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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_UMFPACKSUPPORT_H
#define EIGEN_UMFPACKSUPPORT_H
/* TODO extract L, extrac U, compute det, etc... */
inline int umfpack_symbolic(int n_row,int n_col,
const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
{
return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
}
inline int umfpack_symbolic(int n_row,int n_col,
const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
{
return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&Ax[0].real(),0,Symbolic,Control,Info);
}
inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
void *Symbolic, void **Numeric,
const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
{
return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
}
inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
void *Symbolic, void **Numeric,
const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
{
return umfpack_zi_numeric(Ap,Ai,&Ax[0].real(),0,Symbolic,Numeric,Control,Info);
}
inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
double X[], const double B[], void *Numeric,
const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
{
return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
}
inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
std::complex<double> X[], const std::complex<double> B[], void *Numeric,
const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
{
return umfpack_zi_solve(sys,Ap,Ai,&Ax[0].real(),0,&X[0].real(),0,&B[0].real(),0,Numeric,Control,Info);
}
template<typename MatrixType>
class SparseLU<MatrixType,UmfPack> : public SparseLU<MatrixType>
{
protected:
typedef SparseLU<MatrixType> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::RealScalar RealScalar;
typedef Matrix<Scalar,Dynamic,1> Vector;
using Base::m_flags;
using Base::m_status;
public:
SparseLU(int flags = NaturalOrdering)
: Base(flags), m_numeric(0)
{
}
SparseLU(const MatrixType& matrix, int flags = NaturalOrdering)
: Base(flags), m_numeric(0)
{
compute(matrix);
}
~SparseLU()
{
if (m_numeric)
umfpack_di_free_numeric(&m_numeric);
}
template<typename BDerived, typename XDerived>
bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x) const;
void compute(const MatrixType& matrix);
protected:
// cached data:
void* m_numeric;
const MatrixType* m_matrixRef;
};
template<typename MatrixType>
void SparseLU<MatrixType,UmfPack>::compute(const MatrixType& a)
{
const int rows = a.rows();
const int cols = a.cols();
ei_assert((MatrixType::Flags&RowMajorBit)==0 && "Row major matrices are not supported yet");
m_matrixRef = &a;
if (m_numeric)
umfpack_di_free_numeric(&m_numeric);
void* symbolic;
int errorCode = 0;
errorCode = umfpack_symbolic(rows, cols, a._outerIndexPtr(), a._innerIndexPtr(), a._valuePtr(),
&symbolic, 0, 0);
if (errorCode==0)
errorCode = umfpack_numeric(a._outerIndexPtr(), a._innerIndexPtr(), a._valuePtr(),
symbolic, &m_numeric, 0, 0);
umfpack_di_free_symbolic(&symbolic);
Base::m_succeeded = (errorCode==0);
}
// template<typename MatrixType>
// inline const MatrixType&
// SparseLU<MatrixType,SuperLU>::matrixL() const
// {
// ei_assert(false && "matrixL() is Not supported by the SuperLU backend");
// return m_matrix;
// }
//
// template<typename MatrixType>
// inline const MatrixType&
// SparseLU<MatrixType,SuperLU>::matrixU() const
// {
// ei_assert(false && "matrixU() is Not supported by the SuperLU backend");
// return m_matrix;
// }
template<typename MatrixType>
template<typename BDerived,typename XDerived>
bool SparseLU<MatrixType,UmfPack>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> *x) const
{
//const int size = m_matrix.rows();
const int rhsCols = b.cols();
// ei_assert(size==b.rows());
ei_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPack backend does not support non col-major rhs yet");
ei_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPack backend does not support non col-major result yet");
int errorCode;
for (int j=0; j<rhsCols; ++j)
{
errorCode = umfpack_solve(UMFPACK_A,
m_matrixRef->_outerIndexPtr(), m_matrixRef->_innerIndexPtr(), m_matrixRef->_valuePtr(),
&x->col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0);
if (errorCode!=0)
return false;
}
// errorCode = umfpack_di_solve(UMFPACK_A,
// m_matrixRef._outerIndexPtr(), m_matrixRef._innerIndexPtr(), m_matrixRef._valuePtr(),
// x->derived().data(), b.derived().data(), m_numeric, 0, 0);
return true;
}
#endif // EIGEN_UMFPACKSUPPORT_H
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