Eigen/src/Cholesky/LDLT.h - mirror - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
 // Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
 // License as published by the Free Software Foundation; either
 // version 3 of the License, or (at your option) any later version.
 //
 // Alternatively, you can redistribute it and/or
 // modify it under the terms of the GNU General Public License as
 // published by the Free Software Foundation; either version 2 of
 // the License, or (at your option) any later version.
 //
 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
 // GNU General Public License for more details.
 //
 // You should have received a copy of the GNU Lesser General Public
 // License and a copy of the GNU General Public License along with
 // Eigen. If not, see <http://www.gnu.org/licenses/>.

 #ifndef EIGEN_LDLT_H
 #define EIGEN_LDLT_H

 /** \ingroup cholesky_Module
   *
   * \class LDLT
   *
   * \brief Robust Cholesky decomposition of a matrix
   *
   * \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
   *
   * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
   * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
   * is lower triangular with a unit diagonal and D is a diagonal matrix.
   *
   * The decomposition uses pivoting to ensure stability, so that L will have
   * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
   * on D also stabilizes the computation.
   *
   * Remember that Cholesky decompositions are not rank-revealing.  Also, do not use a Cholesky
 	* decomposition to determine whether a system of equations has a solution.
   *
   * \sa MatrixBase::ldlt(), class LLT
   */
  /* THIS PART OF THE DOX IS CURRENTLY DISABLED BECAUSE INACCURATE BECAUSE OF BUG IN THE DECOMPOSITION CODE
   * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
   * the strict lower part does not have to store correct values.
   */
 template<typename _MatrixType> class LDLT
 {
   public:
     typedef _MatrixType MatrixType;
     enum {
       RowsAtCompileTime = MatrixType::RowsAtCompileTime,
       ColsAtCompileTime = MatrixType::ColsAtCompileTime,
       Options = MatrixType::Options,
       MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
       MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
     };
     typedef typename MatrixType::Scalar Scalar;
     typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
     typedef typename MatrixType::Index Index;
     typedef typename ei_plain_col_type<MatrixType, Index>::type IntColVectorType;
     typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;

     /** \brief Default Constructor.
       *
       * The default constructor is useful in cases in which the user intends to
       * perform decompositions via LDLT::compute(const MatrixType&).
       */
     LDLT() : m_matrix(), m_p(), m_transpositions(), m_isInitialized(false) {}

     /** \brief Default Constructor with memory preallocation
       *
       * Like the default constructor but with preallocation of the internal data
       * according to the specified problem \a size.
       * \sa LDLT()
       */
     LDLT(Index size) : m_matrix(size, size),
                      m_p(size),
                      m_transpositions(size),
                      m_temporary(size),
                      m_isInitialized(false) {}

     LDLT(const MatrixType& matrix)
       : m_matrix(matrix.rows(), matrix.cols()),
         m_p(matrix.rows()),
         m_transpositions(matrix.rows()),
         m_temporary(matrix.rows()),
         m_isInitialized(false)
     {
       compute(matrix);
     }

     /** \returns the lower triangular matrix L */
     inline TriangularView<MatrixType, UnitLower> matrixL(void) const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_matrix;
     }

     /** \returns a vector of integers, whose size is the number of rows of the matrix being decomposed,
       * representing the P permutation i.e. the permutation of the rows. For its precise meaning,
       * see the examples given in the documentation of class FullPivLU.
       */
     inline const IntColVectorType& permutationP() const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_p;
     }

     /** \returns the coefficients of the diagonal matrix D */
     inline Diagonal<MatrixType,0> vectorD(void) const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_matrix.diagonal();
     }

     /** \returns true if the matrix is positive (semidefinite) */
     inline bool isPositive(void) const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_sign == 1;
     }

     /** \returns true if the matrix is negative (semidefinite) */
     inline bool isNegative(void) const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_sign == -1;
     }

     /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
       *
       * \note_about_checking_solutions
       *
       * \sa solveInPlace(), MatrixBase::ldlt()
       */
     template<typename Rhs>
     inline const ei_solve_retval<LDLT, Rhs>
     solve(const MatrixBase<Rhs>& b) const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       ei_assert(m_matrix.rows()==b.rows()
                 && "LDLT::solve(): invalid number of rows of the right hand side matrix b");
       return ei_solve_retval<LDLT, Rhs>(*this, b.derived());
     }

     template<typename Derived>
     bool solveInPlace(MatrixBase<Derived> &bAndX) const;

     LDLT& compute(const MatrixType& matrix);

     /** \returns the LDLT decomposition matrix
       *
       * TODO: document the storage layout
       */
     inline const MatrixType& matrixLDLT() const
     {
       ei_assert(m_isInitialized && "LDLT is not initialized.");
       return m_matrix;
     }

     MatrixType reconstructedMatrix() const;

     inline Index rows() const { return m_matrix.rows(); }
     inline Index cols() const { return m_matrix.cols(); }

   protected:
     /** \internal
       * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
       * The strict upper part is used during the decomposition, the strict lower
       * part correspond to the coefficients of L (its diagonal is equal to 1 and
       * is not stored), and the diagonal entries correspond to D.
       */
     MatrixType m_matrix;
     IntColVectorType m_p;
     IntColVectorType m_transpositions; // FIXME do we really need to store permanently the transpositions?
     TmpMatrixType m_temporary;
     Index m_sign;
     bool m_isInitialized;
 };

 /** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
   */
 template<typename MatrixType>
 LDLT<MatrixType>& LDLT<MatrixType>::compute(const MatrixType& a)
 {
   ei_assert(a.rows()==a.cols());
   const Index size = a.rows();

   m_matrix = a;

   m_p.resize(size);
   m_transpositions.resize(size);
   m_isInitialized = false;

   if (size <= 1) {
     m_p.setZero();
     m_transpositions.setZero();
     m_sign = ei_real(a.coeff(0,0))>0 ? 1:-1;
     m_isInitialized = true;
     return *this;
   }

   RealScalar cutoff = 0, biggest_in_corner;

   // By using a temorary, packet-aligned products are guarenteed. In the LLT
   // case this is unnecessary because the diagonal is included and will always
   // have optimal alignment.
   m_temporary.resize(size);

   for (Index j = 0; j < size; ++j)
   {
     // Find largest diagonal element
     Index index_of_biggest_in_corner;
     biggest_in_corner = m_matrix.diagonal().tail(size-j).cwiseAbs()
                        .maxCoeff(&index_of_biggest_in_corner);
     index_of_biggest_in_corner += j;

     if(j == 0)
     {
       // The biggest overall is the point of reference to which further diagonals
       // are compared; if any diagonal is negligible compared
       // to the largest overall, the algorithm bails.
       cutoff = ei_abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);

       m_sign = ei_real(m_matrix.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
     }

     // Finish early if the matrix is not full rank.
     if(biggest_in_corner < cutoff)
     {
       for(Index i = j; i < size; i++) m_transpositions.coeffRef(i) = i;
       break;
     }

     m_transpositions.coeffRef(j) = index_of_biggest_in_corner;
     if(j != index_of_biggest_in_corner)
     {
       m_matrix.row(j).swap(m_matrix.row(index_of_biggest_in_corner));
       m_matrix.col(j).swap(m_matrix.col(index_of_biggest_in_corner));
     }

     if (j == 0) {
       m_matrix.row(0) = m_matrix.row(0).conjugate();
       m_matrix.col(0).tail(size-1) = m_matrix.row(0).tail(size-1) / m_matrix.coeff(0,0);
       continue;
     }

     RealScalar Djj = ei_real(m_matrix.coeff(j,j) -  m_matrix.row(j).head(j).dot(m_matrix.col(j).head(j)));
     m_matrix.coeffRef(j,j) = Djj;

     Index endSize = size - j - 1;
     if (endSize > 0) {
       m_temporary.tail(endSize).noalias() = m_matrix.block(j+1,0, endSize, j)
                                 * m_matrix.col(j).head(j).conjugate();

       m_matrix.row(j).tail(endSize) = m_matrix.row(j).tail(endSize).conjugate()
                                     - m_temporary.tail(endSize).transpose();

       if(ei_abs(Djj) > cutoff)
       {
         m_matrix.col(j).tail(endSize) = m_matrix.row(j).tail(endSize) / Djj;
       }
     }
   }

   // Reverse applied swaps to get P matrix.
   for(Index k = 0; k < size; ++k) m_p.coeffRef(k) = k;
   for(Index k = size-1; k >= 0; --k) {
     std::swap(m_p.coeffRef(k), m_p.coeffRef(m_transpositions.coeff(k)));
   }

   m_isInitialized = true;
   return *this;
 }

 template<typename _MatrixType, typename Rhs>
 struct ei_solve_retval<LDLT<_MatrixType>, Rhs>
   : ei_solve_retval_base<LDLT<_MatrixType>, Rhs>
 {
   EIGEN_MAKE_SOLVE_HELPERS(LDLT<_MatrixType>,Rhs)

   template<typename Dest> void evalTo(Dest& dst) const
   {
     dst = rhs();
     dec().solveInPlace(dst);
   }
 };

 /** This is the \em in-place version of solve().
   *
   * \param bAndX represents both the right-hand side matrix b and result x.
   *
   * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
   *
   * This version avoids a copy when the right hand side matrix b is not
   * needed anymore.
   *
   * \sa LDLT::solve(), MatrixBase::ldlt()
   */
 template<typename MatrixType>
 template<typename Derived>
 bool LDLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
 {
   ei_assert(m_isInitialized && "LDLT is not initialized.");
   const Index size = m_matrix.rows();
   ei_assert(size == bAndX.rows());

   // z = P b
   for(Index i = 0; i < size; ++i) bAndX.row(m_transpositions.coeff(i)).swap(bAndX.row(i));

   // y = L^-1 z
   //matrixL().solveInPlace(bAndX);
   m_matrix.template triangularView<UnitLower>().solveInPlace(bAndX);

   // w = D^-1 y
   bAndX = m_matrix.diagonal().asDiagonal().inverse() * bAndX;

   // u = L^-T w
   m_matrix.adjoint().template triangularView<UnitUpper>().solveInPlace(bAndX);

   // x = P^T u
   for (Index i = size-1; i >= 0; --i) bAndX.row(m_transpositions.coeff(i)).swap(bAndX.row(i));

   return true;
 }

 /** \returns the matrix represented by the decomposition,
  * i.e., it returns the product: P^T L D L^* P.
  * This function is provided for debug purpose. */
 template<typename MatrixType>
 MatrixType LDLT<MatrixType>::reconstructedMatrix() const
 {
   ei_assert(m_isInitialized && "LDLT is not initialized.");
   const Index size = m_matrix.rows();
   MatrixType res(size,size);
   res.setIdentity();

   // PI
   for(Index i = 0; i < size; ++i) res.row(m_transpositions.coeff(i)).swap(res.row(i));
   // L^* P
   res = matrixL().adjoint() * res;
   // D(L^*P)
   res = vectorD().asDiagonal() * res;
   // L(DL^*P)
   res = matrixL() * res;
   // P^T (LDL^*P)
   for (Index i = size-1; i >= 0; --i) res.row(m_transpositions.coeff(i)).swap(res.row(i));

   return res;
 }

 /** \cholesky_module
   * \returns the Cholesky decomposition with full pivoting without square root of \c *this
   */
 template<typename Derived>
 inline const LDLT<typename MatrixBase<Derived>::PlainObject>
 MatrixBase<Derived>::ldlt() const
 {
   return LDLT<PlainObject>(derived());
 }

 #endif // EIGEN_LDLT_H
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
	// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
	// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
	//
	// Eigen is free software; you can redistribute it and/or
	// modify it under the terms of the GNU Lesser General Public
	// License as published by the Free Software Foundation; either
	// version 3 of the License, or (at your option) any later version.
	//
	// Alternatively, you can redistribute it and/or
	// modify it under the terms of the GNU General Public License as
	// published by the Free Software Foundation; either version 2 of
	// the License, or (at your option) any later version.
	//
	// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
	// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
	// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
	// GNU General Public License for more details.
	//
	// You should have received a copy of the GNU Lesser General Public
	// License and a copy of the GNU General Public License along with
	// Eigen. If not, see <http://www.gnu.org/licenses/>.

	#ifndef EIGEN_LDLT_H
	#define EIGEN_LDLT_H

	/** \ingroup cholesky_Module
	*
	* \class LDLT
	*
	* \brief Robust Cholesky decomposition of a matrix
	*
	* \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
	*
	* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
	* matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L
	* is lower triangular with a unit diagonal and D is a diagonal matrix.
	*
	* The decomposition uses pivoting to ensure stability, so that L will have
	* zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
	* on D also stabilizes the computation.
	*
	* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
	* decomposition to determine whether a system of equations has a solution.
	*
	* \sa MatrixBase::ldlt(), class LLT
	*/
	/* THIS PART OF THE DOX IS CURRENTLY DISABLED BECAUSE INACCURATE BECAUSE OF BUG IN THE DECOMPOSITION CODE
	* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
	* the strict lower part does not have to store correct values.
	*/
	template<typename _MatrixType> class LDLT
	{
	public:
	typedef _MatrixType MatrixType;
	enum {
	RowsAtCompileTime = MatrixType::RowsAtCompileTime,
	ColsAtCompileTime = MatrixType::ColsAtCompileTime,
	Options = MatrixType::Options,
	MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
	MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
	};
	typedef typename MatrixType::Scalar Scalar;
	typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
	typedef typename MatrixType::Index Index;
	typedef typename ei_plain_col_type<MatrixType, Index>::type IntColVectorType;
	typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;

	/** \brief Default Constructor.
	*
	* The default constructor is useful in cases in which the user intends to
	* perform decompositions via LDLT::compute(const MatrixType&).
	*/
	LDLT() : m_matrix(), m_p(), m_transpositions(), m_isInitialized(false) {}

	/** \brief Default Constructor with memory preallocation
	*
	* Like the default constructor but with preallocation of the internal data
	* according to the specified problem \a size.
	* \sa LDLT()
	*/
	LDLT(Index size) : m_matrix(size, size),
	m_p(size),
	m_transpositions(size),
	m_temporary(size),
	m_isInitialized(false) {}

	LDLT(const MatrixType& matrix)
	: m_matrix(matrix.rows(), matrix.cols()),
	m_p(matrix.rows()),
	m_transpositions(matrix.rows()),
	m_temporary(matrix.rows()),
	m_isInitialized(false)
	{
	compute(matrix);
	}

	/** \returns the lower triangular matrix L */
	inline TriangularView<MatrixType, UnitLower> matrixL(void) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_matrix;
	}

	/** \returns a vector of integers, whose size is the number of rows of the matrix being decomposed,
	* representing the P permutation i.e. the permutation of the rows. For its precise meaning,
	* see the examples given in the documentation of class FullPivLU.
	*/
	inline const IntColVectorType& permutationP() const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_p;
	}

	/** \returns the coefficients of the diagonal matrix D */
	inline Diagonal<MatrixType,0> vectorD(void) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_matrix.diagonal();
	}

	/** \returns true if the matrix is positive (semidefinite) */
	inline bool isPositive(void) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_sign == 1;
	}

	/** \returns true if the matrix is negative (semidefinite) */
	inline bool isNegative(void) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_sign == -1;
	}

	/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
	*
	* \note_about_checking_solutions
	*
	* \sa solveInPlace(), MatrixBase::ldlt()
	*/
	template<typename Rhs>
	inline const ei_solve_retval<LDLT, Rhs>
	solve(const MatrixBase<Rhs>& b) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	ei_assert(m_matrix.rows()==b.rows()
	&& "LDLT::solve(): invalid number of rows of the right hand side matrix b");
	return ei_solve_retval<LDLT, Rhs>(*this, b.derived());
	}

	template<typename Derived>
	bool solveInPlace(MatrixBase<Derived> &bAndX) const;

	LDLT& compute(const MatrixType& matrix);

	/** \returns the LDLT decomposition matrix
	*
	* TODO: document the storage layout
	*/
	inline const MatrixType& matrixLDLT() const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	return m_matrix;
	}

	MatrixType reconstructedMatrix() const;

	inline Index rows() const { return m_matrix.rows(); }
	inline Index cols() const { return m_matrix.cols(); }

	protected:
	/** \internal
	* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
	* The strict upper part is used during the decomposition, the strict lower
	* part correspond to the coefficients of L (its diagonal is equal to 1 and
	* is not stored), and the diagonal entries correspond to D.
	*/
	MatrixType m_matrix;
	IntColVectorType m_p;
	IntColVectorType m_transpositions; // FIXME do we really need to store permanently the transpositions?
	TmpMatrixType m_temporary;
	Index m_sign;
	bool m_isInitialized;
	};

	/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
	*/
	template<typename MatrixType>
	LDLT<MatrixType>& LDLT<MatrixType>::compute(const MatrixType& a)
	{
	ei_assert(a.rows()==a.cols());
	const Index size = a.rows();

	m_matrix = a;

	m_p.resize(size);
	m_transpositions.resize(size);
	m_isInitialized = false;

	if (size <= 1) {
	m_p.setZero();
	m_transpositions.setZero();
	m_sign = ei_real(a.coeff(0,0))>0 ? 1:-1;
	m_isInitialized = true;
	return *this;
	}

	RealScalar cutoff = 0, biggest_in_corner;

	// By using a temorary, packet-aligned products are guarenteed. In the LLT
	// case this is unnecessary because the diagonal is included and will always
	// have optimal alignment.
	m_temporary.resize(size);

	for (Index j = 0; j < size; ++j)
	{
	// Find largest diagonal element
	Index index_of_biggest_in_corner;
	biggest_in_corner = m_matrix.diagonal().tail(size-j).cwiseAbs()
	.maxCoeff(&index_of_biggest_in_corner);
	index_of_biggest_in_corner += j;

	if(j == 0)
	{
	// The biggest overall is the point of reference to which further diagonals
	// are compared; if any diagonal is negligible compared
	// to the largest overall, the algorithm bails.
	cutoff = ei_abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);

	m_sign = ei_real(m_matrix.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
	}

	// Finish early if the matrix is not full rank.
	if(biggest_in_corner < cutoff)
	{
	for(Index i = j; i < size; i++) m_transpositions.coeffRef(i) = i;
	break;
	}

	m_transpositions.coeffRef(j) = index_of_biggest_in_corner;
	if(j != index_of_biggest_in_corner)
	{
	m_matrix.row(j).swap(m_matrix.row(index_of_biggest_in_corner));
	m_matrix.col(j).swap(m_matrix.col(index_of_biggest_in_corner));
	}

	if (j == 0) {
	m_matrix.row(0) = m_matrix.row(0).conjugate();
	m_matrix.col(0).tail(size-1) = m_matrix.row(0).tail(size-1) / m_matrix.coeff(0,0);
	continue;
	}

	RealScalar Djj = ei_real(m_matrix.coeff(j,j) - m_matrix.row(j).head(j).dot(m_matrix.col(j).head(j)));
	m_matrix.coeffRef(j,j) = Djj;

	Index endSize = size - j - 1;
	if (endSize > 0) {
	m_temporary.tail(endSize).noalias() = m_matrix.block(j+1,0, endSize, j)
	* m_matrix.col(j).head(j).conjugate();

	m_matrix.row(j).tail(endSize) = m_matrix.row(j).tail(endSize).conjugate()
	- m_temporary.tail(endSize).transpose();

	if(ei_abs(Djj) > cutoff)
	{
	m_matrix.col(j).tail(endSize) = m_matrix.row(j).tail(endSize) / Djj;
	}
	}
	}

	// Reverse applied swaps to get P matrix.
	for(Index k = 0; k < size; ++k) m_p.coeffRef(k) = k;
	for(Index k = size-1; k >= 0; --k) {
	std::swap(m_p.coeffRef(k), m_p.coeffRef(m_transpositions.coeff(k)));
	}

	m_isInitialized = true;
	return *this;
	}

	template<typename _MatrixType, typename Rhs>
	struct ei_solve_retval<LDLT<_MatrixType>, Rhs>
	: ei_solve_retval_base<LDLT<_MatrixType>, Rhs>
	{
	EIGEN_MAKE_SOLVE_HELPERS(LDLT<_MatrixType>,Rhs)

	template<typename Dest> void evalTo(Dest& dst) const
	{
	dst = rhs();
	dec().solveInPlace(dst);
	}
	};

	/** This is the \em in-place version of solve().
	*
	* \param bAndX represents both the right-hand side matrix b and result x.
	*
	* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
	*
	* This version avoids a copy when the right hand side matrix b is not
	* needed anymore.
	*
	* \sa LDLT::solve(), MatrixBase::ldlt()
	*/
	template<typename MatrixType>
	template<typename Derived>
	bool LDLT<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	const Index size = m_matrix.rows();
	ei_assert(size == bAndX.rows());

	// z = P b
	for(Index i = 0; i < size; ++i) bAndX.row(m_transpositions.coeff(i)).swap(bAndX.row(i));

	// y = L^-1 z
	//matrixL().solveInPlace(bAndX);
	m_matrix.template triangularView<UnitLower>().solveInPlace(bAndX);

	// w = D^-1 y
	bAndX = m_matrix.diagonal().asDiagonal().inverse() * bAndX;

	// u = L^-T w
	m_matrix.adjoint().template triangularView<UnitUpper>().solveInPlace(bAndX);

	// x = P^T u
	for (Index i = size-1; i >= 0; --i) bAndX.row(m_transpositions.coeff(i)).swap(bAndX.row(i));

	return true;
	}

	/** \returns the matrix represented by the decomposition,
	* i.e., it returns the product: P^T L D L^* P.
	* This function is provided for debug purpose. */
	template<typename MatrixType>
	MatrixType LDLT<MatrixType>::reconstructedMatrix() const
	{
	ei_assert(m_isInitialized && "LDLT is not initialized.");
	const Index size = m_matrix.rows();
	MatrixType res(size,size);
	res.setIdentity();

	// PI
	for(Index i = 0; i < size; ++i) res.row(m_transpositions.coeff(i)).swap(res.row(i));
	// L^* P
	res = matrixL().adjoint() * res;
	// D(L^*P)
	res = vectorD().asDiagonal() * res;
	// L(DL^*P)
	res = matrixL() * res;
	// P^T (LDL^*P)
	for (Index i = size-1; i >= 0; --i) res.row(m_transpositions.coeff(i)).swap(res.row(i));

	return res;
	}

	/** \cholesky_module
	* \returns the Cholesky decomposition with full pivoting without square root of \c *this
	*/
	template<typename Derived>
	inline const LDLT<typename MatrixBase<Derived>::PlainObject>
	MatrixBase<Derived>::ldlt() const
	{
	return LDLT<PlainObject>(derived());
	}

	#endif // EIGEN_LDLT_H