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Desire NUENTSA 2012-06-13 18:26:05 +02:00
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2
Eigen/OrderingMethods
View File

@ -17,7 +17,7 @@
*/
#include "src/OrderingMethods/Amd.h"
#include "src/OrderingMethods/Ordering.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ORDERINGMETHODS_MODULE_H

17
Eigen/SparseLU Normal file
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@ -0,0 +1,17 @@
#ifndef EIGEN_SPARSELU_MODULE_H
#define EIGEN_SPARSELU_MODULE_H
#include "SparseCore"
/** \ingroup Sparse_modules
* \defgroup SparseLU_Module SparseLU module
*
*/
// Ordering interface
#include "OrderingMethods"
#include "src/SparseLU/SparseLU.h"
#endif // EIGEN_SPARSELU_MODULE_H

166
Eigen/src/OrderingMethods/Ordering.h
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@ -26,9 +26,7 @@
#ifndef EIGEN_ORDERING_H
#define EIGEN_ORDERING_H
#include <Eigen_Colamd.h>
#include <Amd.h>
#include "Amd.h"
namespace Eigen {
template<class Derived>
class OrderingBase
@ -68,8 +66,23 @@ class OrderingBase
if (m_isInitialized = true) return m_P;
else abort(); // FIXME Should find a smoother way to exit with error code
}
/**
* Get the symmetric pattern A^T+A from the input matrix A.
* FIXME: The values should not be considered here
*/
template<typename MatrixType>
void at_plus_a(const MatrixType& mat);
void at_plus_a(const MatrixType& mat)
{
MatrixType C;
C = mat.transpose(); // NOTE: Could be costly
for (int i = 0; i < C.rows(); i++)
{
for (typename MatrixType::InnerIterator it(C, i); it; ++it)
it.valueRef() = 0.0;
}
m_mat = C + mat;
}
/** keeps off-diagonal entries; drops diagonal entries */
struct keep_diag {
@ -87,99 +100,30 @@ class OrderingBase
PermutationType m_P; // The computed permutation
mutable bool m_isInitialized;
SparseMatrix<Scalar,ColMajor,Index> m_mat; // Stores the (symmetrized) matrix to permute
}
/**
* Get the symmetric pattern A^T+A from the input matrix A.
* NOTE: The values should not be considered here
*/
template<typename MatrixType>
void OrderingBase::at_plus_a(const MatrixType& mat)
{
MatrixType C;
C = mat.transpose(); // NOTE: Could be costly
for (int i = 0; i < C.rows(); i++)
{
for (typename MatrixType::InnerIterator it(C, i); it; ++it)
it.valueRef() = 0.0;
}
m_mat = C + mat;
/**
* Get the column approximate minimum degree ordering
* The matrix should be in column-major format
*/
template<typename Scalar, typename Index>
class COLAMDOrdering: public OrderingBase< ColamdOrdering<Scalar, Index> >
{
public:
typedef OrderingBase< ColamdOrdering<Scalar, Index> > Base;
typedef SparseMatrix<Scalar,ColMajor,Index> MatrixType;
public:
COLAMDOrdering():Base() {}
COLAMDOrdering(const MatrixType& matrix):Base()
{
compute(matrix);
}
COLAMDOrdering(const MatrixType& mat, PermutationType& perm_c):Base()
{
compute(matrix);
perm_c = this.get_perm();
}
void compute(const MatrixType& mat)
{
// Test if the matrix is column major...
int m = mat.rows();
int n = mat.cols();
int nnz = mat.nonZeros();
// Get the recommended value of Alen to be used by colamd
int Alen = colamd_recommended(nnz, m, n);
// Set the default parameters
double knobs[COLAMD_KNOBS];
colamd_set_defaults(knobs);
int info;
VectorXi p(n), A(nnz);
for(int i=0; i < n; i++) p(i) = mat.outerIndexPtr()(i);
for(int i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()(i);
// Call Colamd routine to compute the ordering
info = colamd(m, n, Alen, A,p , knobs, stats)
eigen_assert( (info != FALSE)&& "COLAMD failed " );
m_P.resize(n);
for (int i = 0; i < n; i++) m_P(p(i)) = i;
m_isInitialized = true;
}
protected:
using Base::m_isInitialized;
using Base m_P;
}
};
/**
* Get the approximate minimum degree ordering
* If the matrix is not structurally symmetric, an ordering of A^T+A is computed
* \tparam Scalar The type of the scalar of the matrix for which the ordering is applied
* \tparam Index The type of indices of the matrix
* \tparam _UpLo If the matrix is symmetric, indicates which part to use
*/
template <typename Scalar, typename Index, typename _UpLo>
class AMDordering : public OrderingBase<AMDOrdering<Scalar, Index> >
template <typename Scalar, typename Index>
class AMDOrdering : public OrderingBase<AMDOrdering<Scalar, Index> >
{
public:
enum { UpLo = _UpLo };
typedef OrderingBase< AMDOrdering<Scalar, Index> > Base;
typedef SparseMatrix<Scalar, ColMajor,Index> MatrixType;
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
public:
AMDOrdering():Base(){}
AMDOrdering(const MatrixType& mat):Base()
{
compute(matrix);
compute(mat);
}
AMDOrdering(const MatrixType& mat, PermutationType& perm_c):Base()
{
compute(matrix);
compute(mat);
perm_c = this.get_perm();
}
/** Compute the permutation vector from a column-major sparse matrix */
@ -200,15 +144,75 @@ class AMDordering : public OrderingBase<AMDOrdering<Scalar, Index> >
m_mat = mat;
// Call the AMD routine
m_mat.prune(keep_diag());
m_mat.prune(keep_diag()); //Remove the diagonal elements
internal::minimum_degree_ordering(m_mat, m_P);
if (m_P.size()>0) m_isInitialized = true;
}
protected:
struct keep_diag{
inline bool operator() (const Index& row, const Index& col, const Scalar&) const
{
return row!=col;
}
};
using Base::m_isInitialized;
using Base::m_P;
using Base::m_mat;
}
};
/**
* Get the column approximate minimum degree ordering
* The matrix should be in column-major format
*/
// template<typename Scalar, typename Index>
// class COLAMDOrdering: public OrderingBase< ColamdOrdering<Scalar, Index> >
// {
// public:
// typedef OrderingBase< ColamdOrdering<Scalar, Index> > Base;
// typedef SparseMatrix<Scalar,ColMajor,Index> MatrixType;
//
// public:
// COLAMDOrdering():Base() {}
//
// COLAMDOrdering(const MatrixType& matrix):Base()
// {
// compute(matrix);
// }
// COLAMDOrdering(const MatrixType& mat, PermutationType& perm_c):Base()
// {
// compute(matrix);
// perm_c = this.get_perm();
// }
// void compute(const MatrixType& mat)
// {
// // Test if the matrix is column major...
//
// int m = mat.rows();
// int n = mat.cols();
// int nnz = mat.nonZeros();
// // Get the recommended value of Alen to be used by colamd
// int Alen = colamd_recommended(nnz, m, n);
// // Set the default parameters
// double knobs[COLAMD_KNOBS];
// colamd_set_defaults(knobs);
//
// int info;
// VectorXi p(n), A(nnz);
// for(int i=0; i < n; i++) p(i) = mat.outerIndexPtr()(i);
// for(int i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()(i);
// // Call Colamd routine to compute the ordering
// info = colamd(m, n, Alen, A,p , knobs, stats)
// eigen_assert( (info != FALSE)&& "COLAMD failed " );
//
// m_P.resize(n);
// for (int i = 0; i < n; i++) m_P(p(i)) = i;
// m_isInitialized = true;
// }
// protected:
// using Base::m_isInitialized;
// using Base m_P;
// };
} // end namespace Eigen
#endif

6
Eigen/src/SparseLU/CMakeLists.txt Normal file
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@ -0,0 +1,6 @@
FILE(GLOB Eigen_SparseLU_SRCS "*.h")
INSTALL(FILES
${Eigen_SparseLU_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseLU COMPONENT Devel
)

429
Eigen/src/SparseLU/SparseLU.h
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@ -30,8 +30,8 @@ namespace Eigen {
// Data structure needed by all routines
#include <SparseLU_Structs.h>
#include <SparseLU_Matrix.h>
#include "SparseLU_Structs.h"
#include "SparseLU_Matrix.h"
/**
* \ingroup SparseLU_Module
@ -41,18 +41,20 @@ namespace Eigen {
*
* \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<>
*/
template <typename _MatrixType>
template <typename _MatrixType, typename _OrderingType>
class SparseLU
{
public:
typedef _MatrixType MatrixType;
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 SuperNodalMatrix<Scalar, Index> SCMatrix;
typedef GlobalLU_t<Scalar, Index> LU_GlobalLU_t;
typedef Matrix<Scalar,Dynamic,1> ScalarVector;
typedef Matrix<Index,Dynamic,1> IndexVector;
// typedef GlobalLU_t<ScalarVector, IndexVector> LU_GlobalLU_t;
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
public:
SparseLU():m_isInitialized(true),m_symmetricmode(false),m_diagpivotthresh(1.0)
@ -83,9 +85,9 @@ class SparseLU
//Factorize
factorize(matrix);
}
template<typename Rhs, typename Dest>
bool SparseLU::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
inline Index rows() const { return m_mat.rows(); }
inline Index cols() const { return m_mat.cols(); }
/** Indicate that the pattern of the input matrix is symmetric */
void isSymmetric(bool sym)
{
@ -99,44 +101,151 @@ class SparseLU
}
/** \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()
*/
template<typename Rhs>
inline const internal::solve_retval<SparseLU, Rhs> solve(const MatrixBase<Rhs>& b) const
// template<typename Rhs>
// inline const solve_retval<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const
// {
// eigen_assert(m_factorizationIsOk && "SparseLU is not initialized.");
// eigen_assert(rows()==B.rows()
// && "SparseLU::solve(): invalid number of rows of the right hand side matrix B");
// return solve_retval<SparseLU, Rhs>(*this, B.derived());
// }
template<typename Rhs, typename Dest>
bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X) const
{
eigen_assert(m_factorizationIsOk && "SparseLU is not initialized.");
eigen_assert(rows()==b.rows()
&& "SparseLU::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<SuperLUBase, Rhs>(*this, b.derived());
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);
X = B; /* on return, X is overwritten by the computed solution */
int nrhs = B.cols();
// Permute the right hand side to form Pr*B
X = m_perm_r * X;
// Forward solve PLy = Pb;
Index n = B.rows();
Index fsupc; // First column of the current supernode
Index istart; // Pointer index to the subscript of the current column
Index nsupr; // Number of rows in the current supernode
Index nsupc; // Number of columns in the current supernode
Index nrow; // Number of rows in the non-diagonal part of the supernode
Index luptr; // Pointer index to the current nonzero value
Index iptr; // row index pointer iterator
Index irow; //Current index row
const Scalar * Lval = m_Lstore.valuePtr(); // Nonzero values
Matrix<Scalar,Dynamic,Dynamic> work(n, nrhs); // working vector
work.setZero();
int j, k, i, icol,jcol;
for (k = 0; k <= m_Lstore.nsuper(); k ++)
{
fsupc = m_Lstore.supToCol()[k];
istart = m_Lstore.rowIndexPtr()[fsupc];
nsupr = m_Lstore.rowIndexPtr()[fsupc+1] - istart;
nsupc = m_Lstore.supToCol()[k+1] - fsupc;
nrow = nsupr - nsupc;
luptr = m_Lstore.colIndexPtr()[fsupc];
if (nsupc == 1 )
{
for (j = 0; j < nrhs; j++)
{
for (iptr = istart+1; iptr < m_Lstore.rowIndexPtr()[fsupc+1]; iptr++)
{
irow = m_Lstore.rowIndex()[iptr];
++luptr;
X(irow, j) -= X(fsupc, j) * Lval[luptr];
}
}
}
else
{
// The supernode has more than one column
// Triangular solve
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
// Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride > u( &(X(fsupc,0)), nsupc, nrhs, OuterStride<>(X.rows()) );
Matrix<Scalar,Dynamic,Dynamic>& U = X.block(fsupc, 0, nsupc, nrhs); //FIXME Check this
U = A.template triangularView<Lower>().solve(U);
// Matrix-vector product
new (&A) Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(nsupr) );
work.block(0, 0, nrow, nrhs) = A * U;
//Begin Scatter
for (j = 0; j < nrhs; j++)
{
iptr = istart + nsupc;
for (i = 0; i < nrow; i++)
{
irow = m_Lstore.rowIndex()[iptr];
X(irow, j) -= work(i, j); // Scatter operation
work(i, j) = Scalar(0);
iptr++;
}
}
}
} // end for all supernodes
// Back solve Ux = y
for (k = m_Lstore.nsuper(); k >= 0; k--)
{
fsupc = m_Lstore.supToCol()[k];
istart = m_Lstore.rowIndexPtr()[fsupc];
nsupr = m_Lstore.rowIndexPtr()[fsupc+1] - istart;
nsupc = m_Lstore.supToCol()[k+1] - fsupc;
luptr = m_Lstore.colIndexPtr()[fsupc];
if (nsupc == 1)
{
for (j = 0; j < nrhs; j++)
{
X(fsupc, j) /= Lval[luptr];
}
}
else
{
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
Matrix<Scalar,Dynamic,Dynamic>& U = X.block(fsupc, 0, nsupc, nrhs);
U = A.template triangularView<Upper>().solve(U);
}
for (j = 0; j < nrhs; ++j)
{
for (jcol = fsupc; jcol < fsupc + nsupc; jcol++)
{
for (i = m_Ustore.outerIndexPtr()[jcol]; i < m_Ustore.outerIndexPtr()[jcol]; i++)
{
irow = m_Ustore.InnerIndices()[i];
X(irow, j) -= X(jcol, j) * m_Ustore.Values()[i];
}
}
}
} // End For U-solve
// Permute back the solution
X = m_perm_c * X;
return true;
}
protected:
// Functions
void initperfvalues();
int LU_snode_dfs(const int jcol, const int kcol, const IndexVector* asub,
const IndexVector* colptr, IndexVector& xprune, IndexVector& marker, LU_GlobalLU_t& glu);
int LU_dsnode_bmod (const Index jcol, const Index jsupno, const Index fsupc,
ScalarVector& dense, LU_GlobalLU_t& Glu);
int LU_pivotL(const int jcol, const RealScalar diagpivotthresh, IndexVector& perm_r,
IndexVector& iperm_c, int& pivrow, GlobalLU_t& Glu);
void LU_panel_dfs(const int m, const int w, const int jcol, MatrixType& A,
IndexVector& perm_r, int& nseg, ScalarVector& dense, IndexVector& panel_lsub,
IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker,
IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t& Glu);
void LU_panel_bmod(const int m, const int w, const int jcol, const int nseg,
ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep,
IndexVector& repfnz, LU_GlobalLU_t& glu);
int LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, IndexVector& nseg,
IndexVector& lsub_col, IndexVector& segrep, IndexVector& repfnz,
IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t& glu);
int LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense, ScalarVector& tempv,
IndexVector& segrep, IndexVector& repfnz, int fpanelc, LU_GlobalLU_t& Glu);
int LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segrep, IndexVector& repfnz,
IndexVector& perm_r, ScalarVector& dense, LU_GlobalLU_t& glu);
void LU_pruneL(const int jcol, const IndexVector& perm_r, const int pivrow, const int nseg,
const IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, GlobalLU_t& Glu)
void initperfvalues()
{
m_panel_size = 12;
m_relax = 1;
m_maxsuper = 100;
m_rowblk = 200;
m_colblk = 60;
m_fillfactor = 20;
}
// Variables
mutable ComputationInfo m_info;
@ -150,9 +259,7 @@ class SparseLU
PermutationType m_perm_r ; // Row permutation
IndexVector m_etree; // Column elimination tree
ScalarVector m_work; // Scalar work vector
IndexVector m_iwork; //Index work vector
static LU_GlobalLU_t m_glu; // persistent data to facilitate multiple factors
static LU_GlobalLU_t<IndexVector, ScalarVector> m_glu; // persistent data to facilitate multiple factors
// FIXME All fields of this struct can be defined separately as class members
// SuperLU/SparseLU options
@ -176,21 +283,9 @@ class SparseLU
}; // End class SparseLU
/* Set the default values for performance */
void SparseLU::initperfvalues()
{
m_panel_size = 12;
m_relax = 1;
m_maxsuper = 100;
m_rowblk = 200;
m_colblk = 60;
m_fillfactor = 20;
}
// Functions needed by the anaysis phase
#include <SparseLU_Coletree.h>
// Ordering interface
#include <Ordering.h>
#include "SparseLU_Coletree.h"
/**
* Compute the column permutation to minimize the fill-in (file amd.c )
*
@ -202,7 +297,7 @@ void SparseLU::initperfvalues()
*
*/
template <typename MatrixType, typename OrderingType>
void SparseLU::analyzePattern(const MatrixType& mat)
void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
{
//TODO It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
@ -219,6 +314,7 @@ void SparseLU::analyzePattern(const MatrixType& mat)
// Apply the permutation to the column of the input matrix
m_mat = mat * m_perm_c;
// Compute the column elimination tree of the permuted matrix
if (m_etree.size() == 0) m_etree.resize(m_mat.cols());
LU_sp_coletree(m_mat, m_etree);
@ -230,8 +326,9 @@ void SparseLU::analyzePattern(const MatrixType& mat)
LU_TreePostorder(m_mat.cols(), m_etree, post);
// Renumber etree in postorder
iwork.resize(n+1);
for (i = 0; i < n; ++i) iwork(post(i)) = post(m_etree(i));
int m = m_mat.cols();
iwork.resize(m+1);
for (int i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i));
m_etree = iwork;
// Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree
@ -242,23 +339,23 @@ void SparseLU::analyzePattern(const MatrixType& mat)
m_perm_c = m_perm_c * post_perm;
} // end postordering
m_analysisIsok = true;
m_analysisIsOk = true;
}
// Functions needed by the numerical factorization phase
#include <SparseLU_Memory.h>
#include <SparseLU_heap_relax_snode.h>
#include <SparseLU_relax_snode.h>
#include <SparseLU_snode_dfs.h>
#include <SparseLU_snode_bmod.h>
#include <SparseLU_pivotL.h>
#include <SparseLU_panel_dfs.h>
#include <SparseLU_panel_bmod.h>
#include <SparseLU_column_dfs.h>
#include <SparseLU_column_bmod.h>
#include <SparseLU_copy_to_ucol.h>
#include <SparseLU_pruneL.h>
#include <SparseLU_Utils.h>
#include "SparseLU_Memory.h"
#include "SparseLU_heap_relax_snode.h"
#include "SparseLU_relax_snode.h"
#include "SparseLU_snode_dfs.h"
#include "SparseLU_snode_bmod.h"
#include "SparseLU_pivotL.h"
#include "SparseLU_panel_dfs.h"
#include "SparseLU_panel_bmod.h"
#include "SparseLU_column_dfs.h"
#include "SparseLU_column_bmod.h"
#include "SparseLU_copy_to_ucol.h"
#include "SparseLU_pruneL.h"
#include "SparseLU_Utils.h"
/**
* - Numerical factorization
@ -276,13 +373,17 @@ void SparseLU::analyzePattern(const MatrixType& mat)
* failure occurred, plus A->ncol. If lwork = -1, it is
* the estimated amount of space needed, plus A->ncol.
*/
template <typename MatrixType>
void SparseLU::factorize(const MatrixType& matrix)
template <typename MatrixType, typename OrderingType>
void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
{
eigen_assert(m_analysisIsok && "analyzePattern() should be called first");
eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
ScalarVector work; // Scalar work vector
IndexVector iwork; //Index work vector
// Apply the column permutation computed in analyzepattern()
m_mat = matrix * m_perm_c;
m_mat.makeCompressed();
@ -293,7 +394,7 @@ void SparseLU::factorize(const MatrixType& matrix)
int maxpanel = m_panel_size * m;
// Allocate storage common to the factor routines
int lwork = 0;
int info = LUMemInit(m, n, nnz, m_work, m_iwork, lwork, m_fillratio, m_panel_size, m_maxsuper, m_rowblk, m_glu);
int info = LUMemInit(m, n, nnz, work, iwork, lwork, m_fillfactor, m_panel_size, m_maxsuper, m_rowblk, m_glu);
if (info)
{
std::cerr << "UNABLE TO ALLOCATE WORKING MEMORY\n\n" ;
@ -304,27 +405,27 @@ void SparseLU::factorize(const MatrixType& matrix)
// Set up pointers for integer working arrays
int idx = 0;
VectorBlock<IndexVector> segrep(m_iwork, idx, m);
VectorBlock<IndexVector> segrep(iwork, idx, m);
idx += m;
VectorBlock<IndexVector> parent(m_iwork, idx, m);
VectorBlock<IndexVector> parent(iwork, idx, m);
idx += m;
VectorBlock<IndexVector> xplore(m_iwork, idx, m);
VectorBlock<IndexVector> xplore(iwork, idx, m);
idx += m;
VectorBlock<IndexVector> repfnz(m_iwork, idx, maxpanel);
VectorBlock<IndexVector> repfnz(iwork, idx, maxpanel);
idx += maxpanel;
VectorBlock<IndexVector> panel_lsub(m_iwork, idx, maxpanel)
VectorBlock<IndexVector> panel_lsub(iwork, idx, maxpanel);
idx += maxpanel;
VectorBlock<IndexVector> xprune(m_iwork, idx, n);
VectorBlock<IndexVector> xprune(iwork, idx, n);
idx += n;
VectorBlock<IndexVector> marker(m_iwork, idx, m * LU_NO_MARKER);
VectorBlock<IndexVector> marker(iwork, idx, m * LU_NO_MARKER);
repfnz.setConstant(-1);
panel_lsub.setConstant(-1);
// Set up pointers for scalar working arrays
VectorBlock<ScalarVector> dense(m_work, 0, maxpanel);
VectorBlock<ScalarVector> dense(work, 0, maxpanel);
dense.setZero();
VectorBlock<ScalarVector> tempv(m_work, maxpanel, LU_NUM_TEMPV(m, m_panel_size, m_maxsuper, m_rowblk) );
VectorBlock<ScalarVector> tempv(work, maxpanel, LU_NUM_TEMPV(m, m_panel_size, m_maxsuper, m_rowblk) );
tempv.setZero();
// Setup Permutation vectors
@ -334,9 +435,9 @@ void SparseLU::factorize(const MatrixType& matrix)
// Identify initial relaxed snodes
IndexVector relax_end(n);
if ( m_symmetricmode = true )
internal::LU_heap_relax_snode(n, m_etree, m_relax, marker, relax_end);
LU_heap_relax_snode(n, m_etree, m_relax, marker, relax_end);
else
internal::LU_relax_snode(n, m_etree, m_relax, marker, relax_end);
LU_relax_snode(n, m_etree, m_relax, marker, relax_end);
m_perm_r.setConstant(-1);
marker.setConstant(-1);
@ -346,6 +447,7 @@ void SparseLU::factorize(const MatrixType& matrix)
IndexVector& xlsub = m_glu.xlsub;
IndexVector& xlusup = m_glu.xlusup;
IndexVector& xusub = m_glu.xusub;
ScalarVector& lusup = m_glu.lusup;
Index& nzlumax = m_glu.nzlumax;
supno(0) = IND_EMPTY;
@ -360,7 +462,8 @@ void SparseLU::factorize(const MatrixType& matrix)
Index pivrow; // Pivotal row number in the original row matrix
int nseg1; // Number of segments in U-column above panel row jcol
int nseg; // Number of segments in each U-column
int irep,ir;
int irep,ir, icol;
int i, k, jj,j;
for (jcol = 0; jcol < n; )
{
if (relax_end(jcol) != IND_EMPTY)
@ -382,9 +485,10 @@ void SparseLU::factorize(const MatrixType& matrix)
jsupno = supno(jcol); // Supernode number which column jcol belongs to
fsupc = xsup(jsupno); //First column number of the current supernode
new_next = nextlu + (xlsub(fsupc+1)-xlsub(fsupc)) * (kcol - jcol + 1);
int mem;
while (new_next > nzlumax )
{
mem = LUMemXpand<Scalar>(lusup, nzlumax, nextlu, LUSUP, m_glu);
mem = LUMemXpand<Scalar>(lusup, nzlumax, nextlu, LUSUP, m_glu.num_expansions);
if (mem)
{
std::cerr << "MEMORY ALLOCATION FAILED FOR L FACTOR \n";
@ -401,10 +505,10 @@ void SparseLU::factorize(const MatrixType& matrix)
dense(it.row()) = it.val();
// Numeric update within the snode
LU_snode_bmod(icol, jsupno, fsupc, dense, glu);
LU_snode_bmod(icol, jsupno, fsupc, dense, m_glu);
// Eliminate the current column
info = LU_pivotL(icol, m_diagpivotthresh, m_perm_r, m_iperm_c, pivrow, m_glu);
info = LU_pivotL(icol, m_diagpivotthresh, m_perm_r, iperm_c, pivrow, m_glu);
if ( info )
{
m_info = NumericalIssue;
@ -419,7 +523,7 @@ void SparseLU::factorize(const MatrixType& matrix)
{ // Work on one panel of panel_size columns
// Adjust panel size so that a panel won't overlap with the next relaxed snode.
int panel_size = wdef; // upper bound on panel width
int panel_size = m_panel_size; // upper bound on panel width
for (k = jcol + 1; k < std::min(jcol+panel_size, n); k++)
{
if (relax_end(k) != IND_EMPTY)
@ -438,7 +542,7 @@ void SparseLU::factorize(const MatrixType& matrix)
LU_panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu);
// Sparse LU within the panel, and below the panel diagonal
for ( jj = jcol, j< jcol + panel_size; jj++)
for ( jj = jcol; j< jcol + panel_size; jj++)
{
k = (jj - jcol) * m; // Column index for w-wide arrays
@ -446,7 +550,7 @@ void SparseLU::factorize(const MatrixType& matrix)
//Depth-first-search for the current column
VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m);
VectorBlock<IndexVector> repfnz_k(repfnz, k, m);
info = LU_column_dfs(m, jj, perm_r, nseg, panel_lsub(k), segrep, repfnz_k, xprune, marker, parent, xplore, m_glu);
info = LU_column_dfs(m, jj, m_perm_r, m_maxsuper, nseg, panel_lsub(k), segrep, repfnz_k, xprune, marker, parent, xplore, m_glu);
if ( !info )
{
std::cerr << "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() \n";
@ -467,7 +571,7 @@ void SparseLU::factorize(const MatrixType& matrix)
}
// Copy the U-segments to ucol(*)
info = LU_copy_to_col(jj, nseg, segrep, repfnz_k, perm_r, dense_k, m_glu);
info = LU_copy_to_col(jj, nseg, segrep, repfnz_k, m_perm_r, dense_k, m_glu);
if ( info )
{
std::cerr << "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() \n";
@ -506,9 +610,9 @@ void SparseLU::factorize(const MatrixType& matrix)
k = 0;
for (i = 0; i < m; ++i)
{
if ( perm_r(i) == IND_EMPTY )
if ( m_perm_r(i) == IND_EMPTY )
{
perm_r(i) = n + k;
m_perm_r(i) = n + k;
++k;
}
}
@ -518,140 +622,21 @@ void SparseLU::factorize(const MatrixType& matrix)
// Apply permutation to the L subscripts
LU_fixupL(n, m_perm_r, m_glu);
// Free work space iwork and work
//...
// Create supernode matrix L
m_Lstore.setInfos(m, n, m_nnzL, Glu.lusup, Glu.xlusup, Glu.lsub, Glu.xlsub, Glu.supno; Glu.xsup);
// Create the column major upper sparse matrix U
new (&m_Ustore) Map<SparseMatrix<Scalar, ColumnMajor> > ( m, n, m_nnzU, Glu.xusub.data(), Glu.usub.data(), Glu.ucol.data() ); //FIXME
this.m_Ustore = m_Ustore;
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;
// it is assumed here that MatrixType = SparseMatrix<Scalar,ColumnMajor>
new (&m_Ustore) Map<MatrixType > ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
this.m_Ustore = m_Ustore; //FIXME Is it necessary
m_info = Success;
m_factorizationIsOk = ok;
m_factorizationIsOk = true;
}
template<typename Rhs, typename Dest>
bool SparseLU::_solve(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);
X = b; /* on return, X is overwritten by the computed solution */
int nrhs = b.cols();
// Permute the right hand side to form Pr*B
X = m_perm_r * X;
// Forward solve PLy = Pb;
Index fsupc; // First column of the current supernode
Index istart; // Pointer index to the subscript of the current column
Index nsupr; // Number of rows in the current supernode
Index nsupc; // Number of columns in the current supernode
Index nrow; // Number of rows in the non-diagonal part of the supernode
Index luptr; // Pointer index to the current nonzero value
Index iptr; // row index pointer iterator
Index irow; //Current index row
Scalar * Lval = m_Lstore.valuePtr(); // Nonzero values
Matrix<Scalar,Dynamic,Dynamic> work(n,nrhs); // working vector
work.setZero();
int j;
for (k = 0; k <= m_Lstore.nsuper(); k ++)
{
fsupc = m_Lstore.sup_to_col()[k];
istart = m_Lstore.rowIndexPtr()[fsupc];
nsupr = m_Lstore..rowIndexPtr()[fsupc+1] - istart;
nsupc = m_Lstore.sup_to_col()[k+1] - fsupc;
nrow = nsupr - nsupc;
if (nsupc == 1 )
{
for (j = 0; j < nrhs; j++)
{
luptr = m_Lstore.colIndexPtr()[fsupc]; //FIXME Should be outside the for loop
for (iptr = istart+1; iptr < m_Lstore.rowIndexPtr()[fsupc+1]; iptr++)
{
irow = m_Lstore.rowIndex()[iptr];
++luptr;
X(irow, j) -= X(fsupc, j) * Lval[luptr];
}
}
}
else
{
// The supernode has more than one column
// Triangular solve
luptr = m_Lstore.colIndexPtr()[fsupc]; //FIXME Should be outside the loop
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
// Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride > u( &(X(fsupc,0)), nsupc, nrhs, OuterStride<>(X.rows()) );
Matrix<Scalar,Dynamic,Dynamic>& u = X.block(fsupc, 0, nsupc, nrhs); //FIXME Check this
u = A.triangularView<Lower>().solve(u);
// Matrix-vector product
new (&A) Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(nsupr) );
work.block(0, 0, nrow, nrhs) = A * u;
//Begin Scatter
for (j = 0; j < nrhs; j++)
{
iptr = istart + nsupc;
for (i = 0; i < nrow; i++)
{
irow = m_Lstore.rowIndex()[iptr];
X(irow, j) -= work(i, j); // Scatter operation
work(i, j) = Scalar(0);
iptr++;
}
}
}
} // end for all supernodes
// Back solve Ux = y
for (k = m_Lstore.nsuper(); k >= 0; k--)
{
fsupc = m_Lstore.sup_to_col()[k];
istart = m_Lstore.rowIndexPtr()[fsupc];
nsupr = m_Lstore..rowIndexPtr()[fsupc+1] - istart;
nsupc = m_Lstore.sup_to_col()[k+1] - fsupc;
luptr = m_Lstore.colIndexPtr()[fsupc];
if (nsupc == 1)
{
for (j = 0; j < nrhs; j++)
{
X(fsupc, j) /= Lval[luptr];
}
}
else
{
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
Matrix<Scalar,Dynamic,Dynamic>& u = X.block(fsupc, 0, nsupc, nrhs);
u = A.triangularView<Upper>().solve(u);
}
for (j = 0; j < nrhs; ++j)
{
for (jcol = fsupc; jcol < fsupc + nsupc; jcol++)
{
for (i = m_Ustore.outerIndexPtr()[jcol]; i < m_Ustore.outerIndexPtr()[jcol]; i++)
{
irow = m_Ustore.InnerIndices()[i];
X(irow, j) -= X(irow, jcol) * m_Ustore.Values()[i];
}
}
}
} // End For U-solve
// Permute back the solution
X = m_perm_c * X;
return true;
}
namespace internal {
/*namespace internal {
template<typename _MatrixType, typename Derived, typename Rhs>
struct solve_retval<SparseLU<_MatrixType,Derived>, Rhs>
@ -666,7 +651,7 @@ struct solve_retval<SparseLU<_MatrixType,Derived>, Rhs>
}
};
} // end namespace internal
}*/ // end namespace internal

97
Eigen/src/SparseLU/SparseLU_Coletree.h
View File

@ -44,13 +44,28 @@
*/
#ifndef SPARSELU_COLETREE_H
#define SPARSELU_COLETREE_H
/** Find the root of the tree/set containing the vertex i : Use Path halving */
template<typename IndexVector>
int etree_find (int i, IndexVector& pp)
{
int p = pp(i); // Parent
int gp = pp(p); // Grand parent
while (gp != p)
{
pp(i) = gp; // Parent pointer on find path is changed to former grand parent
i = gp;
p = pp(i);
gp = pp(p);
}
return p;
}
/** Compute the column elimination tree of a sparse matrix
* NOTE : The matrix is supposed to be in column-major format.
*
*/
template<typename MatrixType, typename IndexVector>
int SparseLU::LU_sp_coletree(const MatrixType& mat, IndexVector& parent)
int LU_sp_coletree(const MatrixType& mat, IndexVector& parent)
{
int nc = mat.cols(); // Number of columns
int nr = mat.rows(); // Number of rows
@ -87,7 +102,7 @@ int SparseLU::LU_sp_coletree(const MatrixType& mat, IndexVector& parent)
{ // A sequence of interleaved find and union is performed
row = firstcol(it.row());
if (row >= col) continue;
rset = internal::etree_find(row, pp); // Find the name of the set containing row
rset = etree_find(row, pp); // Find the name of the set containing row
rroot = root(rset);
if (rroot != col)
{
@ -100,52 +115,6 @@ int SparseLU::LU_sp_coletree(const MatrixType& mat, IndexVector& parent)
return 0;
}
/** Find the root of the tree/set containing the vertex i : Use Path halving */
template<typename IndexVector>
int etree_find (int i, IndexVector& pp)
{
int p = pp(i); // Parent
int gp = pp(p); // Grand parent
while (gp != p)
{
pp(i) = gp; // Parent pointer on find path is changed to former grand parent
i = gp;
p = pp(i);
gp = pp(p);
}
return p;
}
/**
* Post order a tree
* \param parent Input tree
* \param post postordered tree
*/
template<typename IndexVector>
void SparseLU::LU_TreePostorder(int n, IndexVector& parent, IndexVector& post)
{
IndexVector first_kid, next_kid; // Linked list of children
int postnum;
// Allocate storage for working arrays and results
first_kid.resize(n+1);
next_kid.setZero(n+1);
post.setZero(n+1);
// Set up structure describing children
int v, dad;
first_kid.setConstant(-1);
for (v = n-1, v >= 0; v--)
{
dad = parent(v);
next_kid(v) = first_kid(dad);
first_kid(dad) = v;
}
// Depth-first search from dummy root vertex #n
postnum = 0;
internal::LU_nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
return post;
}
/**
* Depth-first search from vertex n. No recursion.
* This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France.
@ -190,4 +159,36 @@ void LU_nr_etdfs (int n, IndexVector& parent, IndexVector& first_kid, IndexVecto
}
}
/**
* Post order a tree
* \param parent Input tree
* \param post postordered tree
*/
template<typename IndexVector>
void LU_TreePostorder(int n, IndexVector& parent, IndexVector& post)
{
IndexVector first_kid, next_kid; // Linked list of children
int postnum;
// Allocate storage for working arrays and results
first_kid.resize(n+1);
next_kid.setZero(n+1);
post.setZero(n+1);
// Set up structure describing children
int v, dad;
first_kid.setConstant(-1);
for (v = n-1; v >= 0; v--)
{
dad = parent(v);
next_kid(v) = first_kid(dad);
first_kid(dad) = v;
}
// Depth-first search from dummy root vertex #n
postnum = 0;
LU_nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
return post;
}
#endif

24
Eigen/src/SparseLU/SparseLU_Matrix.h
View File

@ -45,17 +45,17 @@ template <typename _Scalar, typename _Index>
class SuperNodalMatrix
{
public:
typedef typename _Scalar Scalar;
typedef typename _Index Index;
typedef _Scalar Scalar;
typedef _Index Index;
public:
SuperNodalMatrix()
{
}
SuperNodalMatrix(Index m, Index n, Index nnz, Scalar *nzval, Index* nzval_colptr, Index* rowind,
SuperNodalMatrix(Index m, Index n, Scalar *nzval, Index* nzval_colptr, Index* rowind,
Index* rowind_colptr, Index* col_to_sup, Index* sup_to_col )
{
setInfos(m, n, nnz, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
}
~SuperNodalMatrix()
@ -68,12 +68,11 @@ class SuperNodalMatrix
* FIXME This class will be modified such that it can be use in the course
* of the factorization.
*/
void setInfos(Index m, Index n, Index nnz, Scalar *nzval, Index* nzval_colptr, Index* rowind,
void setInfos(Index m, Index n, Scalar *nzval, Index* nzval_colptr, Index* rowind,
Index* rowind_colptr, Index* col_to_sup, Index* sup_to_col )
{
m_row = m;
m_col = n;
m_nnz = nnz;
m_nzval = nzval;
m_nzval_colptr = nzval_colptr;
m_rowind = rowind;
@ -159,7 +158,7 @@ class SuperNodalMatrix
protected:
Index m_row; // Number of rows
Index m_col; // Number of columns
Index m_nnz; // Number of nonzero values
// Index m_nnz; // Number of nonzero values
Index m_nsuper; // Number of supernodes
Scalar* m_nzval; //array of nonzero values packed by column
Index* m_nzval_colptr; //nzval_colptr[j] Stores the location in nzval[] which starts column j
@ -176,7 +175,7 @@ class SuperNodalMatrix
*
*/
template<typename Scalar, typename Index>
class SuperNodalMatrix::InnerIterator
class SuperNodalMatrix<Scalar,Index>::InnerIterator
{
public:
InnerIterator(const SuperNodalMatrix& mat, Index outer)
@ -184,7 +183,7 @@ class SuperNodalMatrix::InnerIterator
m_outer(outer),
m_idval(mat.colIndexPtr()[outer]),
m_startval(m_idval),
m_endval(mat.colIndexPtr()[outer+1])
m_endval(mat.colIndexPtr()[outer+1]),
m_idrow(mat.rowIndexPtr()[outer]),
m_startidrow(m_idrow),
m_endidrow(mat.rowIndexPtr()[outer+1])
@ -197,7 +196,7 @@ class SuperNodalMatrix::InnerIterator
}
inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; }
inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]; }
inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); }
inline Index index() const { return m_matrix.rowIndex()[m_idrow]; }
inline Index row() const { return index(); }
@ -221,13 +220,14 @@ class SuperNodalMatrix::InnerIterator
const Index m_startidrow; // Start of the row indices of the current column value
const Index m_endidrow; // End of the row indices of the current column value
};
/**
* \brief Iterator class to iterate over nonzeros Supernodes in the triangular supernodal matrix
* \brief Iterator class to iterate over Supernodes in the triangular supernodal matrix
*
* The final goal is to use this class when dealing with supernodes during numerical factorization
*/
template<typename Scalar, typename Index>
class SuperNodalMatrix::SuperNodeIterator
class SuperNodalMatrix<Scalar,Index>::SuperNodeIterator
{
public:
SuperNodeIterator(const SuperNodalMatrix& mat)

174
Eigen/src/SparseLU/SparseLU_Memory.h
View File

@ -55,86 +55,6 @@
#define LU_TempSpace(m, w) ( (2*w + 4 + LU_NO_MARKER) * m * sizeof(Index) \
+ (w + 1) * m * sizeof(Scalar) )
namespace internal {
/**
* \brief Allocate various working space for the numerical factorization phase.
* \param m number of rows of the input matrix
* \param n number of columns
* \param annz number of initial nonzeros in the matrix
* \param work scalar working space needed by all factor routines
* \param iwork Integer working space
* \param lwork if lwork=-1, this routine returns an estimated size of the required memory
* \param glu persistent data to facilitate multiple factors : will be deleted later ??
* \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated when memory allocation failed
* NOTE Unlike SuperLU, this routine does not support successive factorization with the same pattern and the row permutation
*/
template <typename ScalarVector,typename IndexVector>
int LUMemInit(int m, int n, int annz, ScalarVector& work, IndexVector& iwork, int lwork, int fillratio, int panel_size, int maxsuper, int rowblk, GlobalLU_t<ScalarVector, IndexVector>& glu)
{
typedef typename ScalarVector::Scalar;
typedef typename IndexVector::Index;
int& num_expansions = glu.num_expansions; //No memory expansions so far
num_expansions = 0;
// Guess the size for L\U factors
Index& nzlmax = glu.nzlmax;
Index& nzumax = glu.nzumax;
Index& nzlumax = glu.nzlumax;
nzumax = nzlumax = fillratio * annz; // estimated number of nonzeros in U
nzlmax = std::max(1, m_fill_ratio/4.) * annz; // estimated nnz in L factor
// Return the estimated size to the user if necessary
if (lwork == IND_EMPTY)
{
int estimated_size;
estimated_size = LU_GluIntArray(n) * sizeof(Index) + LU_TempSpace(m, m_panel_size)
+ (nzlmax + nzumax) * sizeof(Index) + (nzlumax+nzumax) * sizeof(Scalar) + n);
return estimated_size;
}
// Setup the required space
// First allocate Integer pointers for L\U factors
glu.xsup.resize(n+1);
glu.supno.resize(n+1);
glu.xlsub.resize(n+1);
glu.xlusup.resize(n+1);
glu.xusub.resize(n+1);
// Reserve memory for L/U factors
expand<ScalarVector>(glu.lusup, nzlumax, 0, 0, num_expansions);
expand<ScalarVector>(glu.ucol,nzumax, 0, 0, num_expansions);
expand<IndexVector>(glu.lsub,nzlmax, 0, 0, num_expansions);
expand<IndexVector>(glu.usub,nzumax, 0, 1, num_expansions);
// Check if the memory is correctly allocated,
// FIXME Should be a try... catch section here
while ( !glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size())
{
//Reduce the estimated size and retry
nzlumax /= 2;
nzumax /= 2;
nzlmax /= 2;
if (nzlumax < annz ) return nzlumax;
expand<ScalarVector>(glu.lsup, nzlumax, 0, 0, num_expansions);
expand<ScalarVector>(glu.ucol, nzumax, 0, 0, num_expansions);
expand<IndexVector>(glu.lsub, nzlmax, 0, 0, num_expansions);
expand<IndexVector>(glu.usub, nzumax, 0, 1, num_expansions);
}
// LUWorkInit : Now, allocate known working storage
int isize = (2 * m_panel_size + 3 + LU_NO_MARKER) * m + n;
int dsize = m * m_panel_size + LU_NUM_TEMPV(m, m_panel_size, m_maxsuper, m_rowblk);
iwork.resize(isize);
work.resize(isize);
++num_expansions;
return 0;
} // end LuMemInit
/**
* Expand the existing storage to accomodate more fill-ins
@ -145,7 +65,7 @@ int LUMemInit(int m, int n, int annz, ScalarVector& work, IndexVector& iwork, in
* \param [in,out]num_expansions Number of times the memory has been expanded
*/
template <typename VectorType >
int SparseLU::expand(VectorType& vec, int& length, int len_to_copy, bool keep_prev, int& num_expansions)
int expand(VectorType& vec, int& length, int len_to_copy, bool keep_prev, int& num_expansions)
{
float alpha = 1.5; // Ratio of the memory increase
@ -195,6 +115,85 @@ int SparseLU::expand(VectorType& vec, int& length, int len_to_copy, bool keep_p
return 0;
}
/**
* \brief Allocate various working space for the numerical factorization phase.
* \param m number of rows of the input matrix
* \param n number of columns
* \param annz number of initial nonzeros in the matrix
* \param work scalar working space needed by all factor routines
* \param iwork Integer working space
* \param lwork if lwork=-1, this routine returns an estimated size of the required memory
* \param glu persistent data to facilitate multiple factors : will be deleted later ??
* \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated when memory allocation failed
* NOTE Unlike SuperLU, this routine does not support successive factorization with the same pattern and the row permutation
*/
template <typename ScalarVector,typename IndexVector>
int LUMemInit(int m, int n, int annz, ScalarVector& work, IndexVector& iwork, int lwork, int fillratio, int panel_size, int maxsuper, int rowblk, LU_GlobalLU_t<ScalarVector, IndexVector>& glu)
{
typedef typename ScalarVector::Scalar Scalar;
typedef typename IndexVector::Index Index;
int& num_expansions = glu.num_expansions; //No memory expansions so far
num_expansions = 0;
// Guess the size for L\U factors
Index& nzlmax = glu.nzlmax;
Index& nzumax = glu.nzumax;
Index& nzlumax = glu.nzlumax;
nzumax = nzlumax = fillratio * annz; // estimated number of nonzeros in U
nzlmax = std::max(1., fillratio/4.) * annz; // estimated nnz in L factor
// Return the estimated size to the user if necessary
if (lwork == IND_EMPTY)
{
int estimated_size;
estimated_size = LU_GluIntArray(n) * sizeof(Index) + LU_TempSpace(m, panel_size)
+ (nzlmax + nzumax) * sizeof(Index) + (nzlumax+nzumax) * sizeof(Scalar) + n;
return estimated_size;
}
// Setup the required space
// First allocate Integer pointers for L\U factors
glu.xsup.resize(n+1);
glu.supno.resize(n+1);
glu.xlsub.resize(n+1);
glu.xlusup.resize(n+1);
glu.xusub.resize(n+1);
// Reserve memory for L/U factors
expand<ScalarVector>(glu.lusup, nzlumax, 0, 0, num_expansions);
expand<ScalarVector>(glu.ucol,nzumax, 0, 0, num_expansions);
expand<IndexVector>(glu.lsub,nzlmax, 0, 0, num_expansions);
expand<IndexVector>(glu.usub,nzumax, 0, 1, num_expansions);
// Check if the memory is correctly allocated,
// FIXME Should be a try... catch section here
while ( !glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size())
{
//Reduce the estimated size and retry
nzlumax /= 2;
nzumax /= 2;
nzlmax /= 2;
if (nzlumax < annz ) return nzlumax;
expand<ScalarVector>(glu.lsup, nzlumax, 0, 0, num_expansions);
expand<ScalarVector>(glu.ucol, nzumax, 0, 0, num_expansions);
expand<IndexVector>(glu.lsub, nzlmax, 0, 0, num_expansions);
expand<IndexVector>(glu.usub, nzumax, 0, 1, num_expansions);
}
// LUWorkInit : Now, allocate known working storage
int isize = (2 * panel_size + 3 + LU_NO_MARKER) * m + n;
int dsize = m * panel_size + LU_NUM_TEMPV(m, panel_size, maxsuper, rowblk);
iwork.resize(isize);
work.resize(isize);
++num_expansions;
return 0;
} // end LuMemInit
/**
* \brief Expand the existing storage
* \param vec vector to expand
@ -203,18 +202,17 @@ int SparseLU::expand(VectorType& vec, int& length, int len_to_copy, bool keep_p
* \param glu Global data structure
* \return 0 on success, > 0 size of the memory allocated so far
*/
template <typename IndexVector>
int SparseLU::LUMemXpand(VectorType& vec, int& maxlen, int next, LU_MemType memtype, LU_GlobalLu_t& glu)
template <typename VectorType>
int LUMemXpand(VectorType& vec, int& maxlen, int next, LU_MemType memtype, int& num_expansions)
{
int failed_size;
int& num_expansions = glu.num_expansions;
if (memtype == USUB)
failed_size = expand<IndexVector>(vec, maxlen, next, 1, num_expansions);
failed_size = expand<VectorType>(vec, maxlen, next, 1, num_expansions);
else
failed_size = expand<IndexVector>(vec, maxlen, next, 0, num_expansions);
failed_size = expand<VectorType>(vec, maxlen, next, 0, num_expansions);
if (failed_size)
return faileld_size;
return failed_size;
// The following code is not really needed since maxlen is passed by reference
// and correspond to the appropriate field in glu
@ -236,6 +234,4 @@ int SparseLU::LUMemXpand(VectorType& vec, int& maxlen, int next, LU_MemType memt
return 0 ;
}
}// Namespace Internal
#endif

11
Eigen/src/SparseLU/SparseLU_Structs.h
View File

@ -82,11 +82,11 @@
*/
#ifndef EIGEN_LU_STRUCTS
#define EIGEN_LU_STRUCTS
typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} MemType;
typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} LU_MemType;
template <typename ScalarVector, typename IndexVector>
struct {
template <typename IndexVector, typename ScalarVector>
struct LU_GlobalLU_t {
typedef typename IndexVector::Index Index;
IndexVector xsup; //First supernode column ... xsup(s) points to the beginning of the s-th supernode
IndexVector supno; // Supernode number corresponding to this column (column to supernode mapping)
@ -96,14 +96,11 @@ struct {
IndexVector xlsub; // pointers to the beginning of each column in lsub
Index nzlmax; // Current max size of lsub
Index nzlumax; // Current max size of lusup
ScalarVector ucol; // nonzero values of U ordered by columns
IndexVector usub; // row indices of U columns in ucol
IndexVector xusub; // Pointers to the beginning of each column of U in ucol
Index nzumax; // Current max size of ucol
Index n; // Number of columns in the matrix
int num_expansions;
} GlobalLU_t;
};
#endif

3
Eigen/src/SparseLU/SparseLU_Utils.h
View File

@ -25,7 +25,6 @@
#ifdef EIGEN_SPARSELU_UTILS_H
#define EIGEN_SPARSELU_UTILS_H
// Number of marker arrays used in the factorization each of size n
template <typename IndexVector>
void SparseLU::LU_countnz(const int n, IndexVector& xprune, int& nnzL, int& nnzU, GlobalLU_t& Glu)
@ -34,7 +33,6 @@ void SparseLU::LU_countnz(const int n, IndexVector& xprune, int& nnzL, int& nnzU
IndexVector& xlsub = Glu.xlsub;
nnzL = 0;
nnzU = (Glu.xusub)(n);
int nnzL0 = 0;
int nsuper = (Glu.supno)(n);
int jlen, irep;
@ -52,7 +50,6 @@ void SparseLU::LU_countnz(const int n, IndexVector& xprune, int& nnzL, int& nnzU
jlen--;
}
irep = xsup(i+1) - 1;
nnzL0 += xprune(irep) - xlsub(irep);
}
}

21
Eigen/src/SparseLU/SparseLU_column_bmod.h
View File

@ -44,6 +44,7 @@
*/
#ifndef SPARSELU_COLUMN_BMOD_H
#define SPARSELU_COLUMN_BMOD_H
/**
* \brief Performs numeric block updates (sup-col) in topological order
*
@ -59,11 +60,13 @@
* > 0 - number of bytes allocated when run out of space
*
*/
template <typename ScalarVector, typename IndexVector>
int SparseLU::LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, int fpanelc, LU_GlobalLU_t& glu)
template <typename IndexVector, typename ScalarVector>
int LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, int fpanelc, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
typedef typename IndexVector::Index Index;
typedef typename ScalarVector::Scalar Scalar;
int jsupno, k, ksub, krep, krep_ind, ksupno;
int lptr, nrow, isub, i, irow, nextlu, new_next, ufirst;
int fsupc, nsupc, nsupr, luptr, kfnz, no_zeros;
/* krep = representative of current k-th supernode
* fsupc = first supernodal column
@ -81,7 +84,7 @@ int SparseLU::LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense
ScalarVector& lusup = glu.lusup;
Index& nzlumax = glu.nzlumax;
int jsupno = supno(jcol);
jsupno = supno(jcol);
// For each nonzero supernode segment of U[*,j] in topological order
k = nseg - 1;
int d_fsupc; // distance between the first column of the current panel and the
@ -134,7 +137,7 @@ int SparseLU::LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(nsupr) );
VectorBlock<ScalarVector> u(tempv, 0, segsize);
u = A.triangularView<Lower>().solve(u);
u = A.template triangularView<Lower>().solve(u);
// Dense matrix-vector product y <-- A*x
luptr += segsize;
@ -168,18 +171,18 @@ int SparseLU::LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense
fsupc = xsup(jsupno);
// copy the SPA dense into L\U[*,j]
int mem;
new_next = nextlu + xlsub(fsupc + 1) - xlsub(fsupc);
while (new_next > nzlumax )
{
mem = LUmemXpand<Scalar>(glu.lusup, nzlumax, nextlu, LUSUP, glu);
mem = LUMemXpand<ScalarVector>(glu.lusup, nzlumax, nextlu, LUSUP, glu.num_expansions);
if (mem) return mem;
//lsub = glu.lsub; // Should not be updated here
}
for (isub = xlsub(fsupc); isub < xlsub(fsupc+1); isub++)
{
irow = lsub(isub);
lusub(nextlu) = dense(irow);
lusup(nextlu) = dense(irow);
dense(irow) = Scalar(0.0);
++nextlu;
}
@ -210,7 +213,7 @@ int SparseLU::LU_column_bmod(const int jcol, const int nseg, ScalarVector& dense
ufirst = xlusup(jcol) + d_fsupc;
Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
VectorBlock<ScalarVector> u(lusup, ufirst, nsupc);
u = A.triangularView().solve(u);
u = A.template triangularView().solve(u);
new (&A) Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > ( &(lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(nsupr) );
VectorBlock<ScalarVector> l(lusup, ufirst+nsupc, nrow);

18
Eigen/src/SparseLU/SparseLU_column_dfs.h
View File

@ -44,6 +44,7 @@
*/
#ifndef SPARSELU_COLUMN_DFS_H
#define SPARSELU_COLUMN_DFS_H
/**
* \brief Performs a symbolic factorization on column jcol and decide the supernode boundary
*
@ -57,6 +58,7 @@
* \param m number of rows in the matrix
* \param jcol Current column
* \param perm_r Row permutation
* \param maxsuper
* \param [in,out] nseg Number of segments in current U[*,j] - new segments appended
* \param lsub_col defines the rhs vector to start the dfs
* \param [in,out] segrep Segment representatives - new segments appended
@ -71,9 +73,10 @@
*
*/
template <typename IndexVector, typename ScalarVector>
int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, IndexVector& nseg IndexVector& lsub_col, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t& glu)
int LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, int maxsuper, IndexVector& nseg, IndexVector& lsub_col, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
typedef typename IndexVector::IndexVector;
typedef typename IndexVector::Index Index;
typedef typename ScalarVector::Scalar Scalar;
int jcolp1, jcolm1, jsuper, nsuper, nextl;
int krow; // Row index of the current element
@ -95,6 +98,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
jsuper = nsuper;
nextl = xlsub(jcol);
VectorBlock<IndexVector> marker2(marker, 2*m, m);
int fsupc, jptr, jm1ptr, ito, ifrom, istop;
// For each nonzero in A(*,jcol) do dfs
for (k = 0; lsub_col[k] != IND_EMPTY; k++)
{
@ -115,7 +119,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
lsub(nextl++) = krow; // krow is indexed into A
if ( nextl >= nzlmax )
{
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu);
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu.num_expansions);
if ( mem ) return mem;
}
if (kmark != jcolm1) jsuper = IND_EMPTY; // Row index subset testing
@ -163,7 +167,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
lsub(nextl++) = kchild;
if (nextl >= nzlmax)
{
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu);
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu.num_expansions);
if (mem) return mem;
}
if (chmark != jcolm1) jsuper = IND_EMPTY;
@ -186,7 +190,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
xplore(krep) = xdfs;
oldrep = krep;
krep = chrep; // Go deeped down G(L^t)
parent(krep) = olddrep;
parent(krep) = oldrep;
repfnz(krep) = chperm;
xdfs = xlsub(krep);
maxdfs = xprune(krep);
@ -230,7 +234,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
// Make sure the number of columns in a supernode doesn't
// exceed threshold
if ( (jcol - fsupc) >= m_maxsuper) jsuper = IND_EMPTY;
if ( (jcol - fsupc) >= maxsuper) jsuper = IND_EMPTY;
/* If jcol starts a new supernode, reclaim storage space in
* lsub from previous supernode. Note we only store
@ -241,7 +245,7 @@ int SparseLU::LU_column_dfs(const int m, const int jcol, IndexVector& perm_r, In
{ // starts a new supernode
if ( (fsupc < jcolm1-1) )
{ // >= 3 columns in nsuper
ito = xlsub(fsupcc+1)
ito = xlsub(fsupc+1);
xlsub(jcolm1) = ito;
istop = ito + jptr - jm1ptr;
xprune(jcolm1) = istop; // intialize xprune(jcol-1)

20
Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
View File

@ -44,6 +44,7 @@
*/
#ifndef SPARSELU_COPY_TO_UCOL_H
#define SPARSELU_COPY_TO_UCOL_H
/**
* \brief Performs numeric block updates (sup-col) in topological order
*
@ -58,17 +59,18 @@
* > 0 - number of bytes allocated when run out of space
*
*/
template <typename ScalarVector, typename IndexVector>
int SparseLU::LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segrep, IndexVector& repfnz, IndexVector& perm_r, ScalarVector& dense, LU_GlobalLU_t& glu)
template < typename IndexVector, typename ScalarVector>
int LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segrep, IndexVector& repfnz, IndexVector& perm_r, ScalarVector& dense, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
Index ksupno, k, ksub, krep, ksupno;
typedef typename IndexVector::Index;
typedef typename IndexVector::Index Index;
typedef typename ScalarVector::Scalar Scalar;
Index ksub, krep, ksupno;
IndexVector& xsup = glu.xsup;
IndexVector& supno = glu.supno;
IndexVector& lsub = glu.lsub;
IndexVector& xlsub = glu.xlsub;
ScalarVector& ucol = GLu.ucol;
ScalarVector& ucol = glu.ucol;
IndexVector& usub = glu.usub;
IndexVector& xusub = glu.xusub;
Index& nzumax = glu.nzumax;
@ -76,10 +78,11 @@ int SparseLU::LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segre
Index jsupno = supno(jcol);
// For each nonzero supernode segment of U[*,j] in topological order
k = nseg - 1;
int k = nseg - 1, i;
Index nextu = xusub(jcol);
Index kfnz, isub, segsize;
Index new_next,irow;
Index fsupc, mem;
for (ksub = 0; ksub < nseg; ksub++)
{
krep = segrep(k); k--;
@ -95,9 +98,9 @@ int SparseLU::LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segre
new_next = nextu + segsize;
while (new_next > nzumax)
{
mem = LU_MemXpand<ScalarVector>(ucol, nzumax, nextu, UCOL, glu);
mem = LUMemXpand<ScalarVector>(ucol, nzumax, nextu, UCOL, glu.num_expansions);
if (mem) return mem;
mem = LU_MemXpand<Index>(usub, nzumax, nextu, USUB, glu);
mem = LUMemXpand<IndexVector>(usub, nzumax, nextu, USUB, glu.num_expansions);
if (mem) return mem;
}
@ -120,4 +123,5 @@ int SparseLU::LU_copy_to_ucol(const int jcol, const int nseg, IndexVector& segre
xusub(jcol + 1) = nextu; // close U(*,jcol)
return 0;
}
#endif

6
Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
View File

@ -42,8 +42,7 @@
#ifndef SPARSELU_HEAP_RELAX_SNODE_H
#define SPARSELU_HEAP_RELAX_SNODE_H
#include <SparseLU_coletree.h>
namespace internal {
#include "SparseLU_Coletree.h"
/**
* \brief Identify the initial relaxed supernodes
*
@ -85,12 +84,12 @@ void LU_heap_relax_snode (const int n, IndexVector& et, const int relax_columns,
if (parent != n) // not the dummy root
descendants(parent) += descendants(j) + 1;
}
// Identify the relaxed supernodes by postorder traversal of the etree
register int snode_start; // beginning of a snode
register int k;
int nsuper_et_post = 0; // Number of relaxed snodes in postordered etree
int nsuper_et = 0; // Number of relaxed snodes in the original etree
int l;
for (j = 0; j < n; )
{
parent = et(j);
@ -132,5 +131,4 @@ void LU_heap_relax_snode (const int n, IndexVector& et, const int relax_columns,
// Recover the original etree
et = et_save;
}
} // end namespace internal
#endif

14
Eigen/src/SparseLU/SparseLU_panel_bmod.h
View File

@ -63,8 +63,9 @@
*
*/
template <typename IndexVector, typename ScalarVector>
void SparseLU::LU_panel_bmod(const int m, const int w, const int jcol, const int nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, LU_GlobalLU_t& glu)
void LU_panel_bmod(const int m, const int w, const int jcol, const int nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, LU_GlobalLU_t<IndexVector,ScalarVector>& glu)
{
typedef typename ScalarVector::Scalar Scalar;
IndexVector& xsup = glu.xsup;
IndexVector& supno = glu.supno;
IndexVector& lsub = glu.lsub;
@ -74,11 +75,10 @@ void SparseLU::LU_panel_bmod(const int m, const int w, const int jcol, const int
int i,ksub,jj,nextl_col,irow;
int fsupc, nsupc, nsupr, nrow;
int krep, krep_ind;
int nrow;
int krep, krep_ind, kfnz;
int lptr; // points to the row subscripts of a supernode
int luptr; // ...
int segsze,no_zeros,irow ;
int segsize,no_zeros,isub ;
// For each nonz supernode segment of U[*,j] in topological order
int k = nseg - 1;
for (ksub = 0; ksub < nseg; ksub++)
@ -105,7 +105,7 @@ void SparseLU::LU_panel_bmod(const int m, const int w, const int jcol, const int
{
nextl_col = (jj-jcol) * m;
VectorBlock<IndexVector> repfnz_col(repfnz.segment(nextl_col, m)); // First nonzero column index for each row
VectorBLock<IndexVector> dense_col(dense.segment(nextl_col, m)); // Scatter/gather entire matrix column from/to here
VectorBlock<IndexVector> dense_col(dense.segment(nextl_col, m)); // Scatter/gather entire matrix column from/to here
kfnz = repfnz_col(krep);
if ( kfnz == IND_EMPTY )
@ -134,14 +134,12 @@ void SparseLU::LU_panel_bmod(const int m, const int w, const int jcol, const int
luptr += nsupr * no_zeros + no_zeros;
// triangular solve with Eigen
Map<Matrix<Scalar,Dynamic, Dynamic>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(nsupr) );
// Map<Matrix<Scalar,Dynamic,1> > u( tempv.data(), segsize);
VectorBlock<ScalarVector> u(tempv, 0, segsize);
u = A.triangularView<Lower>().solve(u);
u = A.template triangularView<Lower>().solve(u);
luptr += segsize;
// Dense Matrix vector product y <-- A*x;
new (&A) Map<Matrix<Scalar,Dynamic, Dynamic>, 0, OuterStride<> > ( &(lusup.data()[luptr]), nrow, segsize, OuterStride<>(nsupr) );
// Map<ScalarVector> l( &(tempv.data()[segsize]), nrow);
VectorBlock<ScalarVector> l(tempv, segsize, nrow);
l= A * u;

12
Eigen/src/SparseLU/SparseLU_panel_dfs.h
View File

@ -78,7 +78,7 @@
*
*/
template <typename MatrixType, typename IndexVector, typename ScalarVector>
void SparseLU::LU_panel_dfs(const int m, const int w, const int jcol, MatrixType& A, IndexVector& perm_r, int& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t& Glu)
void LU_panel_dfs(const int m, const int w, const int jcol, MatrixType& A, IndexVector& perm_r, int& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
int jj; // Index through each column in the panel
@ -95,10 +95,10 @@ void SparseLU::LU_panel_dfs(const int m, const int w, const int jcol, MatrixType
// IndexVector& marker1 = marker.block(m, m);
VectorBlock<IndexVector> marker1(marker, m, m);
nseg = 0;
IndexVector& xsup = Glu.xsup;
IndexVector& supno = Glu.supno;
IndexVector& lsub = Glu.lsub;
IndexVector& xlsub = Glu.xlsub;
IndexVector& xsup = glu.xsup;
IndexVector& supno = glu.supno;
IndexVector& lsub = glu.lsub;
IndexVector& xlsub = glu.xlsub;
// For each column in the panel
for (jj = jcol; jj < jcol + w; jj++)
{
@ -109,7 +109,7 @@ void SparseLU::LU_panel_dfs(const int m, const int w, const int jcol, MatrixType
// For each nnz in A[*, jj] do depth first search
for (MatrixType::InnerIterator it(A, jj); it; ++it)
for (typename MatrixType::InnerIterator it(A, jj); it; ++it)
{
krow = it.row();
dense_col(krow) = it.val();

15
Eigen/src/SparseLU/SparseLU_pivotL.h
View File

@ -63,22 +63,22 @@
* \param [in,out]perm_r Row permutation (threshold pivoting)
* \param [in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
* \param [out]pivrow The pivot row
* \param Glu Global LU data
* \param glu Global LU data
* \return 0 if success, i > 0 if U(i,i) is exactly zero
*
*/
template <typename IndexVector, typename ScalarVector>
int SparseLU::LU_pivotL(const int jcol, const RealScalar diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, int& pivrow, GlobalLU_t& Glu)
int LU_pivotL(const int jcol, const typename ScalarVector::RealScalar diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, int& pivrow, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
typedef typename IndexVector::Index Index;
typedef typename ScalarVector::Scalar Scalar;
// Initialize pointers
IndexVector& lsub = Glu.lsub; // Compressed row subscripts of L rectangular supernodes.
IndexVector& xlsub = Glu.xlsub; // pointers to the beginning of each column subscript in lsub
ScalarVector& lusup = Glu.lusup; // Numerical values of L ordered by columns
IndexVector& xlusup = Glu.xlusup; // pointers to the beginning of each colum in lusup
IndexVector& lsub = glu.lsub; // Compressed row subscripts of L rectangular supernodes.
IndexVector& xlsub = glu.xlsub; // pointers to the beginning of each column subscript in lsub
ScalarVector& lusup = glu.lusup; // Numerical values of L ordered by columns
IndexVector& xlusup = glu.xlusup; // pointers to the beginning of each colum in lusup
Index fsupc = (Glu.xsup)((Glu.supno)(jcol)); // First column in the supernode containing the column jcol
Index fsupc = (glu.xsup)((glu.supno)(jcol)); // First column in the supernode containing the column jcol
Index nsupc = jcol - fsupc; // Number of columns in the supernode portion, excluding jcol; nsupc >=0
Index lptr = xlsub(fsupc); // pointer to the starting location of the row subscripts for this supernode portion
Index nsupr = xlsub(fsupc+1) - lptr; // Number of rows in the supernode
@ -93,6 +93,7 @@ int SparseLU::LU_pivotL(const int jcol, const RealScalar diagpivotthresh, IndexV
Index diag = IND_EMPTY;
Index old_pivptr = nsupc;
Scalar rtemp;
Index isub, icol, itemp, k;
for (isub = nsupc; isub < nsupr; ++isub) {
rtemp = std::abs(lu_col_ptr[isub]);
if (rtemp > pivmax) {

26
Eigen/src/SparseLU/SparseLU_pruneL.h
View File

@ -44,6 +44,7 @@
*/
#ifndef SPARSELU_PRUNEL_H
#define SPARSELU_PRUNEL_H
/**
* \brief Prunes the L-structure.
*
@ -57,25 +58,27 @@
* \param segrep
* \param repfnz
* \param [out]xprune
* \param Glu Global LU data
* \param glu Global LU data
*
*/
template <typename IndexVector, typename ScalarVector>
void SparseLU::LU_pruneL(const int jcol, const IndexVector& perm_r, const int pivrow, const int nseg, const IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, GlobalLU_t& Glu)
void LU_pruneL(const int jcol, const IndexVector& perm_r, const int pivrow, const int nseg, const IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{
typedef typename IndexVector::Index Index;
typedef typename ScalarVector::Scalar Scalar;
// Initialize pointers
IndexVector& xsup = Glu.xsup;
IndexVector& supno = Glu.supno;
IndexVector& lsub = Glu.lsub;
IndexVector& xlsub = Glu.xlsub;
ScalarVector& lusup = Glu.lusup;
IndexVector& xlusup = Glu.xlusup;
IndexVector& xsup = glu.xsup;
IndexVector& supno = glu.supno;
IndexVector& lsub = glu.lsub;
IndexVector& xlsub = glu.xlsub;
ScalarVector& lusup = glu.lusup;
IndexVector& xlusup = glu.xlusup;
// For each supernode-rep irep in U(*,j]
int jsupno = supno(jcol);
int i,irep,irep1;
bool movnum, do_prune = false;
Index kmin, kmax, ktemp, minloc, maxloc;
Index kmin, kmax, ktemp, minloc, maxloc,krow;
for (i = 0; i < nseg; i++)
{
irep = segrep(i);
@ -88,12 +91,12 @@ void SparseLU::LU_pruneL(const int jcol, const IndexVector& perm_r, const int pi
// If a snode overlaps with the next panel, then the U-segment
// is fragmented into two parts -- irep and irep1. We should let
// pruning occur at the rep-column in irep1s snode.
if (supno(irep) == supno(irep1) continue; // don't prune
if (supno(irep) == supno(irep1) ) continue; // don't prune
// If it has not been pruned & it has a nonz in row L(pivrow,i)
if (supno(irep) != jsupno )
{
if ( xprune (irep) >= xlsub(irep1)
if ( xprune (irep) >= xlsub(irep1) )
{
kmin = xlsub(irep);
kmax = xlsub(irep1) - 1;
@ -147,4 +150,5 @@ void SparseLU::LU_pruneL(const int jcol, const IndexVector& perm_r, const int pi
} // end pruning
} // End for each U-segment
}
#endif

3
Eigen/src/SparseLU/SparseLU_relax_snode.h
View File

@ -42,7 +42,6 @@
#ifndef SPARSELU_RELAX_SNODE_H
#define SPARSELU_RELAX_SNODE_H
namespace internal {
/**
* \brief Identify the initial relaxed supernodes
*
@ -87,6 +86,4 @@ void LU_relax_snode (const int n, IndexVector& et, const int relax_columns, Inde
} // End postorder traversal of the etree
}
} // end namespace internal
#endif

15
Eigen/src/SparseLU/SparseLU_snode_bmod.h
View File

@ -42,14 +42,13 @@
* granted, provided the above notices are retained, and a notice that
* the code was modified is included with the above copyright notice.
*/
namespace internal {
#ifndef SPARSELU_SNODE_BMOD_H
#define SPARSELU_SNODE_BMOD_H
template <typename Index, typename ScalarVector>
int SparseLU::LU_dsnode_bmod (const Index jcol, const Index jsupno, const Index fsupc,
ScalarVector& dense, LU_GlobalLU_t& glu)
template <typename IndexVector, typename ScalarVector>
int LU_snode_bmod (const int jcol, const int jsupno, const int fsupc,
ScalarVector& dense, LU_GlobalLU_t<IndexVector,ScalarVector>& glu)
{
typedef typename Matrix<Index, Dynamic, Dynamic> IndexVector;
typedef typename ScalarVector::Scalar Scalar;
IndexVector& lsub = glu.lsub; // Compressed row subscripts of ( rectangular supernodes ??)
IndexVector& xlsub = glu.xlsub; // xlsub[j] is the starting location of the j-th column in lsub(*)
ScalarVector& lusup = glu.lusup; // Numerical values of the rectangular supernodes
@ -77,17 +76,15 @@ int SparseLU::LU_dsnode_bmod (const Index jcol, const Index jsupno, const Index
// Solve the triangular system for U(fsupc:jcol, jcol) with L(fspuc:jcol, fsupc:jcol)
Map<Matrix<Scalar,Dynamic,Dynamic>,0,OuterStride<> > A( &(lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(nsupr) );
// Map<Matrix<Scalar,Dynamic,1> > u(&(lusup.data()[ufirst]), nsupc);
VectorBlock<ScalarVector> u(lusup, ufirst, nsupc);
u = A.triangularView<Lower>().solve(u); // Call the Eigen dense triangular solve interface
u = A.template triangularView<Lower>().solve(u); // Call the Eigen dense triangular solve interface
// Update the trailing part of the column jcol U(jcol:jcol+nrow, jcol) using L(jcol:jcol+nrow, fsupc:jcol) and U(fsupc:jcol)
new (&A) Map<Matrix<Scalar,Dynamic,Dynamic>,0,OuterStride<> > ( &(lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(nsupr) );
// Map<Matrix<Scalar,Dynamic,1> > l(&(lusup.data()[ufirst+nsupc], nrow);
VectorBlock<ScalarVector> l(lusup, ufirst+nsupc, nrow);
l = l - A * u;
}
return 0;
}
} // End namespace internal
#endif

9
Eigen/src/SparseLU/SparseLU_snode_dfs.h
View File

@ -44,7 +44,6 @@
*/
#ifdef SPARSELU_SNODE_DFS_H
#define SPARSELU_SNODE_DFS_H
namespace eigen {
/**
* \brief Determine the union of the row structures of those columns within the relaxed snode.
* NOTE: The relaxed snodes are leaves of the supernodal etree, therefore,
@ -59,7 +58,7 @@ namespace eigen {
* \return 0 on success, > 0 size of the memory when memory allocation failed
*/
template <typename IndexVector, typename ScalarVector>
int SparseLU::LU_snode_dfs(const int jcol, const int kcol, const IndexVector* asub, const IndexVector* colptr, IndexVector& xprune, IndexVector& marker, LU_GlobalLU_t& glu)
int LU_snode_dfs(const int jcol, const int kcol, const IndexVector* asub, const IndexVector* colptr, IndexVector& xprune, IndexVector& marker, LU_GlobalLU_t& glu)
{
typedef typename IndexVector::Index;
IndexVector& xsup = glu.xsup;
@ -86,7 +85,7 @@ namespace eigen {
lsub(nextl++) = krow;
if( nextl >= nzlmax )
{
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu);
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu.num_expansions);
if (mem) return mem;
}
}
@ -100,7 +99,7 @@ namespace eigen {
Index new_next = nextl + (nextl - xlsub(jcol));
while (new_next > nzlmax)
{
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu);
mem = LUMemXpand<IndexVector>(lsub, nzlmax, nextl, LSUB, glu.num_expansions);
if (mem) return mem;
}
Index ifrom, ito = nextl;
@ -115,6 +114,4 @@ namespace eigen {
xlsub(kcol+1) = nextl;
return 0;
}
} // end namespace eigen
#endif

5
bench/spbench/CMakeLists.txt
View File

@ -63,3 +63,8 @@ endif(RT_LIBRARY)
add_executable(spbenchsolver spbenchsolver.cpp)
target_link_libraries (spbenchsolver ${SPARSE_LIBS})
add_executable(spsolver sp_solver.cpp)
target_link_libraries (spsolver ${SPARSE_LIBS})
add_executable(test_sparseLU test_sparseLU.cpp)
target_link_libraries (test_sparseLU ${SPARSE_LIBS})