add Eigen as a dependency
This commit is contained in:
Sven Czarnian
678
external/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
vendored
Normal file
678
external/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
vendored
Normal file
@@ -0,0 +1,678 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@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/.
|
||||
|
||||
#ifndef EIGEN_PASTIXSUPPORT_H
|
||||
#define EIGEN_PASTIXSUPPORT_H
|
||||
|
||||
namespace Eigen {
|
||||
|
||||
#if defined(DCOMPLEX)
|
||||
#define PASTIX_COMPLEX COMPLEX
|
||||
#define PASTIX_DCOMPLEX DCOMPLEX
|
||||
#else
|
||||
#define PASTIX_COMPLEX std::complex<float>
|
||||
#define PASTIX_DCOMPLEX std::complex<double>
|
||||
#endif
|
||||
|
||||
/** \ingroup PaStiXSupport_Module
|
||||
* \brief Interface to the PaStix solver
|
||||
*
|
||||
* This class is used to solve the linear systems A.X = B via the PaStix library.
|
||||
* The matrix can be either real or complex, symmetric or not.
|
||||
*
|
||||
* \sa TutorialSparseDirectSolvers
|
||||
*/
|
||||
template<typename _MatrixType, bool IsStrSym = false> class PastixLU;
|
||||
template<typename _MatrixType, int Options> class PastixLLT;
|
||||
template<typename _MatrixType, int Options> class PastixLDLT;
|
||||
|
||||
namespace internal
|
||||
{
|
||||
|
||||
template<class Pastix> struct pastix_traits;
|
||||
|
||||
template<typename _MatrixType>
|
||||
struct pastix_traits< PastixLU<_MatrixType> >
|
||||
{
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef typename _MatrixType::Scalar Scalar;
|
||||
typedef typename _MatrixType::RealScalar RealScalar;
|
||||
typedef typename _MatrixType::StorageIndex StorageIndex;
|
||||
};
|
||||
|
||||
template<typename _MatrixType, int Options>
|
||||
struct pastix_traits< PastixLLT<_MatrixType,Options> >
|
||||
{
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef typename _MatrixType::Scalar Scalar;
|
||||
typedef typename _MatrixType::RealScalar RealScalar;
|
||||
typedef typename _MatrixType::StorageIndex StorageIndex;
|
||||
};
|
||||
|
||||
template<typename _MatrixType, int Options>
|
||||
struct pastix_traits< PastixLDLT<_MatrixType,Options> >
|
||||
{
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef typename _MatrixType::Scalar Scalar;
|
||||
typedef typename _MatrixType::RealScalar RealScalar;
|
||||
typedef typename _MatrixType::StorageIndex StorageIndex;
|
||||
};
|
||||
|
||||
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
|
||||
{
|
||||
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
|
||||
if (nbrhs == 0) {x = NULL; nbrhs=1;}
|
||||
s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
|
||||
}
|
||||
|
||||
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
|
||||
{
|
||||
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
|
||||
if (nbrhs == 0) {x = NULL; nbrhs=1;}
|
||||
d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
|
||||
}
|
||||
|
||||
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
|
||||
{
|
||||
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
|
||||
if (nbrhs == 0) {x = NULL; nbrhs=1;}
|
||||
c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm);
|
||||
}
|
||||
|
||||
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
|
||||
{
|
||||
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
|
||||
if (nbrhs == 0) {x = NULL; nbrhs=1;}
|
||||
z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm);
|
||||
}
|
||||
|
||||
// Convert the matrix to Fortran-style Numbering
|
||||
template <typename MatrixType>
|
||||
void c_to_fortran_numbering (MatrixType& mat)
|
||||
{
|
||||
if ( !(mat.outerIndexPtr()[0]) )
|
||||
{
|
||||
int i;
|
||||
for(i = 0; i <= mat.rows(); ++i)
|
||||
++mat.outerIndexPtr()[i];
|
||||
for(i = 0; i < mat.nonZeros(); ++i)
|
||||
++mat.innerIndexPtr()[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Convert to C-style Numbering
|
||||
template <typename MatrixType>
|
||||
void fortran_to_c_numbering (MatrixType& mat)
|
||||
{
|
||||
// Check the Numbering
|
||||
if ( mat.outerIndexPtr()[0] == 1 )
|
||||
{ // Convert to C-style numbering
|
||||
int i;
|
||||
for(i = 0; i <= mat.rows(); ++i)
|
||||
--mat.outerIndexPtr()[i];
|
||||
for(i = 0; i < mat.nonZeros(); ++i)
|
||||
--mat.innerIndexPtr()[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// This is the base class to interface with PaStiX functions.
|
||||
// Users should not used this class directly.
|
||||
template <class Derived>
|
||||
class PastixBase : public SparseSolverBase<Derived>
|
||||
{
|
||||
protected:
|
||||
typedef SparseSolverBase<Derived> Base;
|
||||
using Base::derived;
|
||||
using Base::m_isInitialized;
|
||||
public:
|
||||
using Base::_solve_impl;
|
||||
|
||||
typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType;
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
typedef typename MatrixType::StorageIndex StorageIndex;
|
||||
typedef Matrix<Scalar,Dynamic,1> Vector;
|
||||
typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
|
||||
enum {
|
||||
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
|
||||
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
|
||||
};
|
||||
|
||||
public:
|
||||
|
||||
PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0)
|
||||
{
|
||||
init();
|
||||
}
|
||||
|
||||
~PastixBase()
|
||||
{
|
||||
clean();
|
||||
}
|
||||
|
||||
template<typename Rhs,typename Dest>
|
||||
bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
|
||||
|
||||
/** Returns a reference to the integer vector IPARM of PaStiX parameters
|
||||
* to modify the default parameters.
|
||||
* The statistics related to the different phases of factorization and solve are saved here as well
|
||||
* \sa analyzePattern() factorize()
|
||||
*/
|
||||
Array<StorageIndex,IPARM_SIZE,1>& iparm()
|
||||
{
|
||||
return m_iparm;
|
||||
}
|
||||
|
||||
/** Return a reference to a particular index parameter of the IPARM vector
|
||||
* \sa iparm()
|
||||
*/
|
||||
|
||||
int& iparm(int idxparam)
|
||||
{
|
||||
return m_iparm(idxparam);
|
||||
}
|
||||
|
||||
/** Returns a reference to the double vector DPARM of PaStiX parameters
|
||||
* The statistics related to the different phases of factorization and solve are saved here as well
|
||||
* \sa analyzePattern() factorize()
|
||||
*/
|
||||
Array<double,DPARM_SIZE,1>& dparm()
|
||||
{
|
||||
return m_dparm;
|
||||
}
|
||||
|
||||
|
||||
/** Return a reference to a particular index parameter of the DPARM vector
|
||||
* \sa dparm()
|
||||
*/
|
||||
double& dparm(int idxparam)
|
||||
{
|
||||
return m_dparm(idxparam);
|
||||
}
|
||||
|
||||
inline Index cols() const { return m_size; }
|
||||
inline Index rows() const { return m_size; }
|
||||
|
||||
/** \brief Reports whether previous computation was successful.
|
||||
*
|
||||
* \returns \c Success if computation was succesful,
|
||||
* \c NumericalIssue if the PaStiX reports a problem
|
||||
* \c InvalidInput if the input matrix is invalid
|
||||
*
|
||||
* \sa iparm()
|
||||
*/
|
||||
ComputationInfo info() const
|
||||
{
|
||||
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
|
||||
return m_info;
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
// Initialize the Pastix data structure, check the matrix
|
||||
void init();
|
||||
|
||||
// Compute the ordering and the symbolic factorization
|
||||
void analyzePattern(ColSpMatrix& mat);
|
||||
|
||||
// Compute the numerical factorization
|
||||
void factorize(ColSpMatrix& mat);
|
||||
|
||||
// Free all the data allocated by Pastix
|
||||
void clean()
|
||||
{
|
||||
eigen_assert(m_initisOk && "The Pastix structure should be allocated first");
|
||||
m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
|
||||
m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
|
||||
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
|
||||
m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
|
||||
}
|
||||
|
||||
void compute(ColSpMatrix& mat);
|
||||
|
||||
int m_initisOk;
|
||||
int m_analysisIsOk;
|
||||
int m_factorizationIsOk;
|
||||
mutable ComputationInfo m_info;
|
||||
mutable pastix_data_t *m_pastixdata; // Data structure for pastix
|
||||
mutable int m_comm; // The MPI communicator identifier
|
||||
mutable Array<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
|
||||
mutable Array<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
|
||||
mutable Matrix<StorageIndex,Dynamic,1> m_perm; // Permutation vector
|
||||
mutable Matrix<StorageIndex,Dynamic,1> m_invp; // Inverse permutation vector
|
||||
mutable int m_size; // Size of the matrix
|
||||
};
|
||||
|
||||
/** Initialize the PaStiX data structure.
|
||||
*A first call to this function fills iparm and dparm with the default PaStiX parameters
|
||||
* \sa iparm() dparm()
|
||||
*/
|
||||
template <class Derived>
|
||||
void PastixBase<Derived>::init()
|
||||
{
|
||||
m_size = 0;
|
||||
m_iparm.setZero(IPARM_SIZE);
|
||||
m_dparm.setZero(DPARM_SIZE);
|
||||
|
||||
m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
|
||||
pastix(&m_pastixdata, MPI_COMM_WORLD,
|
||||
0, 0, 0, 0,
|
||||
0, 0, 0, 1, m_iparm.data(), m_dparm.data());
|
||||
|
||||
m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
|
||||
m_iparm[IPARM_VERBOSE] = API_VERBOSE_NOT;
|
||||
m_iparm[IPARM_ORDERING] = API_ORDER_SCOTCH;
|
||||
m_iparm[IPARM_INCOMPLETE] = API_NO;
|
||||
m_iparm[IPARM_OOC_LIMIT] = 2000;
|
||||
m_iparm[IPARM_RHS_MAKING] = API_RHS_B;
|
||||
m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
|
||||
|
||||
m_iparm(IPARM_START_TASK) = API_TASK_INIT;
|
||||
m_iparm(IPARM_END_TASK) = API_TASK_INIT;
|
||||
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
|
||||
0, 0, 0, 0, m_iparm.data(), m_dparm.data());
|
||||
|
||||
// Check the returned error
|
||||
if(m_iparm(IPARM_ERROR_NUMBER)) {
|
||||
m_info = InvalidInput;
|
||||
m_initisOk = false;
|
||||
}
|
||||
else {
|
||||
m_info = Success;
|
||||
m_initisOk = true;
|
||||
}
|
||||
}
|
||||
|
||||
template <class Derived>
|
||||
void PastixBase<Derived>::compute(ColSpMatrix& mat)
|
||||
{
|
||||
eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
|
||||
|
||||
analyzePattern(mat);
|
||||
factorize(mat);
|
||||
|
||||
m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
|
||||
}
|
||||
|
||||
|
||||
template <class Derived>
|
||||
void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat)
|
||||
{
|
||||
eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
|
||||
|
||||
// clean previous calls
|
||||
if(m_size>0)
|
||||
clean();
|
||||
|
||||
m_size = internal::convert_index<int>(mat.rows());
|
||||
m_perm.resize(m_size);
|
||||
m_invp.resize(m_size);
|
||||
|
||||
m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
|
||||
m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
|
||||
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
|
||||
mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
|
||||
|
||||
// Check the returned error
|
||||
if(m_iparm(IPARM_ERROR_NUMBER))
|
||||
{
|
||||
m_info = NumericalIssue;
|
||||
m_analysisIsOk = false;
|
||||
}
|
||||
else
|
||||
{
|
||||
m_info = Success;
|
||||
m_analysisIsOk = true;
|
||||
}
|
||||
}
|
||||
|
||||
template <class Derived>
|
||||
void PastixBase<Derived>::factorize(ColSpMatrix& mat)
|
||||
{
|
||||
// if(&m_cpyMat != &mat) m_cpyMat = mat;
|
||||
eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
|
||||
m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
|
||||
m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
|
||||
m_size = internal::convert_index<int>(mat.rows());
|
||||
|
||||
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
|
||||
mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
|
||||
|
||||
// Check the returned error
|
||||
if(m_iparm(IPARM_ERROR_NUMBER))
|
||||
{
|
||||
m_info = NumericalIssue;
|
||||
m_factorizationIsOk = false;
|
||||
m_isInitialized = false;
|
||||
}
|
||||
else
|
||||
{
|
||||
m_info = Success;
|
||||
m_factorizationIsOk = true;
|
||||
m_isInitialized = true;
|
||||
}
|
||||
}
|
||||
|
||||
/* Solve the system */
|
||||
template<typename Base>
|
||||
template<typename Rhs,typename Dest>
|
||||
bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
|
||||
{
|
||||
eigen_assert(m_isInitialized && "The matrix should be factorized first");
|
||||
EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
|
||||
THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
|
||||
int rhs = 1;
|
||||
|
||||
x = b; /* on return, x is overwritten by the computed solution */
|
||||
|
||||
for (int i = 0; i < b.cols(); i++){
|
||||
m_iparm[IPARM_START_TASK] = API_TASK_SOLVE;
|
||||
m_iparm[IPARM_END_TASK] = API_TASK_REFINE;
|
||||
|
||||
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
|
||||
m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
|
||||
}
|
||||
|
||||
// Check the returned error
|
||||
m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue;
|
||||
|
||||
return m_iparm(IPARM_ERROR_NUMBER)==0;
|
||||
}
|
||||
|
||||
/** \ingroup PaStiXSupport_Module
|
||||
* \class PastixLU
|
||||
* \brief Sparse direct LU solver based on PaStiX library
|
||||
*
|
||||
* This class is used to solve the linear systems A.X = B with a supernodal LU
|
||||
* factorization in the PaStiX library. The matrix A should be squared and nonsingular
|
||||
* PaStiX requires that the matrix A has a symmetric structural pattern.
|
||||
* This interface can symmetrize the input matrix otherwise.
|
||||
* The vectors or matrices X and B can be either dense or sparse.
|
||||
*
|
||||
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
||||
* \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
|
||||
* NOTE : Note that if the analysis and factorization phase are called separately,
|
||||
* the input matrix will be symmetrized at each call, hence it is advised to
|
||||
* symmetrize the matrix in a end-user program and set \p IsStrSym to true
|
||||
*
|
||||
* \implsparsesolverconcept
|
||||
*
|
||||
* \sa \ref TutorialSparseSolverConcept, class SparseLU
|
||||
*
|
||||
*/
|
||||
template<typename _MatrixType, bool IsStrSym>
|
||||
class PastixLU : public PastixBase< PastixLU<_MatrixType> >
|
||||
{
|
||||
public:
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef PastixBase<PastixLU<MatrixType> > Base;
|
||||
typedef typename Base::ColSpMatrix ColSpMatrix;
|
||||
typedef typename MatrixType::StorageIndex StorageIndex;
|
||||
|
||||
public:
|
||||
PastixLU() : Base()
|
||||
{
|
||||
init();
|
||||
}
|
||||
|
||||
explicit PastixLU(const MatrixType& matrix):Base()
|
||||
{
|
||||
init();
|
||||
compute(matrix);
|
||||
}
|
||||
/** Compute the LU supernodal factorization of \p matrix.
|
||||
* iparm and dparm can be used to tune the PaStiX parameters.
|
||||
* see the PaStiX user's manual
|
||||
* \sa analyzePattern() factorize()
|
||||
*/
|
||||
void compute (const MatrixType& matrix)
|
||||
{
|
||||
m_structureIsUptodate = false;
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::compute(temp);
|
||||
}
|
||||
/** Compute the LU symbolic factorization of \p matrix using its sparsity pattern.
|
||||
* Several ordering methods can be used at this step. See the PaStiX user's manual.
|
||||
* The result of this operation can be used with successive matrices having the same pattern as \p matrix
|
||||
* \sa factorize()
|
||||
*/
|
||||
void analyzePattern(const MatrixType& matrix)
|
||||
{
|
||||
m_structureIsUptodate = false;
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::analyzePattern(temp);
|
||||
}
|
||||
|
||||
/** Compute the LU supernodal factorization of \p matrix
|
||||
* WARNING The matrix \p matrix should have the same structural pattern
|
||||
* as the same used in the analysis phase.
|
||||
* \sa analyzePattern()
|
||||
*/
|
||||
void factorize(const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::factorize(temp);
|
||||
}
|
||||
protected:
|
||||
|
||||
void init()
|
||||
{
|
||||
m_structureIsUptodate = false;
|
||||
m_iparm(IPARM_SYM) = API_SYM_NO;
|
||||
m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
|
||||
}
|
||||
|
||||
void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
|
||||
{
|
||||
if(IsStrSym)
|
||||
out = matrix;
|
||||
else
|
||||
{
|
||||
if(!m_structureIsUptodate)
|
||||
{
|
||||
// update the transposed structure
|
||||
m_transposedStructure = matrix.transpose();
|
||||
|
||||
// Set the elements of the matrix to zero
|
||||
for (Index j=0; j<m_transposedStructure.outerSize(); ++j)
|
||||
for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it)
|
||||
it.valueRef() = 0.0;
|
||||
|
||||
m_structureIsUptodate = true;
|
||||
}
|
||||
|
||||
out = m_transposedStructure + matrix;
|
||||
}
|
||||
internal::c_to_fortran_numbering(out);
|
||||
}
|
||||
|
||||
using Base::m_iparm;
|
||||
using Base::m_dparm;
|
||||
|
||||
ColSpMatrix m_transposedStructure;
|
||||
bool m_structureIsUptodate;
|
||||
};
|
||||
|
||||
/** \ingroup PaStiXSupport_Module
|
||||
* \class PastixLLT
|
||||
* \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
|
||||
*
|
||||
* This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
|
||||
* available in the PaStiX library. The matrix A should be symmetric and positive definite
|
||||
* WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
|
||||
* The vectors or matrices X and B can be either dense or sparse
|
||||
*
|
||||
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
||||
* \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
|
||||
*
|
||||
* \implsparsesolverconcept
|
||||
*
|
||||
* \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
|
||||
*/
|
||||
template<typename _MatrixType, int _UpLo>
|
||||
class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> >
|
||||
{
|
||||
public:
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base;
|
||||
typedef typename Base::ColSpMatrix ColSpMatrix;
|
||||
|
||||
public:
|
||||
enum { UpLo = _UpLo };
|
||||
PastixLLT() : Base()
|
||||
{
|
||||
init();
|
||||
}
|
||||
|
||||
explicit PastixLLT(const MatrixType& matrix):Base()
|
||||
{
|
||||
init();
|
||||
compute(matrix);
|
||||
}
|
||||
|
||||
/** Compute the L factor of the LL^T supernodal factorization of \p matrix
|
||||
* \sa analyzePattern() factorize()
|
||||
*/
|
||||
void compute (const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::compute(temp);
|
||||
}
|
||||
|
||||
/** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
|
||||
* The result of this operation can be used with successive matrices having the same pattern as \p matrix
|
||||
* \sa factorize()
|
||||
*/
|
||||
void analyzePattern(const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::analyzePattern(temp);
|
||||
}
|
||||
/** Compute the LL^T supernodal numerical factorization of \p matrix
|
||||
* \sa analyzePattern()
|
||||
*/
|
||||
void factorize(const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::factorize(temp);
|
||||
}
|
||||
protected:
|
||||
using Base::m_iparm;
|
||||
|
||||
void init()
|
||||
{
|
||||
m_iparm(IPARM_SYM) = API_SYM_YES;
|
||||
m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
|
||||
}
|
||||
|
||||
void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
|
||||
{
|
||||
out.resize(matrix.rows(), matrix.cols());
|
||||
// Pastix supports only lower, column-major matrices
|
||||
out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
|
||||
internal::c_to_fortran_numbering(out);
|
||||
}
|
||||
};
|
||||
|
||||
/** \ingroup PaStiXSupport_Module
|
||||
* \class PastixLDLT
|
||||
* \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
|
||||
*
|
||||
* This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
|
||||
* available in the PaStiX library. The matrix A should be symmetric and positive definite
|
||||
* WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
|
||||
* The vectors or matrices X and B can be either dense or sparse
|
||||
*
|
||||
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
||||
* \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
|
||||
*
|
||||
* \implsparsesolverconcept
|
||||
*
|
||||
* \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
|
||||
*/
|
||||
template<typename _MatrixType, int _UpLo>
|
||||
class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> >
|
||||
{
|
||||
public:
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base;
|
||||
typedef typename Base::ColSpMatrix ColSpMatrix;
|
||||
|
||||
public:
|
||||
enum { UpLo = _UpLo };
|
||||
PastixLDLT():Base()
|
||||
{
|
||||
init();
|
||||
}
|
||||
|
||||
explicit PastixLDLT(const MatrixType& matrix):Base()
|
||||
{
|
||||
init();
|
||||
compute(matrix);
|
||||
}
|
||||
|
||||
/** Compute the L and D factors of the LDL^T factorization of \p matrix
|
||||
* \sa analyzePattern() factorize()
|
||||
*/
|
||||
void compute (const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::compute(temp);
|
||||
}
|
||||
|
||||
/** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
|
||||
* The result of this operation can be used with successive matrices having the same pattern as \p matrix
|
||||
* \sa factorize()
|
||||
*/
|
||||
void analyzePattern(const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::analyzePattern(temp);
|
||||
}
|
||||
/** Compute the LDL^T supernodal numerical factorization of \p matrix
|
||||
*
|
||||
*/
|
||||
void factorize(const MatrixType& matrix)
|
||||
{
|
||||
ColSpMatrix temp;
|
||||
grabMatrix(matrix, temp);
|
||||
Base::factorize(temp);
|
||||
}
|
||||
|
||||
protected:
|
||||
using Base::m_iparm;
|
||||
|
||||
void init()
|
||||
{
|
||||
m_iparm(IPARM_SYM) = API_SYM_YES;
|
||||
m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
|
||||
}
|
||||
|
||||
void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
|
||||
{
|
||||
// Pastix supports only lower, column-major matrices
|
||||
out.resize(matrix.rows(), matrix.cols());
|
||||
out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
|
||||
internal::c_to_fortran_numbering(out);
|
||||
}
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif
|
||||
Reference in New Issue
Block a user