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add a "using MKL" documentation page, add a minimal documentation of PARDISO wrapper classes, refine a bit the EIGEN_USE_* logic

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This commit is contained in:
Gael Guennebaud
2011-12-09 16:52:37 +01:00
parent fff25a4b46
commit 37f304a2e6
10 changed files with 219 additions and 25 deletions
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36
Eigen/src/PARDISOSupport/PARDISOSupport.h
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@@ -312,6 +312,18 @@ bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b,
}
/** \ingroup PARDISOSupport_Module
* \class PardisoLU
* \brief A sparse direct LU factorization and solver based on the PARDISO library
*
* This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
* using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
* 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<>
*
* \sa \ref TutorialSparseDirectSolvers
*/
template<typename MatrixType>
class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
{
@@ -340,6 +352,18 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
}
};
/** \ingroup PARDISOSupport_Module
* \class PardisoLLT
* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
*
* This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
* 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<>
*
* \sa \ref TutorialSparseDirectSolvers
*/
template<typename MatrixType>
class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType> >
{
@@ -368,6 +392,18 @@ class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType> >
}
};
/** \ingroup PARDISOSupport_Module
* \class PardisoLDLT
* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
*
* This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
* 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<>
*
* \sa \ref TutorialSparseDirectSolvers
*/
template<typename MatrixType>
class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType> >
{