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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) 2008-2009 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_TAUCSSUPPORT_H
#define EIGEN_TAUCSSUPPORT_H
template<typename Derived>
taucs_ccs_matrix SparseMatrixBase<Derived>::asTaucsMatrix()
{
taucs_ccs_matrix res;
res.n = cols();
res.m = rows();
res.flags = 0;
res.colptr = derived()._outerIndexPtr();
res.rowind = derived()._innerIndexPtr();
res.values.v = derived()._valuePtr();
if (ei_is_same_type<Scalar,int>::ret)
res.flags |= TAUCS_INT;
else if (ei_is_same_type<Scalar,float>::ret)
res.flags |= TAUCS_SINGLE;
else if (ei_is_same_type<Scalar,double>::ret)
res.flags |= TAUCS_DOUBLE;
else if (ei_is_same_type<Scalar,std::complex<float> >::ret)
res.flags |= TAUCS_SCOMPLEX;
else if (ei_is_same_type<Scalar,std::complex<double> >::ret)
res.flags |= TAUCS_DCOMPLEX;
else
{
ei_assert(false && "Scalar type not supported by TAUCS");
}
if (Flags & UpperTriangular)
res.flags |= TAUCS_UPPER;
if (Flags & LowerTriangular)
res.flags |= TAUCS_LOWER;
if (Flags & SelfAdjoint)
res.flags |= (NumTraits<Scalar>::IsComplex ? TAUCS_HERMITIAN : TAUCS_SYMMETRIC);
else if ((Flags & UpperTriangular) || (Flags & LowerTriangular))
res.flags |= TAUCS_TRIANGULAR;
return res;
}
template<typename Scalar, int Flags>
MappedSparseMatrix<Scalar,Flags>::MappedSparseMatrix(taucs_ccs_matrix& taucsMat)
{
m_innerSize = taucsMat.m;
m_outerSize = taucsMat.n;
m_outerIndex = taucsMat.colptr;
m_innerIndices = taucsMat.rowind;
m_values = reinterpret_cast<Scalar*>(taucsMat.values.v);
m_nnz = taucsMat.colptr[taucsMat.n];
}
template<typename MatrixType>
class SparseLLT<MatrixType,Taucs> : public SparseLLT<MatrixType>
{
protected:
typedef SparseLLT<MatrixType> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::RealScalar RealScalar;
using Base::MatrixLIsDirty;
using Base::SupernodalFactorIsDirty;
using Base::m_flags;
using Base::m_matrix;
using Base::m_status;
public:
SparseLLT(int flags = 0)
: Base(flags), m_taucsSupernodalFactor(0)
{
}
SparseLLT(const MatrixType& matrix, int flags = 0)
: Base(flags), m_taucsSupernodalFactor(0)
{
compute(matrix);
}
~SparseLLT()
{
if (m_taucsSupernodalFactor)
taucs_supernodal_factor_free(m_taucsSupernodalFactor);
}
inline const typename Base::CholMatrixType& matrixL(void) const;
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &b) const;
void compute(const MatrixType& matrix);
protected:
void* m_taucsSupernodalFactor;
};
template<typename MatrixType>
void SparseLLT<MatrixType,Taucs>::compute(const MatrixType& a)
{
if (m_taucsSupernodalFactor)
{
taucs_supernodal_factor_free(m_taucsSupernodalFactor);
m_taucsSupernodalFactor = 0;
}
if (m_flags & IncompleteFactorization)
{
taucs_ccs_matrix taucsMatA = const_cast<MatrixType&>(a).asTaucsMatrix();
taucs_ccs_matrix* taucsRes = taucs_ccs_factor_llt(&taucsMatA, Base::m_precision, 0);
// the matrix returned by Taucs is not necessarily sorted,
// so let's copy it in two steps
DynamicSparseMatrix<Scalar,RowMajor> tmp = MappedSparseMatrix<Scalar>(*taucsRes);
m_matrix = tmp;
free(taucsRes);
m_status = (m_status & ~(CompleteFactorization|MatrixLIsDirty))
| IncompleteFactorization
| SupernodalFactorIsDirty;
}
else
{
taucs_ccs_matrix taucsMatA = const_cast<MatrixType&>(a).asTaucsMatrix();
if ( (m_flags & SupernodalLeftLooking)
|| ((!(m_flags & SupernodalMultifrontal)) && (m_flags & MemoryEfficient)) )
{
m_taucsSupernodalFactor = taucs_ccs_factor_llt_ll(&taucsMatA);
}
else
{
// use the faster Multifrontal routine
m_taucsSupernodalFactor = taucs_ccs_factor_llt_mf(&taucsMatA);
}
m_status = (m_status & ~IncompleteFactorization) | CompleteFactorization | MatrixLIsDirty;
}
}
template<typename MatrixType>
inline const typename SparseLLT<MatrixType>::CholMatrixType&
SparseLLT<MatrixType,Taucs>::matrixL() const
{
if (m_status & MatrixLIsDirty)
{
ei_assert(!(m_status & SupernodalFactorIsDirty));
taucs_ccs_matrix* taucsL = taucs_supernodal_factor_to_ccs(m_taucsSupernodalFactor);
// the matrix returned by Taucs is not necessarily sorted,
// so let's copy it in two steps
DynamicSparseMatrix<Scalar,RowMajor> tmp = MappedSparseMatrix<Scalar>(*taucsL);
const_cast<typename Base::CholMatrixType&>(m_matrix) = tmp;
free(taucsL);
m_status = (m_status & ~MatrixLIsDirty);
}
return m_matrix;
}
template<typename MatrixType>
template<typename Derived>
void SparseLLT<MatrixType,Taucs>::solveInPlace(MatrixBase<Derived> &b) const
{
bool inputIsCompatibleWithTaucs = (Derived::Flags&RowMajorBit)==0;
if (!inputIsCompatibleWithTaucs)
{
matrixL();
Base::solveInPlace(b);
}
else if (m_flags & IncompleteFactorization)
{
taucs_ccs_matrix taucsLLT = const_cast<typename Base::CholMatrixType&>(m_matrix).asTaucsMatrix();
typename ei_plain_matrix_type<Derived>::type x(b.rows());
for (int j=0; j<b.cols(); ++j)
{
taucs_ccs_solve_llt(&taucsLLT,x.data(),&b.col(j).coeffRef(0));
b.col(j) = x;
}
}
else
{
typename ei_plain_matrix_type<Derived>::type x(b.rows());
for (int j=0; j<b.cols(); ++j)
{
taucs_supernodal_solve_llt(m_taucsSupernodalFactor,x.data(),&b.col(j).coeffRef(0));
b.col(j) = x;
}
}
}
#endif // EIGEN_TAUCSSUPPORT_H
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