.solve(matB);
+    check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
-    VERIFY_IS_APPROX(symm * matX, matB);
 
     const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows,cols));
     RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
@@ -313,10 +314,9 @@ template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
 
     LLT<RealMatrixType,Lower> chollo(symmLo);
     VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
-    vecX = chollo.solve(vecB);
+
-    VERIFY_IS_APPROX(symm * vecX, vecB);
+    check_solverbase<VectorType, VectorType>(symm, chollo, rows, rows, 1);
-//     matX = chollo.solve(matB);
+    //check_solverbase<MatrixType, MatrixType>(symm, chollo, rows, cols, rows);
-//     VERIFY_IS_APPROX(symm * matX, matB);
   }
 
   // LDLT
@@ -333,10 +333,9 @@ template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
     LDLT<RealMatrixType,Lower> ldltlo(symmLo);
     VERIFY(ldltlo.info()==Success);
     VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
-    vecX = ldltlo.solve(vecB);
+
-    VERIFY_IS_APPROX(symm * vecX, vecB);
+    check_solverbase<VectorType, VectorType>(symm, ldltlo, rows, rows, 1);
-//     matX = ldltlo.solve(matB);
+    //check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
-//     VERIFY_IS_APPROX(symm * matX, matB);
   }
 }
 
@@ -477,16 +476,20 @@ template<typename MatrixType> void cholesky_verify_assert()
   VERIFY_RAISES_ASSERT(llt.matrixL())
   VERIFY_RAISES_ASSERT(llt.matrixU())
   VERIFY_RAISES_ASSERT(llt.solve(tmp))
-  VERIFY_RAISES_ASSERT(llt.solveInPlace(&tmp))
+  VERIFY_RAISES_ASSERT(llt.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(llt.adjoint().solve(tmp))
+  VERIFY_RAISES_ASSERT(llt.solveInPlace(tmp))
 
   LDLT<MatrixType> ldlt;
   VERIFY_RAISES_ASSERT(ldlt.matrixL())
-  VERIFY_RAISES_ASSERT(ldlt.permutationP())
+  VERIFY_RAISES_ASSERT(ldlt.transpositionsP())
   VERIFY_RAISES_ASSERT(ldlt.vectorD())
   VERIFY_RAISES_ASSERT(ldlt.isPositive())
   VERIFY_RAISES_ASSERT(ldlt.isNegative())
   VERIFY_RAISES_ASSERT(ldlt.solve(tmp))
-  VERIFY_RAISES_ASSERT(ldlt.solveInPlace(&tmp))
+  VERIFY_RAISES_ASSERT(ldlt.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(ldlt.adjoint().solve(tmp))
+  VERIFY_RAISES_ASSERT(ldlt.solveInPlace(tmp))
 }
 
 EIGEN_DECLARE_TEST(cholesky)

test/jacobisvd.cpp

@@ -67,6 +67,8 @@ void jacobisvd_method()
   VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
   VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
   VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
+  VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).transpose().solve(m), m);
+  VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).adjoint().solve(m), m);
 }
 
 namespace Foo {

test/lu.cpp

@@ -9,6 +9,7 @@
 
 #include "main.h"
 #include <Eigen/LU>
+#include "solverbase.h"
 using namespace std;
 
 template<typename MatrixType>
@@ -96,32 +97,14 @@ template<typename MatrixType> void lu_non_invertible()
   VERIFY(m1image.fullPivLu().rank() == rank);
   VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image);
 
+  check_solverbase<CMatrixType, MatrixType>(m1, lu, rows, cols, cols2);
+
   m2 = CMatrixType::Random(cols,cols2);
   m3 = m1*m2;
   m2 = CMatrixType::Random(cols,cols2);
   // test that the code, which does resize(), may be applied to an xpr
   m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3);
   VERIFY_IS_APPROX(m3, m1*m2);
-
-  // test solve with transposed
-  m3 = MatrixType::Random(rows,cols2);
-  m2 = m1.transpose()*m3;
-  m3 = MatrixType::Random(rows,cols2);
-  lu.template _solve_impl_transposed<false>(m2, m3);
-  VERIFY_IS_APPROX(m2, m1.transpose()*m3);
-  m3 = MatrixType::Random(rows,cols2);
-  m3 = lu.transpose().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.transpose()*m3);
-
-  // test solve with conjugate transposed
-  m3 = MatrixType::Random(rows,cols2);
-  m2 = m1.adjoint()*m3;
-  m3 = MatrixType::Random(rows,cols2);
-  lu.template _solve_impl_transposed<true>(m2, m3);
-  VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
-  m3 = MatrixType::Random(rows,cols2);
-  m3 = lu.adjoint().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
 }
 
 template<typename MatrixType> void lu_invertible()
@@ -150,10 +133,12 @@ template<typename MatrixType> void lu_invertible()
   VERIFY(lu.isSurjective());
   VERIFY(lu.isInvertible());
   VERIFY(lu.image(m1).fullPivLu().isInvertible());
+
+  check_solverbase<MatrixType, MatrixType>(m1, lu, size, size, size);
+
+  MatrixType m1_inverse = lu.inverse();
   m3 = MatrixType::Random(size,size);
   m2 = lu.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
-  MatrixType m1_inverse = lu.inverse();
   VERIFY_IS_APPROX(m2, m1_inverse*m3);
 
   RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
@@ -162,20 +147,6 @@ template<typename MatrixType> void lu_invertible()
   // truth.
   VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
 
-  // test solve with transposed
-  lu.template _solve_impl_transposed<false>(m3, m2);
-  VERIFY_IS_APPROX(m3, m1.transpose()*m2);
-  m3 = MatrixType::Random(size,size);
-  m3 = lu.transpose().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.transpose()*m3);
-
-  // test solve with conjugate transposed
-  lu.template _solve_impl_transposed<true>(m3, m2);
-  VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
-  m3 = MatrixType::Random(size,size);
-  m3 = lu.adjoint().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
-
   // Regression test for Bug 302
   MatrixType m4 = MatrixType::Random(size,size);
   VERIFY_IS_APPROX(lu.solve(m3*m4), lu.solve(m3)*m4);
@@ -197,30 +168,17 @@ template<typename MatrixType> void lu_partial_piv()
 
   VERIFY_IS_APPROX(m1, plu.reconstructedMatrix());
 
+  check_solverbase<MatrixType, MatrixType>(m1, plu, size, size, size);
+
+  MatrixType m1_inverse = plu.inverse();
   m3 = MatrixType::Random(size,size);
   m2 = plu.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
-  MatrixType m1_inverse = plu.inverse();
   VERIFY_IS_APPROX(m2, m1_inverse*m3);
 
   RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
   const RealScalar rcond_est = plu.rcond();
   // Verify that the estimate is within a factor of 10 of the truth.
   VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
-
-  // test solve with transposed
-  plu.template _solve_impl_transposed<false>(m3, m2);
-  VERIFY_IS_APPROX(m3, m1.transpose()*m2);
-  m3 = MatrixType::Random(size,size);
-  m3 = plu.transpose().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.transpose()*m3);
-
-  // test solve with conjugate transposed
-  plu.template _solve_impl_transposed<true>(m3, m2);
-  VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
-  m3 = MatrixType::Random(size,size);
-  m3 = plu.adjoint().solve(m2);
-  VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
 }
 
 template<typename MatrixType> void lu_verify_assert()
@@ -234,6 +192,8 @@ template<typename MatrixType> void lu_verify_assert()
   VERIFY_RAISES_ASSERT(lu.kernel())
   VERIFY_RAISES_ASSERT(lu.image(tmp))
   VERIFY_RAISES_ASSERT(lu.solve(tmp))
+  VERIFY_RAISES_ASSERT(lu.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(lu.adjoint().solve(tmp))
   VERIFY_RAISES_ASSERT(lu.determinant())
   VERIFY_RAISES_ASSERT(lu.rank())
   VERIFY_RAISES_ASSERT(lu.dimensionOfKernel())
@@ -246,6 +206,8 @@ template<typename MatrixType> void lu_verify_assert()
   VERIFY_RAISES_ASSERT(plu.matrixLU())
   VERIFY_RAISES_ASSERT(plu.permutationP())
   VERIFY_RAISES_ASSERT(plu.solve(tmp))
+  VERIFY_RAISES_ASSERT(plu.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(plu.adjoint().solve(tmp))
   VERIFY_RAISES_ASSERT(plu.determinant())
   VERIFY_RAISES_ASSERT(plu.inverse())
 }

test/qr.cpp

@@ -9,6 +9,7 @@
 
 #include "main.h"
 #include <Eigen/QR>
+#include "solverbase.h"
 
 template<typename MatrixType> void qr(const MatrixType& m)
 {
@@ -41,11 +42,7 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
 
   VERIFY_IS_APPROX(m1, qr.householderQ() * r);
 
-  Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
+  check_solverbase<Matrix<Scalar,Cols,Cols2>, Matrix<Scalar,Rows,Cols2> >(m1, qr, Rows, Cols, Cols2);
-  Matrix<Scalar,Rows,Cols2> m3 = m1*m2;
-  m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
-  m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
 }
 
 template<typename MatrixType> void qr_invertible()
@@ -57,6 +54,8 @@ template<typename MatrixType> void qr_invertible()
   typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
   typedef typename MatrixType::Scalar Scalar;
 
+  STATIC_CHECK(( internal::is_same<typename HouseholderQR<MatrixType>::StorageIndex,int>::value ));
+
   int size = internal::random<int>(10,50);
 
   MatrixType m1(size, size), m2(size, size), m3(size, size);
@@ -70,9 +69,8 @@ template<typename MatrixType> void qr_invertible()
   }
 
   HouseholderQR<MatrixType> qr(m1);
-  m3 = MatrixType::Random(size,size);
+
-  m2 = qr.solve(m3);
+  check_solverbase<MatrixType, MatrixType>(m1, qr, size, size, size);
-  VERIFY_IS_APPROX(m3, m1*m2);
 
   // now construct a matrix with prescribed determinant
   m1.setZero();
@@ -95,6 +93,8 @@ template<typename MatrixType> void qr_verify_assert()
   HouseholderQR<MatrixType> qr;
   VERIFY_RAISES_ASSERT(qr.matrixQR())
   VERIFY_RAISES_ASSERT(qr.solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.adjoint().solve(tmp))
   VERIFY_RAISES_ASSERT(qr.householderQ())
   VERIFY_RAISES_ASSERT(qr.absDeterminant())
   VERIFY_RAISES_ASSERT(qr.logAbsDeterminant())

test/qr_colpivoting.cpp

@@ -11,9 +11,12 @@
 #include "main.h"
 #include <Eigen/QR>
 #include <Eigen/SVD>
+#include "solverbase.h"
 
 template <typename MatrixType>
 void cod() {
+  STATIC_CHECK(( internal::is_same<typename CompleteOrthogonalDecomposition<MatrixType>::StorageIndex,int>::value ));
+
   Index rows = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
   Index cols = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
   Index cols2 = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
@@ -46,12 +49,12 @@ void cod() {
   MatrixType c = q * t * z * cod.colsPermutation().inverse();
   VERIFY_IS_APPROX(matrix, c);
 
+  check_solverbase<MatrixType, MatrixType>(matrix, cod, rows, cols, cols2);
+
+  // Verify that we get the same minimum-norm solution as the SVD.
   MatrixType exact_solution = MatrixType::Random(cols, cols2);
   MatrixType rhs = matrix * exact_solution;
   MatrixType cod_solution = cod.solve(rhs);
-  VERIFY_IS_APPROX(rhs, matrix * cod_solution);
-
-  // Verify that we get the same minimum-norm solution as the SVD.
   JacobiSVD<MatrixType> svd(matrix, ComputeThinU | ComputeThinV);
   MatrixType svd_solution = svd.solve(rhs);
   VERIFY_IS_APPROX(cod_solution, svd_solution);
@@ -77,13 +80,13 @@ void cod_fixedsize() {
   VERIFY(cod.isSurjective() == (rank == Cols));
   VERIFY(cod.isInvertible() == (cod.isInjective() && cod.isSurjective()));
 
+  check_solverbase<Matrix<Scalar, Cols, Cols2>, Matrix<Scalar, Rows, Cols2> >(matrix, cod, Rows, Cols, Cols2);
+
+  // Verify that we get the same minimum-norm solution as the SVD.
   Matrix<Scalar, Cols, Cols2> exact_solution;
   exact_solution.setRandom(Cols, Cols2);
   Matrix<Scalar, Rows, Cols2> rhs = matrix * exact_solution;
   Matrix<Scalar, Cols, Cols2> cod_solution = cod.solve(rhs);
-  VERIFY_IS_APPROX(rhs, matrix * cod_solution);
-
-  // Verify that we get the same minimum-norm solution as the SVD.
   JacobiSVD<MatrixType> svd(matrix, ComputeFullU | ComputeFullV);
   Matrix<Scalar, Cols, Cols2> svd_solution = svd.solve(rhs);
   VERIFY_IS_APPROX(cod_solution, svd_solution);
@@ -93,6 +96,8 @@ template<typename MatrixType> void qr()
 {
   using std::sqrt;
 
+  STATIC_CHECK(( internal::is_same<typename ColPivHouseholderQR<MatrixType>::StorageIndex,int>::value ));
+
   Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols2 = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
   Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
 
@@ -133,13 +138,10 @@ template<typename MatrixType> void qr()
     VERIFY_IS_APPROX_OR_LESS_THAN(y, x);
   }
 
-  MatrixType m2 = MatrixType::Random(cols,cols2);
+  check_solverbase<MatrixType, MatrixType>(m1, qr, rows, cols, cols2);
-  MatrixType m3 = m1*m2;
-  m2 = MatrixType::Random(cols,cols2);
-  m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
 
   {
+    MatrixType m2, m3;
     Index size = rows;
     do {
       m1 = MatrixType::Random(size,size);
@@ -173,11 +175,8 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
   Matrix<Scalar,Rows,Cols> c = qr.householderQ() * r * qr.colsPermutation().inverse();
   VERIFY_IS_APPROX(m1, c);
 
-  Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
+  check_solverbase<Matrix<Scalar,Cols,Cols2>, Matrix<Scalar,Rows,Cols2> >(m1, qr, Rows, Cols, Cols2);
-  Matrix<Scalar,Rows,Cols2> m3 = m1*m2;
+
-  m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
-  m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
   // Verify that the absolute value of the diagonal elements in R are
   // non-increasing until they reache the singularity threshold.
   RealScalar threshold =
@@ -264,9 +263,8 @@ template<typename MatrixType> void qr_invertible()
   }
 
   ColPivHouseholderQR<MatrixType> qr(m1);
-  m3 = MatrixType::Random(size,size);
+
-  m2 = qr.solve(m3);
+  check_solverbase<MatrixType, MatrixType>(m1, qr, size, size, size);
-  //VERIFY_IS_APPROX(m3, m1*m2);
 
   // now construct a matrix with prescribed determinant
   m1.setZero();
@@ -286,6 +284,8 @@ template<typename MatrixType> void qr_verify_assert()
   ColPivHouseholderQR<MatrixType> qr;
   VERIFY_RAISES_ASSERT(qr.matrixQR())
   VERIFY_RAISES_ASSERT(qr.solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.adjoint().solve(tmp))
   VERIFY_RAISES_ASSERT(qr.householderQ())
   VERIFY_RAISES_ASSERT(qr.dimensionOfKernel())
   VERIFY_RAISES_ASSERT(qr.isInjective())
@@ -296,6 +296,25 @@ template<typename MatrixType> void qr_verify_assert()
   VERIFY_RAISES_ASSERT(qr.logAbsDeterminant())
 }
 
+template<typename MatrixType> void cod_verify_assert()
+{
+  MatrixType tmp;
+
+  CompleteOrthogonalDecomposition<MatrixType> cod;
+  VERIFY_RAISES_ASSERT(cod.matrixQTZ())
+  VERIFY_RAISES_ASSERT(cod.solve(tmp))
+  VERIFY_RAISES_ASSERT(cod.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(cod.adjoint().solve(tmp))
+  VERIFY_RAISES_ASSERT(cod.householderQ())
+  VERIFY_RAISES_ASSERT(cod.dimensionOfKernel())
+  VERIFY_RAISES_ASSERT(cod.isInjective())
+  VERIFY_RAISES_ASSERT(cod.isSurjective())
+  VERIFY_RAISES_ASSERT(cod.isInvertible())
+  VERIFY_RAISES_ASSERT(cod.pseudoInverse())
+  VERIFY_RAISES_ASSERT(cod.absDeterminant())
+  VERIFY_RAISES_ASSERT(cod.logAbsDeterminant())
+}
+
 EIGEN_DECLARE_TEST(qr_colpivoting)
 {
   for(int i = 0; i < g_repeat; i++) {
@@ -330,6 +349,13 @@ EIGEN_DECLARE_TEST(qr_colpivoting)
   CALL_SUBTEST_6(qr_verify_assert<MatrixXcf>());
   CALL_SUBTEST_3(qr_verify_assert<MatrixXcd>());
 
+  CALL_SUBTEST_7(cod_verify_assert<Matrix3f>());
+  CALL_SUBTEST_8(cod_verify_assert<Matrix3d>());
+  CALL_SUBTEST_1(cod_verify_assert<MatrixXf>());
+  CALL_SUBTEST_2(cod_verify_assert<MatrixXd>());
+  CALL_SUBTEST_6(cod_verify_assert<MatrixXcf>());
+  CALL_SUBTEST_3(cod_verify_assert<MatrixXcd>());
+
   // Test problem size constructors
   CALL_SUBTEST_9(ColPivHouseholderQR<MatrixXf>(10, 20));
 

test/qr_fullpivoting.cpp

@@ -10,9 +10,12 @@
 
 #include "main.h"
 #include <Eigen/QR>
+#include "solverbase.h"
 
 template<typename MatrixType> void qr()
 {
+  STATIC_CHECK(( internal::is_same<typename FullPivHouseholderQR<MatrixType>::StorageIndex,int>::value ));
+
   static const int Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime;
   Index max_size = EIGEN_TEST_MAX_SIZE;
   Index min_size = numext::maxi(1,EIGEN_TEST_MAX_SIZE/10);
@@ -48,13 +51,10 @@ template<typename MatrixType> void qr()
   MatrixType tmp;
   VERIFY_IS_APPROX(tmp.noalias() = qr.matrixQ() * r, (qr.matrixQ() * r).eval());
   
-  MatrixType m2 = MatrixType::Random(cols,cols2);
+  check_solverbase<MatrixType, MatrixType>(m1, qr, rows, cols, cols2);
-  MatrixType m3 = m1*m2;
-  m2 = MatrixType::Random(cols,cols2);
-  m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
 
   {
+    MatrixType m2, m3;
     Index size = rows;
     do {
       m1 = MatrixType::Random(size,size);
@@ -93,9 +93,7 @@ template<typename MatrixType> void qr_invertible()
   VERIFY(qr.isInvertible());
   VERIFY(qr.isSurjective());
 
-  m3 = MatrixType::Random(size,size);
+  check_solverbase<MatrixType, MatrixType>(m1, qr, size, size, size);
-  m2 = qr.solve(m3);
-  VERIFY_IS_APPROX(m3, m1*m2);
 
   // now construct a matrix with prescribed determinant
   m1.setZero();
@@ -115,6 +113,8 @@ template<typename MatrixType> void qr_verify_assert()
   FullPivHouseholderQR<MatrixType> qr;
   VERIFY_RAISES_ASSERT(qr.matrixQR())
   VERIFY_RAISES_ASSERT(qr.solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.transpose().solve(tmp))
+  VERIFY_RAISES_ASSERT(qr.adjoint().solve(tmp))
   VERIFY_RAISES_ASSERT(qr.matrixQ())
   VERIFY_RAISES_ASSERT(qr.dimensionOfKernel())
   VERIFY_RAISES_ASSERT(qr.isInjective())

test/solverbase.h

@@ -0,0 +1,36 @@
+#ifndef TEST_SOLVERBASE_H
+#define TEST_SOLVERBASE_H
+
+template<typename DstType, typename RhsType, typename MatrixType, typename SolverType>
+void check_solverbase(const MatrixType& matrix, const SolverType& solver, Index rows, Index cols, Index cols2)
+{
+  // solve
+  DstType m2               = DstType::Random(cols,cols2);
+  RhsType m3               = matrix*m2;
+  DstType solver_solution  = DstType::Random(cols,cols2);
+  solver._solve_impl(m3, solver_solution);
+  VERIFY_IS_APPROX(m3, matrix*solver_solution);
+  solver_solution          = DstType::Random(cols,cols2);
+  solver_solution          = solver.solve(m3);
+  VERIFY_IS_APPROX(m3, matrix*solver_solution);
+  // test solve with transposed
+  m3                       = RhsType::Random(rows,cols2);
+  m2                       = matrix.transpose()*m3;
+  RhsType solver_solution2 = RhsType::Random(rows,cols2);
+  solver.template _solve_impl_transposed<false>(m2, solver_solution2);
+  VERIFY_IS_APPROX(m2, matrix.transpose()*solver_solution2);
+  solver_solution2         = RhsType::Random(rows,cols2);
+  solver_solution2         = solver.transpose().solve(m2);
+  VERIFY_IS_APPROX(m2, matrix.transpose()*solver_solution2);
+  // test solve with conjugate transposed
+  m3                       = RhsType::Random(rows,cols2);
+  m2                       = matrix.adjoint()*m3;
+  solver_solution2         = RhsType::Random(rows,cols2);
+  solver.template _solve_impl_transposed<true>(m2, solver_solution2);
+  VERIFY_IS_APPROX(m2, matrix.adjoint()*solver_solution2);
+  solver_solution2         = RhsType::Random(rows,cols2);
+  solver_solution2         = solver.adjoint().solve(m2);
+  VERIFY_IS_APPROX(m2, matrix.adjoint()*solver_solution2);
+}
+
+#endif // TEST_SOLVERBASE_H

test/svd_common.h

@@ -17,6 +17,7 @@
 #endif
 
 #include "svd_fill.h"
+#include "solverbase.h"
 
 // Check that the matrix m is properly reconstructed and that the U and V factors are unitary
 // The SVD must have already been computed.
@@ -219,12 +220,33 @@ void svd_min_norm(const MatrixType& m, unsigned int computationOptions)
   VERIFY_IS_APPROX(x21, x3);
 }
 
+template<typename MatrixType, typename SolverType>
+void svd_test_solvers(const MatrixType& m, const SolverType& solver) {
+    Index rows, cols, cols2;
+
+    rows = m.rows();
+    cols = m.cols();
+
+    if(MatrixType::ColsAtCompileTime==Dynamic)
+    {
+      cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE);
+    }
+    else
+    {
+      cols2 = cols;
+    }
+    typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> CMatrixType;
+    check_solverbase<CMatrixType, MatrixType>(m, solver, rows, cols, cols2);
+}
+
 // Check full, compare_to_full, least_square, and min_norm for all possible compute-options
 template<typename SvdType, typename MatrixType>
 void svd_test_all_computation_options(const MatrixType& m, bool full_only)
 {
 //   if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
 //     return;
+  STATIC_CHECK(( internal::is_same<typename SvdType::StorageIndex,int>::value ));
+
   SvdType fullSvd(m, ComputeFullU|ComputeFullV);
   CALL_SUBTEST(( svd_check_full(m, fullSvd) ));
   CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeFullV) ));
@@ -234,6 +256,9 @@ void svd_test_all_computation_options(const MatrixType& m, bool full_only)
   // remark #111: statement is unreachable
   #pragma warning disable 111
   #endif
+
+  svd_test_solvers(m, fullSvd);
+
   if(full_only)
     return;
 
@@ -448,6 +473,8 @@ void svd_verify_assert(const MatrixType& m)
   VERIFY_RAISES_ASSERT(svd.singularValues())
   VERIFY_RAISES_ASSERT(svd.matrixV())
   VERIFY_RAISES_ASSERT(svd.solve(rhs))
+  VERIFY_RAISES_ASSERT(svd.transpose().solve(rhs))
+  VERIFY_RAISES_ASSERT(svd.adjoint().solve(rhs))
   MatrixType a = MatrixType::Zero(rows, cols);
   a.setZero();
   svd.compute(a, 0);