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modify the unit tests of sparse linear solvers to enable tests on real matrices, from MatrixMarket for instance

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Desire NUENTSA
2012-03-29 14:32:54 +02:00
parent ada9e79145
commit f804a319c8
14 changed files with 177 additions and 71 deletions
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16
Eigen/src/PaStiXSupport/PaStiXSupport.h
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@@ -136,10 +136,10 @@ namespace internal
// WARNING It is assumed here that successive calls to this routine are done
// with matrices having the same pattern.
template <typename MatrixType>
void EigenSymmetrizeMatrixGraph (const MatrixType& In, MatrixType& Out, MatrixType& StrMatTrans)
void EigenSymmetrizeMatrixGraph (const MatrixType& In, MatrixType& Out, MatrixType& StrMatTrans, bool& hasTranspose)
{
eigen_assert(In.cols()==In.rows() && " Can only symmetrize the graph of a square matrix");
if (StrMatTrans.outerSize() == 0)
if (!hasTranspose)
{ //First call to this routine, need to compute the structural pattern of In^T
StrMatTrans = In.transpose();
// Set the elements of the matrix to zero
@@ -148,6 +148,7 @@ namespace internal
for (typename MatrixType::InnerIterator it(StrMatTrans, i); it; ++it)
it.valueRef() = 0.0;
}
hasTranspose = true;
}
Out = (StrMatTrans + In).eval();
}
@@ -172,6 +173,7 @@ class PastixBase
PastixBase():m_initisOk(false),m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false)
{
m_pastixdata = 0;
m_hasTranspose = false;
PastixInit();
}
@@ -314,7 +316,6 @@ class PastixBase
m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, m_mat_null.outerIndexPtr(), m_mat_null.innerIndexPtr(),
m_mat_null.valuePtr(), m_perm.data(), m_invp.data(), m_vec_null.data(), 1, m_iparm.data(), m_dparm.data());
}
Derived& compute (MatrixType& mat);
@@ -328,6 +329,7 @@ class PastixBase
mutable SparseMatrix<Scalar, ColMajor> m_mat_null; // An input null matrix
mutable Matrix<Scalar, Dynamic,1> m_vec_null; // An input null vector
mutable SparseMatrix<Scalar, ColMajor> m_StrMatTrans; // The transpose pattern of the input matrix
mutable bool m_hasTranspose; // The transpose of the current matrix has already been computed
mutable int m_comm; // The MPI communicator identifier
mutable Matrix<Index,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
mutable Matrix<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
@@ -357,6 +359,7 @@ void PastixBase<Derived>::PastixInit()
PastixDestroy();
m_pastixdata = 0;
m_iparm(IPARM_MODIFY_PARAMETER) = API_YES;
m_hasTranspose = false;
}
m_iparm(IPARM_START_TASK) = API_TASK_INIT;
@@ -537,7 +540,7 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> >
temp = matrix;
else
{
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans);
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans, m_hasTranspose);
}
m_iparm[IPARM_SYM] = API_SYM_NO;
m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
@@ -559,7 +562,7 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> >
temp = matrix;
else
{
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans);
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans,m_hasTranspose);
}
m_iparm(IPARM_SYM) = API_SYM_NO;
@@ -581,7 +584,7 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> >
temp = matrix;
else
{
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans);
internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans,m_hasTranspose);
}
m_iparm(IPARM_SYM) = API_SYM_NO;
m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
@@ -591,6 +594,7 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> >
using Base::m_iparm;
using Base::m_dparm;
using Base::m_StrMatTrans;
using Base::m_hasTranspose;
};
/** \ingroup PaStiXSupport_Module

8
Eigen/src/UmfPackSupport/UmfPackSupport.h
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@@ -124,9 +124,11 @@ inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *N
* \brief A sparse LU factorization and solver based on UmfPack
*
* This class allows to solve for A.X = B sparse linear problems via a LU factorization
* using the UmfPack library. The sparse matrix A must be column-major, squared and full rank.
* using the UmfPack library. The sparse matrix A must be in a compressed column-major form, squared and full rank.
* The vectors or matrices X and B can be either dense or sparse.
*
* WARNING The Eigen column-major SparseMatrix is not always in compressed form.
* The user should call makeCompressed() to get a matrix in CSC suitable for UMFPACK
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
*
* \sa \ref TutorialSparseDirectSolvers
@@ -198,7 +200,9 @@ class UmfPackLU
return m_q;
}
/** Computes the sparse Cholesky decomposition of \a matrix */
/** Computes the sparse Cholesky decomposition of \a matrix
* Note that the matrix should be in compressed format. Please, use makeCompressed() to get it !!
*/
void compute(const MatrixType& matrix)
{
analyzePattern(matrix);