bug #707: add inplace decomposition through Ref<> for Cholesky, LU and QR decompositions.

Eigen/src/Cholesky/LDLT.h

@@ -52,7 +52,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
       UpLo = _UpLo
@@ -61,7 +60,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
     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, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
+    typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
 
     typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
     typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
@@ -97,6 +96,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
     /** \brief Constructor with decomposition
       *
       * This calculates the decomposition for the input \a matrix.
+      *
       * \sa LDLT(Index size)
       */
     template<typename InputType>
@@ -110,6 +110,23 @@ template<typename _MatrixType, int _UpLo> class LDLT
       compute(matrix.derived());
     }
 
+    /** \brief Constructs a LDLT factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa LDLT(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit LDLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
     /** Clear any existing decomposition
      * \sa rankUpdate(w,sigma)
      */

Eigen/src/Cholesky/LLT.h

@@ -54,7 +54,6 @@ template<typename _MatrixType, int _UpLo> class LLT
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
     typedef typename MatrixType::Scalar Scalar;
@@ -95,6 +94,21 @@ template<typename _MatrixType, int _UpLo> class LLT
       compute(matrix.derived());
     }
 
+    /** \brief Constructs a LDLT factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when
+      * \c MatrixType is a Eigen::Ref.
+      *
+      * \sa LLT(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit LLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
     /** \returns a view of the upper triangular matrix U */
     inline typename Traits::MatrixU matrixU() const
     {

Eigen/src/LU/FullPivLU.h

@@ -97,6 +97,15 @@ template<typename _MatrixType> class FullPivLU
     template<typename InputType>
     explicit FullPivLU(const EigenBase<InputType>& matrix);
 
+    /** \brief Constructs a LU factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa FullPivLU(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit FullPivLU(EigenBase<InputType>& matrix);
+
     /** Computes the LU decomposition of the given matrix.
       *
       * \param matrix the matrix of which to compute the LU decomposition.
@@ -105,7 +114,11 @@ template<typename _MatrixType> class FullPivLU
       * \returns a reference to *this
       */
     template<typename InputType>
-    FullPivLU& compute(const EigenBase<InputType>& matrix);
+    FullPivLU& compute(const EigenBase<InputType>& matrix) {
+      m_lu = matrix.derived();
+      computeInPlace();
+      return *this;
+    }
 
     /** \returns the LU decomposition matrix: the upper-triangular part is U, the
       * unit-lower-triangular part is L (at least for square matrices; in the non-square
@@ -459,25 +472,28 @@ FullPivLU<MatrixType>::FullPivLU(const EigenBase<InputType>& matrix)
 
 template<typename MatrixType>
 template<typename InputType>
-FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const EigenBase<InputType>& matrix)
+FullPivLU<MatrixType>::FullPivLU(EigenBase<InputType>& matrix)
+  : m_lu(matrix.derived()),
+    m_p(matrix.rows()),
+    m_q(matrix.cols()),
+    m_rowsTranspositions(matrix.rows()),
+    m_colsTranspositions(matrix.cols()),
+    m_isInitialized(false),
+    m_usePrescribedThreshold(false)
 {
-  check_template_parameters();
-
-  // the permutations are stored as int indices, so just to be sure:
-  eigen_assert(matrix.rows()<=NumTraits<int>::highest() && matrix.cols()<=NumTraits<int>::highest());
-
-  m_lu = matrix.derived();
-  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
-
   computeInPlace();
-
-  m_isInitialized = true;
-  return *this;
 }
 
 template<typename MatrixType>
 void FullPivLU<MatrixType>::computeInPlace()
 {
+  check_template_parameters();
+
+  // the permutations are stored as int indices, so just to be sure:
+  eigen_assert(m_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest());
+
+  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
+
   const Index size = m_lu.diagonalSize();
   const Index rows = m_lu.rows();
   const Index cols = m_lu.cols();
@@ -557,6 +573,8 @@ void FullPivLU<MatrixType>::computeInPlace()
     m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
 
   m_det_pq = (number_of_transpositions%2) ? -1 : 1;
+
+  m_isInitialized = true;
 }
 
 template<typename MatrixType>

Eigen/src/LU/PartialPivLU.h

@@ -26,6 +26,17 @@ template<typename _MatrixType> struct traits<PartialPivLU<_MatrixType> >
   };
 };
 
+template<typename T,typename Derived>
+struct enable_if_ref;
+// {
+//   typedef Derived type;
+// };
+
+template<typename T,typename Derived>
+struct enable_if_ref<Ref<T>,Derived> {
+  typedef Derived type;
+};
+
 } // end namespace internal
 
 /** \ingroup LU_Module
@@ -102,8 +113,29 @@ template<typename _MatrixType> class PartialPivLU
     template<typename InputType>
     explicit PartialPivLU(const EigenBase<InputType>& matrix);
 
+    /** Constructor for inplace decomposition
+      *
+      * \param matrix the matrix of which to compute the LU decomposition.
+      *
+      * If \c MatrixType is an Eigen::Ref, then the storage of \a matrix will be shared
+      * between \a matrix and \c *this and the decomposition will take place in-place.
+      * The memory of \a matrix will be used througrough the lifetime of \c *this. In
+      * particular, further calls to \c this->compute(A) will still operate on the memory
+      * of \a matrix meaning. This also implies that the sizes of \c A must match the
+      * ones of \a matrix.
+      *
+      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
+      * If you need to deal with non-full rank, use class FullPivLU instead.
+      */
     template<typename InputType>
-    PartialPivLU& compute(const EigenBase<InputType>& matrix);
+    explicit PartialPivLU(EigenBase<InputType>& matrix);
+
+    template<typename InputType>
+    PartialPivLU& compute(const EigenBase<InputType>& matrix) {
+      m_lu = matrix.derived();
+      compute();
+      return *this;
+    }
 
     /** \returns the LU decomposition matrix: the upper-triangular part is U, the
       * unit-lower-triangular part is L (at least for square matrices; in the non-square
@@ -251,6 +283,8 @@ template<typename _MatrixType> class PartialPivLU
       EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
     }
 
+    void compute();
+
     MatrixType m_lu;
     PermutationType m_p;
     TranspositionType m_rowsTranspositions;
@@ -284,7 +318,7 @@ PartialPivLU<MatrixType>::PartialPivLU(Index size)
 template<typename MatrixType>
 template<typename InputType>
 PartialPivLU<MatrixType>::PartialPivLU(const EigenBase<InputType>& matrix)
-  : m_lu(matrix.rows(), matrix.rows()),
+  : m_lu(matrix.rows(),matrix.cols()),
     m_p(matrix.rows()),
     m_rowsTranspositions(matrix.rows()),
     m_l1_norm(0),
@@ -294,6 +328,19 @@ PartialPivLU<MatrixType>::PartialPivLU(const EigenBase<InputType>& matrix)
   compute(matrix.derived());
 }
 
+template<typename MatrixType>
+template<typename InputType>
+PartialPivLU<MatrixType>::PartialPivLU(EigenBase<InputType>& matrix)
+  : m_lu(matrix.derived()),
+    m_p(matrix.rows()),
+    m_rowsTranspositions(matrix.rows()),
+    m_l1_norm(0),
+    m_det_p(0),
+    m_isInitialized(false)
+{
+  compute();
+}
+
 namespace internal {
 
 /** \internal This is the blocked version of fullpivlu_unblocked() */
@@ -470,19 +517,17 @@ void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, t
 } // end namespace internal
 
 template<typename MatrixType>
-template<typename InputType>
+void PartialPivLU<MatrixType>::compute()
-PartialPivLU<MatrixType>& PartialPivLU<MatrixType>::compute(const EigenBase<InputType>& matrix)
 {
   check_template_parameters();
 
   // the row permutation is stored as int indices, so just to be sure:
-  eigen_assert(matrix.rows()<NumTraits<int>::highest());
+  eigen_assert(m_lu.rows()<NumTraits<int>::highest());
 
-  m_lu = matrix.derived();
   m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
 
-  eigen_assert(matrix.rows() == matrix.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
+  eigen_assert(m_lu.rows() == m_lu.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
-  const Index size = matrix.rows();
+  const Index size = m_lu.rows();
 
   m_rowsTranspositions.resize(size);
 
@@ -493,7 +538,6 @@ PartialPivLU<MatrixType>& PartialPivLU<MatrixType>::compute(const EigenBase<Inpu
   m_p = m_rowsTranspositions;
 
   m_isInitialized = true;
-  return *this;
 }
 
 template<typename MatrixType>

Eigen/src/QR/ColPivHouseholderQR.h

@@ -51,7 +51,6 @@ template<typename _MatrixType> class ColPivHouseholderQR
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
@@ -59,7 +58,6 @@ template<typename _MatrixType> class ColPivHouseholderQR
     typedef typename MatrixType::RealScalar RealScalar;
     // FIXME should be int
     typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
     typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
     typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
     typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
@@ -135,6 +133,27 @@ template<typename _MatrixType> class ColPivHouseholderQR
       compute(matrix.derived());
     }
 
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa ColPivHouseholderQR(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit ColPivHouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
+        m_colsPermutation(PermIndexType(matrix.cols())),
+        m_colsTranspositions(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_colNormsUpdated(matrix.cols()),
+        m_colNormsDirect(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false)
+    {
+      computeInPlace();
+    }
+
     /** 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.
       *
@@ -453,21 +472,19 @@ template<typename MatrixType>
 template<typename InputType>
 ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
 {
-  check_template_parameters();
+  m_qr = matrix.derived();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(matrix.cols()<=NumTraits<int>::highest());
-
-  m_qr = matrix;
-
   computeInPlace();
-
   return *this;
 }
 
 template<typename MatrixType>
 void ColPivHouseholderQR<MatrixType>::computeInPlace()
 {
+  check_template_parameters();
+
+  // the column permutation is stored as int indices, so just to be sure:
+  eigen_assert(m_qr.cols()<=NumTraits<int>::highest());
+
   using std::abs;
 
   Index rows = m_qr.rows();

Eigen/src/QR/CompleteOrthogonalDecomposition.h

@@ -48,16 +48,12 @@ class CompleteOrthogonalDecomposition {
   enum {
     RowsAtCompileTime = MatrixType::RowsAtCompileTime,
     ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    Options = MatrixType::Options,
     MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
     MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
   };
   typedef typename MatrixType::Scalar Scalar;
   typedef typename MatrixType::RealScalar RealScalar;
   typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options,
-                 MaxRowsAtCompileTime, MaxRowsAtCompileTime>
-      MatrixQType;
   typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
   typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime>
       PermutationType;
@@ -114,10 +110,27 @@ class CompleteOrthogonalDecomposition {
   explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix)
       : m_cpqr(matrix.rows(), matrix.cols()),
         m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_temp(matrix.cols()) {
+        m_temp(matrix.cols())
+  {
     compute(matrix.derived());
   }
 
+  /** \brief Constructs a complete orthogonal decomposition from a given matrix
+    *
+    * This overloaded constructor is provided for inplace solving when \c MatrixType is a Eigen::Ref.
+    *
+    * \sa CompleteOrthogonalDecomposition(const EigenBase&)
+    */
+  template<typename InputType>
+  explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix)
+    : m_cpqr(matrix.derived()),
+      m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
+      m_temp(matrix.cols())
+  {
+    computeInPlace();
+  }
+
+
   /** 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.
@@ -165,7 +178,12 @@ class CompleteOrthogonalDecomposition {
   const MatrixType& matrixT() const { return m_cpqr.matrixQR(); }
 
   template <typename InputType>
-  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix);
+  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) {
+    // Compute the column pivoted QR factorization A P = Q R.
+    m_cpqr.compute(matrix);
+    computeInPlace();
+    return *this;
+  }
 
   /** \returns a const reference to the column permutation matrix */
   const PermutationType& colsPermutation() const {
@@ -354,6 +372,8 @@ class CompleteOrthogonalDecomposition {
     EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
   }
 
+  void computeInPlace();
+
   /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
    */
   template <typename Rhs>
@@ -384,20 +404,16 @@ CompleteOrthogonalDecomposition<MatrixType>::logAbsDeterminant() const {
  * CompleteOrthogonalDecomposition(const MatrixType&)
  */
 template <typename MatrixType>
-template <typename InputType>
+void CompleteOrthogonalDecomposition<MatrixType>::computeInPlace()
-CompleteOrthogonalDecomposition<MatrixType>& CompleteOrthogonalDecomposition<
+{
-    MatrixType>::compute(const EigenBase<InputType>& matrix) {
   check_template_parameters();
 
   // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(matrix.cols() <= NumTraits<int>::highest());
+  eigen_assert(m_cpqr.cols() <= NumTraits<int>::highest());
-
-  // Compute the column pivoted QR factorization A P = Q R.
-  m_cpqr.compute(matrix);
 
   const Index rank = m_cpqr.rank();
-  const Index cols = matrix.cols();
+  const Index cols = m_cpqr.cols();
-  const Index rows = matrix.rows();
+  const Index rows = m_cpqr.rows();
   m_zCoeffs.resize((std::min)(rows, cols));
   m_temp.resize(cols);
 
@@ -443,7 +459,6 @@ CompleteOrthogonalDecomposition<MatrixType>& CompleteOrthogonalDecomposition<
       }
     }
   }
-  return *this;
 }
 
 template <typename MatrixType>

Eigen/src/QR/FullPivHouseholderQR.h

@@ -60,7 +60,6 @@ template<typename _MatrixType> class FullPivHouseholderQR
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
@@ -135,6 +134,26 @@ template<typename _MatrixType> class FullPivHouseholderQR
       compute(matrix.derived());
     }
 
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa FullPivHouseholderQR(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit FullPivHouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_permutation(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false)
+    {
+      computeInPlace();
+    }
+
     /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
       * \c *this is the QR decomposition.
       *
@@ -430,18 +449,16 @@ template<typename MatrixType>
 template<typename InputType>
 FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
 {
-  check_template_parameters();
-  
   m_qr = matrix.derived();
-  
   computeInPlace();
-  
   return *this;
 }
 
 template<typename MatrixType>
 void FullPivHouseholderQR<MatrixType>::computeInPlace()
 {
+  check_template_parameters();
+
   using std::abs;
   Index rows = m_qr.rows();
   Index cols = m_qr.cols();

Eigen/src/QR/HouseholderQR.h

@@ -47,7 +47,6 @@ template<typename _MatrixType> class HouseholderQR
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
@@ -102,6 +101,24 @@ template<typename _MatrixType> class HouseholderQR
       compute(matrix.derived());
     }
 
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for inplace solving when
+      * \c MatrixType is a Eigen::Ref.
+      *
+      * \sa HouseholderQR(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit HouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
+        m_temp(matrix.cols()),
+        m_isInitialized(false)
+    {
+      computeInPlace();
+    }
+
     /** 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.
       *
@@ -151,7 +168,11 @@ template<typename _MatrixType> class HouseholderQR
     }
 
     template<typename InputType>
-    HouseholderQR& compute(const EigenBase<InputType>& matrix);
+    HouseholderQR& compute(const EigenBase<InputType>& matrix) {
+      m_qr = matrix.derived();
+      computeInPlace();
+      return *this;
+    }
 
     /** \returns the absolute value of the determinant of the matrix of which
       * *this is the QR decomposition. It has only linear complexity
@@ -204,6 +225,8 @@ template<typename _MatrixType> class HouseholderQR
       EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
     }
 
+    void computeInPlace();
+    
     MatrixType m_qr;
     HCoeffsType m_hCoeffs;
     RowVectorType m_temp;
@@ -354,16 +377,14 @@ void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) c
   * \sa class HouseholderQR, HouseholderQR(const MatrixType&)
   */
 template<typename MatrixType>
-template<typename InputType>
+void HouseholderQR<MatrixType>::computeInPlace()
-HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
 {
   check_template_parameters();
   
-  Index rows = matrix.rows();
+  Index rows = m_qr.rows();
-  Index cols = matrix.cols();
+  Index cols = m_qr.cols();
   Index size = (std::min)(rows,cols);
 
-  m_qr = matrix.derived();
   m_hCoeffs.resize(size);
 
   m_temp.resize(cols);
@@ -371,7 +392,6 @@ HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const EigenBase<In
   internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data());
 
   m_isInitialized = true;
-  return *this;
 }
 
 #ifndef __CUDACC__

test/CMakeLists.txt

@@ -258,6 +258,7 @@ ei_add_test(rvalue_types)
 ei_add_test(dense_storage)
 ei_add_test(ctorleak)
 ei_add_test(mpl2only)
+ei_add_test(inplace_decomposition)
 
 add_executable(bug1213 bug1213.cpp bug1213_main.cpp)
 

test/inplace_decomposition.cpp

@@ -0,0 +1,110 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// 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/.
+
+#include "main.h"
+#include <Eigen/LU>
+#include <Eigen/Cholesky>
+#include <Eigen/QR>
+
+// This file test inplace decomposition through Ref<>, as supported by Cholesky, LU, and QR decompositions.
+
+template<typename DecType,typename MatrixType> void inplace(bool square = false, bool SPD = false)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RhsType;
+  typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ResType;
+
+  Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(2,EIGEN_TEST_MAX_SIZE/2) : MatrixType::RowsAtCompileTime;
+  Index cols = MatrixType::ColsAtCompileTime==Dynamic ? (square?rows:internal::random<Index>(2,rows)) : MatrixType::ColsAtCompileTime;
+
+  MatrixType A = MatrixType::Random(rows,cols);
+  RhsType b = RhsType::Random(rows);
+  ResType x(cols);
+
+  if(SPD)
+  {
+    assert(square);
+    A.topRows(cols) = A.topRows(cols).adjoint() * A.topRows(cols);
+    A.diagonal().array() += 1e-3;
+  }
+
+  MatrixType A0 = A;
+  MatrixType A1 = A;
+
+  DecType dec(A);
+
+  // Check that the content of A has been modified
+  VERIFY_IS_NOT_APPROX( A, A0 );
+
+  // Check that the decomposition is correct:
+  if(rows==cols)
+  {
+    VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b );
+  }
+  else
+  {
+    VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
+  }
+
+  // Check that modifying A breaks the current dec:
+  A.setRandom();
+  if(rows==cols)
+  {
+    VERIFY_IS_NOT_APPROX( A0 * (x = dec.solve(b)), b );
+  }
+  else
+  {
+    VERIFY_IS_NOT_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
+  }
+
+  // Check that calling compute(A1) does not modify A1:
+  A = A0;
+  dec.compute(A1);
+  VERIFY_IS_EQUAL(A0,A1);
+  VERIFY_IS_NOT_APPROX( A, A0 );
+  if(rows==cols)
+  {
+    VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b );
+  }
+  else
+  {
+    VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
+  }
+}
+
+
+void test_inplace_decomposition()
+{
+  EIGEN_UNUSED typedef Matrix<double,4,3> Matrix43d;
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1(( inplace<LLT<Ref<MatrixXd> >, MatrixXd>(true,true) ));
+    CALL_SUBTEST_1(( inplace<LLT<Ref<Matrix4d> >, Matrix4d>(true,true) ));
+
+    CALL_SUBTEST_2(( inplace<LDLT<Ref<MatrixXd> >, MatrixXd>(true,true) ));
+    CALL_SUBTEST_2(( inplace<LDLT<Ref<Matrix4d> >, Matrix4d>(true,true) ));
+
+    CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) ));
+    CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) ));
+
+    CALL_SUBTEST_4(( inplace<FullPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) ));
+    CALL_SUBTEST_4(( inplace<FullPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) ));
+
+    CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
+    CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
+
+    CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
+    CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
+
+    CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
+    CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
+
+    CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<MatrixXd> >, MatrixXd>(false,false) ));
+    CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<Matrix43d> >, Matrix43d>(false,false) ));
+  }
+}