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SparseLU: make COLAMDOrdering the default ordering method.

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This commit is contained in:
Gael Guennebaud 2013-07-17 09:30:25 +02:00
parent bd689ccc28
commit bbaef8ebba
2 changed files with 59 additions and 56 deletions
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111
Eigen/src/SparseLU/SparseLU.h
View File

@ -14,9 +14,10 @@
namespace Eigen {
template <typename _MatrixType, typename _OrderingType> class SparseLU;
template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::Index> > class SparseLU;
template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
/** \ingroup SparseLU_Module
* \class SparseLU
*
@ -62,7 +63,7 @@ template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixURetu
* "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
*
* \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<>
* \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS
* \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
*
*
* \sa \ref TutorialSparseDirectSolvers
@ -105,9 +106,9 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
void simplicialfactorize(const MatrixType& matrix);
/**
* Compute the symbolic and numeric factorization of the input sparse matrix.
* The input matrix should be in column-major storage.
*/
* Compute the symbolic and numeric factorization of the input sparse matrix.
* The input matrix should be in column-major storage.
*/
void compute (const MatrixType& matrix)
{
// Analyze
@ -125,38 +126,38 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
}
/** \returns an expression of the matrix L, internally stored as supernodes
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixL().solveInPlace(y);
* \endcode
*/
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixL().solveInPlace(y);
* \endcode
*/
SparseLUMatrixLReturnType<SCMatrix> matrixL() const
{
return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore);
}
/** \returns an expression of the matrix U,
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixU().solveInPlace(y);
* \endcode
*/
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixU().solveInPlace(y);
* \endcode
*/
SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,Index> > matrixU() const
{
return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,Index> >(m_Lstore, m_Ustore);
}
/**
* \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
* \sa colsPermutation()
*/
* \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
* \sa colsPermutation()
*/
inline const PermutationType& rowsPermutation() const
{
return m_perm_r;
}
/**
* \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
* \sa rowsPermutation()
*/
* \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
* \sa rowsPermutation()
*/
inline const PermutationType& colsPermutation() const
{
return m_perm_c;
@ -182,7 +183,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
return internal::solve_retval<SparseLU, Rhs>(*this, B.derived());
}
/** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
/** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
*
* \sa compute()
*/
@ -195,7 +196,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
return internal::sparse_solve_retval<SparseLU, Rhs>(*this, B.derived());
}
/** \brief Reports whether previous computation was successful.
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
@ -208,9 +209,10 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
/**
* \returns A string describing the type of error
*/
* \returns A string describing the type of error
*/
std::string lastErrorMessage() const
{
return m_lastError;
@ -240,6 +242,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
return true;
}
/**
* \returns the absolute value of the determinant of the matrix of which
* *this is the QR decomposition.
@ -249,7 +252,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
* One way to work around that is to use logAbsDeterminant() instead.
*
* \sa logAbsDeterminant(), signDeterminant()
*/
*/
Scalar absDeterminant()
{
eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
@ -276,8 +279,8 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
* of which **this is the QR decomposition
*
* \note This method is useful to work around the risk of overflow/underflow that's
* inherent to the determinant computation
*a
* inherent to the determinant computation.
*
* \sa absDeterminant(), signDeterminant()
*/
Scalar logAbsDeterminant() const
@ -353,15 +356,15 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
// Functions needed by the anaysis phase
/**
* Compute the column permutation to minimize the fill-in
*
* - Apply this permutation to the input matrix -
*
* - Compute the column elimination tree on the permuted matrix
*
* - Postorder the elimination tree and the column permutation
*
*/
* Compute the column permutation to minimize the fill-in
*
* - Apply this permutation to the input matrix -
*
* - Compute the column elimination tree on the permuted matrix
*
* - Postorder the elimination tree and the column permutation
*
*/
template <typename MatrixType, typename OrderingType>
void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
{
@ -428,23 +431,23 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
/**
* - Numerical factorization
* - Interleaved with the symbolic factorization
* On exit, info is
*
* = 0: successful factorization
*
* > 0: if info = i, and i is
*
* <= A->ncol: U(i,i) is exactly zero. The factorization has
* been completed, but the factor U is exactly singular,
* and division by zero will occur if it is used to solve a
* system of equations.
*
* > A->ncol: number of bytes allocated when memory allocation
* failure occurred, plus A->ncol. If lwork = -1, it is
* the estimated amount of space needed, plus A->ncol.
*/
* - Numerical factorization
* - Interleaved with the symbolic factorization
* On exit, info is
*
* = 0: successful factorization
*
* > 0: if info = i, and i is
*
* <= A->ncol: U(i,i) is exactly zero. The factorization has
* been completed, but the factor U is exactly singular,
* and division by zero will occur if it is used to solve a
* system of equations.
*
* > A->ncol: number of bytes allocated when memory allocation
* failure occurred, plus A->ncol. If lwork = -1, it is
* the estimated amount of space needed, plus A->ncol.
*/
template <typename MatrixType, typename OrderingType>
void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
{

4
test/sparselu.cpp
View File

@ -26,7 +26,7 @@
// SparseLU solve does not accept column major matrices for the destination.
// However, as expected, the generic check_sparse_square_solving routines produces row-major
// rhs and destination matrices when compiled with EIGEN_DEFAULT_TO_ROW_MAJOR
//
#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
#undef EIGEN_DEFAULT_TO_ROW_MAJOR
#endif
@ -37,7 +37,7 @@
template<typename T> void test_sparselu_T()
{
SparseLU<SparseMatrix<T, ColMajor>, COLAMDOrdering<int> > sparselu_colamd;
SparseLU<SparseMatrix<T, ColMajor> /*, COLAMDOrdering<int>*/ > sparselu_colamd; // COLAMDOrdering is the default
SparseLU<SparseMatrix<T, ColMajor>, AMDOrdering<int> > sparselu_amd;
SparseLU<SparseMatrix<T, ColMajor, long int>, NaturalOrdering<long int> > sparselu_natural;