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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// 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_CHOLESKY_WITHOUT_SQUARE_ROOT_H
#define EIGEN_CHOLESKY_WITHOUT_SQUARE_ROOT_H
/** \deprecated \ingroup Cholesky_Module
*
* \class CholeskyWithoutSquareRoot
*
* \deprecated this class has been renamed LDLT
*/
template<typename MatrixType> class CholeskyWithoutSquareRoot
{
public:
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
CholeskyWithoutSquareRoot(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols())
{
compute(matrix);
}
/** \returns the lower triangular matrix L */
inline Part<MatrixType, UnitLowerTriangular> matrixL(void) const { return m_matrix; }
/** \returns the coefficients of the diagonal matrix D */
inline DiagonalCoeffs<MatrixType> vectorD(void) const { return m_matrix.diagonal(); }
/** \returns true if the matrix is positive definite */
inline bool isPositiveDefinite(void) const { return m_isPositiveDefinite; }
template<typename Derived>
EIGEN_DEPRECATED typename Derived::Eval solve(const MatrixBase<Derived> &b) const;
template<typename RhsDerived, typename ResDerived>
bool solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const;
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
void compute(const MatrixType& matrix);
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;
bool m_isPositiveDefinite;
};
/** \deprecated */
template<typename MatrixType>
void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
{
assert(a.rows()==a.cols());
const int size = a.rows();
m_matrix.resize(size, size);
m_isPositiveDefinite = true;
const RealScalar eps = ei_sqrt(precision<Scalar>());
if (size<=1)
{
m_matrix = a;
return;
}
// Let's preallocate a temporay vector to evaluate the matrix-vector product into it.
// Unlike the standard Cholesky decomposition, here we cannot evaluate it to the destination
// matrix because it a sub-row which is not compatible suitable for efficient packet evaluation.
// (at least if we assume the matrix is col-major)
Matrix<Scalar,MatrixType::RowsAtCompileTime,1> _temporary(size);
// Note that, in this algorithm the rows of the strict upper part of m_matrix is used to store
// column vector, thus the strange .conjugate() and .transpose()...
m_matrix.row(0) = a.row(0).conjugate();
m_matrix.col(0).end(size-1) = m_matrix.row(0).end(size-1) / m_matrix.coeff(0,0);
for (int j = 1; j < size; ++j)
{
RealScalar tmp = ei_real(a.coeff(j,j) - (m_matrix.row(j).start(j) * m_matrix.col(j).start(j).conjugate()).coeff(0,0));
m_matrix.coeffRef(j,j) = tmp;
if (tmp < eps)
{
m_isPositiveDefinite = false;
return;
}
int endSize = size-j-1;
if (endSize>0)
{
_temporary.end(endSize) = ( m_matrix.block(j+1,0, endSize, j)
* m_matrix.col(j).start(j).conjugate() ).lazy();
m_matrix.row(j).end(endSize) = a.row(j).end(endSize).conjugate()
- _temporary.end(endSize).transpose();
m_matrix.col(j).end(endSize) = m_matrix.row(j).end(endSize) / tmp;
}
}
}
/** \deprecated */
template<typename MatrixType>
template<typename Derived>
typename Derived::Eval CholeskyWithoutSquareRoot<MatrixType>::solve(const MatrixBase<Derived> &b) const
{
const int size = m_matrix.rows();
ei_assert(size==b.rows());
return m_matrix.adjoint().template part<UnitUpperTriangular>()
.solveTriangular(
( m_matrix.cwise().inverse().template part<Diagonal>()
* matrixL().solveTriangular(b))
);
}
/** \deprecated */
template<typename MatrixType>
template<typename RhsDerived, typename ResDerived>
bool CholeskyWithoutSquareRoot<MatrixType>
::solve(const MatrixBase<RhsDerived> &b, MatrixBase<ResDerived> *result) const
{
const int size = m_matrix.rows();
ei_assert(size==b.rows() && "Cholesky::solve(): invalid number of rows of the right hand side matrix b");
*result = b;
return solveInPlace(*result);
}
/** \deprecated */
template<typename MatrixType>
template<typename Derived>
bool CholeskyWithoutSquareRoot<MatrixType>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
const int size = m_matrix.rows();
ei_assert(size==bAndX.rows());
if (!m_isPositiveDefinite)
return false;
matrixL().solveTriangularInPlace(bAndX);
bAndX = (m_matrix.cwise().inverse().template part<Diagonal>() * bAndX).lazy();
m_matrix.adjoint().template part<UnitUpperTriangular>().solveTriangularInPlace(bAndX);
return true;
}
/** \cholesky_module
* \deprecated has been renamed ldlt()
*/
template<typename Derived>
inline const CholeskyWithoutSquareRoot<typename MatrixBase<Derived>::PlainMatrixType>
MatrixBase<Derived>::choleskyNoSqrt() const
{
return derived();
}
#endif // EIGEN_CHOLESKY_WITHOUT_SQUARE_ROOT_H
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