devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

bug #877, bug #572: Introduce a global Index typedef. Rename Sparse*::Index to StorageIndex, make Dense*::StorageIndex an alias to DenseIndex. Overall this commit gets rid of all Index conversion warnings.

Browse Source
This commit is contained in:
Christoph Hertzberg
2014-12-04 22:48:53 +01:00
parent 6ccf97f3e6
commit e8cdbedefb
95 changed files with 1101 additions and 1111 deletions
Download Patch File Download Diff File Expand all files Collapse all files

55
Eigen/src/SparseLU/SparseLU.h
View File

@@ -14,7 +14,7 @@
namespace Eigen {
template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::Index> > class SparseLU;
template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::StorageIndex> > class SparseLU;
template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
@@ -70,7 +70,7 @@ template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixURetu
* \sa \ref OrderingMethods_Module
*/
template <typename _MatrixType, typename _OrderingType>
class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >, public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::Index>
class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >, public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::StorageIndex>
{
protected:
typedef SparseSolverBase<SparseLU<_MatrixType,_OrderingType> > APIBase;
@@ -82,13 +82,13 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
typedef _OrderingType OrderingType;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef SparseMatrix<Scalar,ColMajor,Index> NCMatrix;
typedef internal::MappedSuperNodalMatrix<Scalar, Index> SCMatrix;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef SparseMatrix<Scalar,ColMajor,StorageIndex> NCMatrix;
typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix;
typedef Matrix<Scalar,Dynamic,1> ScalarVector;
typedef Matrix<Index,Dynamic,1> IndexVector;
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
typedef internal::SparseLUImpl<Scalar, Index> Base;
typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
typedef internal::SparseLUImpl<Scalar, StorageIndex> Base;
public:
SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
@@ -122,8 +122,8 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
factorize(matrix);
}
inline Index rows() const { return m_mat.rows(); }
inline Index cols() const { return m_mat.cols(); }
inline StorageIndex rows() const { return m_mat.rows(); }
inline StorageIndex cols() const { return m_mat.cols(); }
/** Indicate that the pattern of the input matrix is symmetric */
void isSymmetric(bool sym)
{
@@ -146,9 +146,9 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
* y = b; matrixU().solveInPlace(y);
* \endcode
*/
SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,Index> > matrixU() const
SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,StorageIndex> > matrixU() const
{
return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,Index> >(m_Lstore, m_Ustore);
return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,StorageIndex> >(m_Lstore, m_Ustore);
}
/**
@@ -324,7 +324,7 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
std::string m_lastError;
NCMatrix m_mat; // The input (permuted ) matrix
SCMatrix m_Lstore; // The lower triangular matrix (supernodal)
MappedSparseMatrix<Scalar,ColMajor,Index> m_Ustore; // The upper triangular matrix
MappedSparseMatrix<Scalar,ColMajor,StorageIndex> m_Ustore; // The upper triangular matrix
PermutationType m_perm_c; // Column permutation
PermutationType m_perm_r ; // Row permutation
IndexVector m_etree; // Column elimination tree
@@ -334,10 +334,10 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
// SparseLU options
bool m_symmetricmode;
// values for performance
internal::perfvalues<Index> m_perfv;
internal::perfvalues<StorageIndex> m_perfv;
RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot
Index m_nnzL, m_nnzU; // Nonzeros in L and U factors
Index m_detPermR; // Determinant of the coefficient matrix
StorageIndex m_nnzL, m_nnzU; // Nonzeros in L and U factors
StorageIndex m_detPermR; // Determinant of the coefficient matrix
private:
// Disable copy constructor
SparseLU (const SparseLU& );
@@ -375,7 +375,7 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
{
m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.
// Then, permute only the column pointers
ei_declare_aligned_stack_constructed_variable(Index,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<Index*>(mat.outerIndexPtr()):0);
ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0);
// If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is thus needed.
if(!mat.isCompressed())
@@ -640,7 +640,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
// Create supernode matrix L
m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup);
// Create the column major upper sparse matrix U;
new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, Index> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, StorageIndex> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
m_info = Success;
m_factorizationIsOk = true;
@@ -649,12 +649,12 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
template<typename MappedSupernodalType>
struct SparseLUMatrixLReturnType : internal::no_assignment_operator
{
typedef typename MappedSupernodalType::Index Index;
typedef typename MappedSupernodalType::StorageIndex StorageIndex;
typedef typename MappedSupernodalType::Scalar Scalar;
explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL)
{ }
Index rows() { return m_mapL.rows(); }
Index cols() { return m_mapL.cols(); }
StorageIndex rows() { return m_mapL.rows(); }
StorageIndex cols() { return m_mapL.cols(); }
template<typename Dest>
void solveInPlace( MatrixBase<Dest> &X) const
{
@@ -666,21 +666,18 @@ struct SparseLUMatrixLReturnType : internal::no_assignment_operator
template<typename MatrixLType, typename MatrixUType>
struct SparseLUMatrixUReturnType : internal::no_assignment_operator
{
typedef typename MatrixLType::Index Index;
typedef typename MatrixLType::StorageIndex StorageIndex;
typedef typename MatrixLType::Scalar Scalar;
explicit SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU)
: m_mapL(mapL),m_mapU(mapU)
{ }
Index rows() { return m_mapL.rows(); }
Index cols() { return m_mapL.cols(); }
StorageIndex rows() { return m_mapL.rows(); }
StorageIndex cols() { return m_mapL.cols(); }
template<typename Dest> void solveInPlace(MatrixBase<Dest> &X) const
{
/* Explicit type conversion as the Index type of MatrixBase<Dest> may be wider than Index */
eigen_assert(X.rows() <= NumTraits<Index>::highest());
eigen_assert(X.cols() <= NumTraits<Index>::highest());
Index nrhs = Index(X.cols());
Index n = Index(X.rows());
Index nrhs = X.cols();
Index n = X.rows();
// Backward solve with U
for (Index k = m_mapL.nsuper(); k >= 0; k--)
{