[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | #ifndef EIGEN_SUPERLUSUPPORT_H
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| 11 | #define EIGEN_SUPERLUSUPPORT_H
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| 12 |
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| 13 | namespace Eigen {
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| 14 |
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| 15 | #define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE) \
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| 16 | extern "C" { \
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| 17 | typedef struct { FLOATTYPE for_lu; FLOATTYPE total_needed; int expansions; } PREFIX##mem_usage_t; \
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| 18 | extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *, \
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| 19 | char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *, \
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| 20 | void *, int, SuperMatrix *, SuperMatrix *, \
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| 21 | FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, \
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| 22 | PREFIX##mem_usage_t *, SuperLUStat_t *, int *); \
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| 23 | } \
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| 24 | inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A, \
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| 25 | int *perm_c, int *perm_r, int *etree, char *equed, \
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| 26 | FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, \
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| 27 | SuperMatrix *U, void *work, int lwork, \
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| 28 | SuperMatrix *B, SuperMatrix *X, \
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| 29 | FLOATTYPE *recip_pivot_growth, \
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| 30 | FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr, \
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| 31 | SuperLUStat_t *stats, int *info, KEYTYPE) { \
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| 32 | PREFIX##mem_usage_t mem_usage; \
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| 33 | PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L, \
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| 34 | U, work, lwork, B, X, recip_pivot_growth, rcond, \
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| 35 | ferr, berr, &mem_usage, stats, info); \
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| 36 | return mem_usage.for_lu; /* bytes used by the factor storage */ \
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| 37 | }
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| 38 |
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| 39 | DECL_GSSVX(s,float,float)
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| 40 | DECL_GSSVX(c,float,std::complex<float>)
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| 41 | DECL_GSSVX(d,double,double)
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| 42 | DECL_GSSVX(z,double,std::complex<double>)
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| 43 |
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| 44 | #ifdef MILU_ALPHA
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| 45 | #define EIGEN_SUPERLU_HAS_ILU
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| 46 | #endif
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| 47 |
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| 48 | #ifdef EIGEN_SUPERLU_HAS_ILU
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| 49 |
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| 50 | // similarly for the incomplete factorization using gsisx
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| 51 | #define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE) \
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| 52 | extern "C" { \
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| 53 | extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *, \
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| 54 | char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *, \
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| 55 | void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *, \
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| 56 | PREFIX##mem_usage_t *, SuperLUStat_t *, int *); \
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| 57 | } \
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| 58 | inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A, \
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| 59 | int *perm_c, int *perm_r, int *etree, char *equed, \
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| 60 | FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, \
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| 61 | SuperMatrix *U, void *work, int lwork, \
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| 62 | SuperMatrix *B, SuperMatrix *X, \
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| 63 | FLOATTYPE *recip_pivot_growth, \
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| 64 | FLOATTYPE *rcond, \
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| 65 | SuperLUStat_t *stats, int *info, KEYTYPE) { \
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| 66 | PREFIX##mem_usage_t mem_usage; \
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| 67 | PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L, \
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| 68 | U, work, lwork, B, X, recip_pivot_growth, rcond, \
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| 69 | &mem_usage, stats, info); \
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| 70 | return mem_usage.for_lu; /* bytes used by the factor storage */ \
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| 71 | }
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| 72 |
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| 73 | DECL_GSISX(s,float,float)
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| 74 | DECL_GSISX(c,float,std::complex<float>)
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| 75 | DECL_GSISX(d,double,double)
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| 76 | DECL_GSISX(z,double,std::complex<double>)
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| 77 |
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| 78 | #endif
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| 79 |
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| 80 | template<typename MatrixType>
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| 81 | struct SluMatrixMapHelper;
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| 82 |
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| 83 | /** \internal
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| 84 | *
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| 85 | * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
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| 86 | * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
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| 87 | *
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| 88 | * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
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| 89 | */
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| 90 | struct SluMatrix : SuperMatrix
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| 91 | {
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| 92 | SluMatrix()
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| 93 | {
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| 94 | Store = &storage;
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| 95 | }
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| 96 |
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| 97 | SluMatrix(const SluMatrix& other)
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| 98 | : SuperMatrix(other)
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| 99 | {
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| 100 | Store = &storage;
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| 101 | storage = other.storage;
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| 102 | }
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| 103 |
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| 104 | SluMatrix& operator=(const SluMatrix& other)
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| 105 | {
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| 106 | SuperMatrix::operator=(static_cast<const SuperMatrix&>(other));
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| 107 | Store = &storage;
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| 108 | storage = other.storage;
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| 109 | return *this;
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| 110 | }
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| 111 |
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| 112 | struct
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| 113 | {
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| 114 | union {int nnz;int lda;};
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| 115 | void *values;
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| 116 | int *innerInd;
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| 117 | int *outerInd;
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| 118 | } storage;
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| 119 |
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| 120 | void setStorageType(Stype_t t)
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| 121 | {
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| 122 | Stype = t;
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| 123 | if (t==SLU_NC || t==SLU_NR || t==SLU_DN)
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| 124 | Store = &storage;
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| 125 | else
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| 126 | {
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| 127 | eigen_assert(false && "storage type not supported");
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| 128 | Store = 0;
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| 129 | }
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| 130 | }
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| 131 |
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| 132 | template<typename Scalar>
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| 133 | void setScalarType()
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| 134 | {
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| 135 | if (internal::is_same<Scalar,float>::value)
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| 136 | Dtype = SLU_S;
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| 137 | else if (internal::is_same<Scalar,double>::value)
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| 138 | Dtype = SLU_D;
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| 139 | else if (internal::is_same<Scalar,std::complex<float> >::value)
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| 140 | Dtype = SLU_C;
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| 141 | else if (internal::is_same<Scalar,std::complex<double> >::value)
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| 142 | Dtype = SLU_Z;
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| 143 | else
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| 144 | {
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| 145 | eigen_assert(false && "Scalar type not supported by SuperLU");
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| 146 | }
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| 147 | }
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| 148 |
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| 149 | template<typename MatrixType>
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| 150 | static SluMatrix Map(MatrixBase<MatrixType>& _mat)
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| 151 | {
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| 152 | MatrixType& mat(_mat.derived());
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| 153 | eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU");
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| 154 | SluMatrix res;
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| 155 | res.setStorageType(SLU_DN);
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| 156 | res.setScalarType<typename MatrixType::Scalar>();
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| 157 | res.Mtype = SLU_GE;
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| 158 |
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| 159 | res.nrow = mat.rows();
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| 160 | res.ncol = mat.cols();
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| 161 |
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| 162 | res.storage.lda = MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride();
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| 163 | res.storage.values = (void*)(mat.data());
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| 164 | return res;
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| 165 | }
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| 166 |
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| 167 | template<typename MatrixType>
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| 168 | static SluMatrix Map(SparseMatrixBase<MatrixType>& mat)
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| 169 | {
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| 170 | SluMatrix res;
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| 171 | if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
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| 172 | {
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| 173 | res.setStorageType(SLU_NR);
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| 174 | res.nrow = mat.cols();
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| 175 | res.ncol = mat.rows();
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| 176 | }
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| 177 | else
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| 178 | {
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| 179 | res.setStorageType(SLU_NC);
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| 180 | res.nrow = mat.rows();
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| 181 | res.ncol = mat.cols();
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| 182 | }
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| 183 |
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| 184 | res.Mtype = SLU_GE;
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| 185 |
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| 186 | res.storage.nnz = mat.nonZeros();
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| 187 | res.storage.values = mat.derived().valuePtr();
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| 188 | res.storage.innerInd = mat.derived().innerIndexPtr();
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| 189 | res.storage.outerInd = mat.derived().outerIndexPtr();
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| 190 |
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| 191 | res.setScalarType<typename MatrixType::Scalar>();
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| 192 |
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| 193 | // FIXME the following is not very accurate
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| 194 | if (MatrixType::Flags & Upper)
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| 195 | res.Mtype = SLU_TRU;
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| 196 | if (MatrixType::Flags & Lower)
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| 197 | res.Mtype = SLU_TRL;
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| 198 |
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| 199 | eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
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| 200 |
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| 201 | return res;
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| 202 | }
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| 203 | };
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| 204 |
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| 205 | template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
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| 206 | struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> >
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| 207 | {
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| 208 | typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType;
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| 209 | static void run(MatrixType& mat, SluMatrix& res)
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| 210 | {
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| 211 | eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU");
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| 212 | res.setStorageType(SLU_DN);
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| 213 | res.setScalarType<Scalar>();
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| 214 | res.Mtype = SLU_GE;
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| 215 |
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| 216 | res.nrow = mat.rows();
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| 217 | res.ncol = mat.cols();
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| 218 |
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| 219 | res.storage.lda = mat.outerStride();
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| 220 | res.storage.values = mat.data();
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| 221 | }
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| 222 | };
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| 223 |
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| 224 | template<typename Derived>
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| 225 | struct SluMatrixMapHelper<SparseMatrixBase<Derived> >
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| 226 | {
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| 227 | typedef Derived MatrixType;
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| 228 | static void run(MatrixType& mat, SluMatrix& res)
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| 229 | {
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| 230 | if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
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| 231 | {
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| 232 | res.setStorageType(SLU_NR);
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| 233 | res.nrow = mat.cols();
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| 234 | res.ncol = mat.rows();
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| 235 | }
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| 236 | else
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| 237 | {
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| 238 | res.setStorageType(SLU_NC);
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| 239 | res.nrow = mat.rows();
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| 240 | res.ncol = mat.cols();
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| 241 | }
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| 242 |
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| 243 | res.Mtype = SLU_GE;
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| 244 |
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| 245 | res.storage.nnz = mat.nonZeros();
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| 246 | res.storage.values = mat.valuePtr();
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| 247 | res.storage.innerInd = mat.innerIndexPtr();
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| 248 | res.storage.outerInd = mat.outerIndexPtr();
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| 249 |
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| 250 | res.setScalarType<typename MatrixType::Scalar>();
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| 251 |
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| 252 | // FIXME the following is not very accurate
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| 253 | if (MatrixType::Flags & Upper)
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| 254 | res.Mtype = SLU_TRU;
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| 255 | if (MatrixType::Flags & Lower)
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| 256 | res.Mtype = SLU_TRL;
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| 257 |
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| 258 | eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
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| 259 | }
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| 260 | };
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| 261 |
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| 262 | namespace internal {
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| 263 |
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| 264 | template<typename MatrixType>
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| 265 | SluMatrix asSluMatrix(MatrixType& mat)
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| 266 | {
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| 267 | return SluMatrix::Map(mat);
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| 268 | }
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| 269 |
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| 270 | /** View a Super LU matrix as an Eigen expression */
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| 271 | template<typename Scalar, int Flags, typename Index>
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| 272 | MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
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| 273 | {
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| 274 | eigen_assert((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR
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| 275 | || (Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC);
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| 276 |
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| 277 | Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;
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| 278 |
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| 279 | return MappedSparseMatrix<Scalar,Flags,Index>(
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| 280 | sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
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| 281 | sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) );
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| 282 | }
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| 283 |
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| 284 | } // end namespace internal
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| 285 |
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| 286 | /** \ingroup SuperLUSupport_Module
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| 287 | * \class SuperLUBase
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| 288 | * \brief The base class for the direct and incomplete LU factorization of SuperLU
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| 289 | */
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| 290 | template<typename _MatrixType, typename Derived>
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| 291 | class SuperLUBase : internal::noncopyable
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| 292 | {
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| 293 | public:
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| 294 | typedef _MatrixType MatrixType;
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| 295 | typedef typename MatrixType::Scalar Scalar;
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| 296 | typedef typename MatrixType::RealScalar RealScalar;
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| 297 | typedef typename MatrixType::Index Index;
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| 298 | typedef Matrix<Scalar,Dynamic,1> Vector;
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| 299 | typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
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| 300 | typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
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| 301 | typedef SparseMatrix<Scalar> LUMatrixType;
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| 302 |
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| 303 | public:
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| 304 |
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| 305 | SuperLUBase() {}
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| 306 |
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| 307 | ~SuperLUBase()
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| 308 | {
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| 309 | clearFactors();
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| 310 | }
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| 311 |
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| 312 | Derived& derived() { return *static_cast<Derived*>(this); }
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| 313 | const Derived& derived() const { return *static_cast<const Derived*>(this); }
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| 314 |
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| 315 | inline Index rows() const { return m_matrix.rows(); }
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| 316 | inline Index cols() const { return m_matrix.cols(); }
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| 317 |
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| 318 | /** \returns a reference to the Super LU option object to configure the Super LU algorithms. */
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| 319 | inline superlu_options_t& options() { return m_sluOptions; }
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| 320 |
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| 321 | /** \brief Reports whether previous computation was successful.
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| 322 | *
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| 323 | * \returns \c Success if computation was succesful,
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| 324 | * \c NumericalIssue if the matrix.appears to be negative.
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| 325 | */
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| 326 | ComputationInfo info() const
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| 327 | {
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| 328 | eigen_assert(m_isInitialized && "Decomposition is not initialized.");
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| 329 | return m_info;
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| 330 | }
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| 331 |
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| 332 | /** Computes the sparse Cholesky decomposition of \a matrix */
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| 333 | void compute(const MatrixType& matrix)
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| 334 | {
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| 335 | derived().analyzePattern(matrix);
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| 336 | derived().factorize(matrix);
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| 337 | }
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| 338 |
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| 339 | /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
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| 340 | *
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| 341 | * \sa compute()
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| 342 | */
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| 343 | template<typename Rhs>
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| 344 | inline const internal::solve_retval<SuperLUBase, Rhs> solve(const MatrixBase<Rhs>& b) const
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| 345 | {
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| 346 | eigen_assert(m_isInitialized && "SuperLU is not initialized.");
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| 347 | eigen_assert(rows()==b.rows()
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| 348 | && "SuperLU::solve(): invalid number of rows of the right hand side matrix b");
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| 349 | return internal::solve_retval<SuperLUBase, Rhs>(*this, b.derived());
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| 350 | }
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| 351 |
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| 352 | /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
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| 353 | *
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| 354 | * \sa compute()
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| 355 | */
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| 356 | template<typename Rhs>
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| 357 | inline const internal::sparse_solve_retval<SuperLUBase, Rhs> solve(const SparseMatrixBase<Rhs>& b) const
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| 358 | {
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| 359 | eigen_assert(m_isInitialized && "SuperLU is not initialized.");
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| 360 | eigen_assert(rows()==b.rows()
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| 361 | && "SuperLU::solve(): invalid number of rows of the right hand side matrix b");
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| 362 | return internal::sparse_solve_retval<SuperLUBase, Rhs>(*this, b.derived());
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| 363 | }
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| 364 |
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| 365 | /** Performs a symbolic decomposition on the sparcity of \a matrix.
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| 366 | *
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| 367 | * This function is particularly useful when solving for several problems having the same structure.
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| 368 | *
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| 369 | * \sa factorize()
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| 370 | */
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| 371 | void analyzePattern(const MatrixType& /*matrix*/)
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| 372 | {
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| 373 | m_isInitialized = true;
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| 374 | m_info = Success;
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| 375 | m_analysisIsOk = true;
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| 376 | m_factorizationIsOk = false;
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| 377 | }
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| 378 |
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| 379 | template<typename Stream>
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| 380 | void dumpMemory(Stream& /*s*/)
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| 381 | {}
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| 382 |
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| 383 | protected:
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| 384 |
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| 385 | void initFactorization(const MatrixType& a)
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| 386 | {
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| 387 | set_default_options(&this->m_sluOptions);
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| 388 |
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| 389 | const int size = a.rows();
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| 390 | m_matrix = a;
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| 391 |
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| 392 | m_sluA = internal::asSluMatrix(m_matrix);
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| 393 | clearFactors();
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| 394 |
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| 395 | m_p.resize(size);
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| 396 | m_q.resize(size);
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| 397 | m_sluRscale.resize(size);
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| 398 | m_sluCscale.resize(size);
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| 399 | m_sluEtree.resize(size);
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| 400 |
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| 401 | // set empty B and X
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| 402 | m_sluB.setStorageType(SLU_DN);
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| 403 | m_sluB.setScalarType<Scalar>();
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| 404 | m_sluB.Mtype = SLU_GE;
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| 405 | m_sluB.storage.values = 0;
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| 406 | m_sluB.nrow = 0;
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| 407 | m_sluB.ncol = 0;
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| 408 | m_sluB.storage.lda = size;
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| 409 | m_sluX = m_sluB;
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| 410 |
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| 411 | m_extractedDataAreDirty = true;
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| 412 | }
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| 413 |
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| 414 | void init()
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| 415 | {
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| 416 | m_info = InvalidInput;
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| 417 | m_isInitialized = false;
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| 418 | m_sluL.Store = 0;
|
---|
| 419 | m_sluU.Store = 0;
|
---|
| 420 | }
|
---|
| 421 |
|
---|
| 422 | void extractData() const;
|
---|
| 423 |
|
---|
| 424 | void clearFactors()
|
---|
| 425 | {
|
---|
| 426 | if(m_sluL.Store)
|
---|
| 427 | Destroy_SuperNode_Matrix(&m_sluL);
|
---|
| 428 | if(m_sluU.Store)
|
---|
| 429 | Destroy_CompCol_Matrix(&m_sluU);
|
---|
| 430 |
|
---|
| 431 | m_sluL.Store = 0;
|
---|
| 432 | m_sluU.Store = 0;
|
---|
| 433 |
|
---|
| 434 | memset(&m_sluL,0,sizeof m_sluL);
|
---|
| 435 | memset(&m_sluU,0,sizeof m_sluU);
|
---|
| 436 | }
|
---|
| 437 |
|
---|
| 438 | // cached data to reduce reallocation, etc.
|
---|
| 439 | mutable LUMatrixType m_l;
|
---|
| 440 | mutable LUMatrixType m_u;
|
---|
| 441 | mutable IntColVectorType m_p;
|
---|
| 442 | mutable IntRowVectorType m_q;
|
---|
| 443 |
|
---|
| 444 | mutable LUMatrixType m_matrix; // copy of the factorized matrix
|
---|
| 445 | mutable SluMatrix m_sluA;
|
---|
| 446 | mutable SuperMatrix m_sluL, m_sluU;
|
---|
| 447 | mutable SluMatrix m_sluB, m_sluX;
|
---|
| 448 | mutable SuperLUStat_t m_sluStat;
|
---|
| 449 | mutable superlu_options_t m_sluOptions;
|
---|
| 450 | mutable std::vector<int> m_sluEtree;
|
---|
| 451 | mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale;
|
---|
| 452 | mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr;
|
---|
| 453 | mutable char m_sluEqued;
|
---|
| 454 |
|
---|
| 455 | mutable ComputationInfo m_info;
|
---|
| 456 | bool m_isInitialized;
|
---|
| 457 | int m_factorizationIsOk;
|
---|
| 458 | int m_analysisIsOk;
|
---|
| 459 | mutable bool m_extractedDataAreDirty;
|
---|
| 460 |
|
---|
| 461 | private:
|
---|
| 462 | SuperLUBase(SuperLUBase& ) { }
|
---|
| 463 | };
|
---|
| 464 |
|
---|
| 465 |
|
---|
| 466 | /** \ingroup SuperLUSupport_Module
|
---|
| 467 | * \class SuperLU
|
---|
| 468 | * \brief A sparse direct LU factorization and solver based on the SuperLU library
|
---|
| 469 | *
|
---|
| 470 | * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
|
---|
| 471 | * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
|
---|
| 472 | * X and B can be either dense or sparse.
|
---|
| 473 | *
|
---|
| 474 | * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
---|
| 475 | *
|
---|
| 476 | * \sa \ref TutorialSparseDirectSolvers
|
---|
| 477 | */
|
---|
| 478 | template<typename _MatrixType>
|
---|
| 479 | class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> >
|
---|
| 480 | {
|
---|
| 481 | public:
|
---|
| 482 | typedef SuperLUBase<_MatrixType,SuperLU> Base;
|
---|
| 483 | typedef _MatrixType MatrixType;
|
---|
| 484 | typedef typename Base::Scalar Scalar;
|
---|
| 485 | typedef typename Base::RealScalar RealScalar;
|
---|
| 486 | typedef typename Base::Index Index;
|
---|
| 487 | typedef typename Base::IntRowVectorType IntRowVectorType;
|
---|
| 488 | typedef typename Base::IntColVectorType IntColVectorType;
|
---|
| 489 | typedef typename Base::LUMatrixType LUMatrixType;
|
---|
| 490 | typedef TriangularView<LUMatrixType, Lower|UnitDiag> LMatrixType;
|
---|
| 491 | typedef TriangularView<LUMatrixType, Upper> UMatrixType;
|
---|
| 492 |
|
---|
| 493 | public:
|
---|
| 494 |
|
---|
| 495 | SuperLU() : Base() { init(); }
|
---|
| 496 |
|
---|
| 497 | SuperLU(const MatrixType& matrix) : Base()
|
---|
| 498 | {
|
---|
| 499 | init();
|
---|
| 500 | Base::compute(matrix);
|
---|
| 501 | }
|
---|
| 502 |
|
---|
| 503 | ~SuperLU()
|
---|
| 504 | {
|
---|
| 505 | }
|
---|
| 506 |
|
---|
| 507 | /** Performs a symbolic decomposition on the sparcity of \a matrix.
|
---|
| 508 | *
|
---|
| 509 | * This function is particularly useful when solving for several problems having the same structure.
|
---|
| 510 | *
|
---|
| 511 | * \sa factorize()
|
---|
| 512 | */
|
---|
| 513 | void analyzePattern(const MatrixType& matrix)
|
---|
| 514 | {
|
---|
| 515 | m_info = InvalidInput;
|
---|
| 516 | m_isInitialized = false;
|
---|
| 517 | Base::analyzePattern(matrix);
|
---|
| 518 | }
|
---|
| 519 |
|
---|
| 520 | /** Performs a numeric decomposition of \a matrix
|
---|
| 521 | *
|
---|
| 522 | * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
|
---|
| 523 | *
|
---|
| 524 | * \sa analyzePattern()
|
---|
| 525 | */
|
---|
| 526 | void factorize(const MatrixType& matrix);
|
---|
| 527 |
|
---|
| 528 | #ifndef EIGEN_PARSED_BY_DOXYGEN
|
---|
| 529 | /** \internal */
|
---|
| 530 | template<typename Rhs,typename Dest>
|
---|
| 531 | void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
|
---|
| 532 | #endif // EIGEN_PARSED_BY_DOXYGEN
|
---|
| 533 |
|
---|
| 534 | inline const LMatrixType& matrixL() const
|
---|
| 535 | {
|
---|
| 536 | if (m_extractedDataAreDirty) this->extractData();
|
---|
| 537 | return m_l;
|
---|
| 538 | }
|
---|
| 539 |
|
---|
| 540 | inline const UMatrixType& matrixU() const
|
---|
| 541 | {
|
---|
| 542 | if (m_extractedDataAreDirty) this->extractData();
|
---|
| 543 | return m_u;
|
---|
| 544 | }
|
---|
| 545 |
|
---|
| 546 | inline const IntColVectorType& permutationP() const
|
---|
| 547 | {
|
---|
| 548 | if (m_extractedDataAreDirty) this->extractData();
|
---|
| 549 | return m_p;
|
---|
| 550 | }
|
---|
| 551 |
|
---|
| 552 | inline const IntRowVectorType& permutationQ() const
|
---|
| 553 | {
|
---|
| 554 | if (m_extractedDataAreDirty) this->extractData();
|
---|
| 555 | return m_q;
|
---|
| 556 | }
|
---|
| 557 |
|
---|
| 558 | Scalar determinant() const;
|
---|
| 559 |
|
---|
| 560 | protected:
|
---|
| 561 |
|
---|
| 562 | using Base::m_matrix;
|
---|
| 563 | using Base::m_sluOptions;
|
---|
| 564 | using Base::m_sluA;
|
---|
| 565 | using Base::m_sluB;
|
---|
| 566 | using Base::m_sluX;
|
---|
| 567 | using Base::m_p;
|
---|
| 568 | using Base::m_q;
|
---|
| 569 | using Base::m_sluEtree;
|
---|
| 570 | using Base::m_sluEqued;
|
---|
| 571 | using Base::m_sluRscale;
|
---|
| 572 | using Base::m_sluCscale;
|
---|
| 573 | using Base::m_sluL;
|
---|
| 574 | using Base::m_sluU;
|
---|
| 575 | using Base::m_sluStat;
|
---|
| 576 | using Base::m_sluFerr;
|
---|
| 577 | using Base::m_sluBerr;
|
---|
| 578 | using Base::m_l;
|
---|
| 579 | using Base::m_u;
|
---|
| 580 |
|
---|
| 581 | using Base::m_analysisIsOk;
|
---|
| 582 | using Base::m_factorizationIsOk;
|
---|
| 583 | using Base::m_extractedDataAreDirty;
|
---|
| 584 | using Base::m_isInitialized;
|
---|
| 585 | using Base::m_info;
|
---|
| 586 |
|
---|
| 587 | void init()
|
---|
| 588 | {
|
---|
| 589 | Base::init();
|
---|
| 590 |
|
---|
| 591 | set_default_options(&this->m_sluOptions);
|
---|
| 592 | m_sluOptions.PrintStat = NO;
|
---|
| 593 | m_sluOptions.ConditionNumber = NO;
|
---|
| 594 | m_sluOptions.Trans = NOTRANS;
|
---|
| 595 | m_sluOptions.ColPerm = COLAMD;
|
---|
| 596 | }
|
---|
| 597 |
|
---|
| 598 |
|
---|
| 599 | private:
|
---|
| 600 | SuperLU(SuperLU& ) { }
|
---|
| 601 | };
|
---|
| 602 |
|
---|
| 603 | template<typename MatrixType>
|
---|
| 604 | void SuperLU<MatrixType>::factorize(const MatrixType& a)
|
---|
| 605 | {
|
---|
| 606 | eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
|
---|
| 607 | if(!m_analysisIsOk)
|
---|
| 608 | {
|
---|
| 609 | m_info = InvalidInput;
|
---|
| 610 | return;
|
---|
| 611 | }
|
---|
| 612 |
|
---|
| 613 | this->initFactorization(a);
|
---|
| 614 |
|
---|
| 615 | m_sluOptions.ColPerm = COLAMD;
|
---|
| 616 | int info = 0;
|
---|
| 617 | RealScalar recip_pivot_growth, rcond;
|
---|
| 618 | RealScalar ferr, berr;
|
---|
| 619 |
|
---|
| 620 | StatInit(&m_sluStat);
|
---|
| 621 | SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
|
---|
| 622 | &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
|
---|
| 623 | &m_sluL, &m_sluU,
|
---|
| 624 | NULL, 0,
|
---|
| 625 | &m_sluB, &m_sluX,
|
---|
| 626 | &recip_pivot_growth, &rcond,
|
---|
| 627 | &ferr, &berr,
|
---|
| 628 | &m_sluStat, &info, Scalar());
|
---|
| 629 | StatFree(&m_sluStat);
|
---|
| 630 |
|
---|
| 631 | m_extractedDataAreDirty = true;
|
---|
| 632 |
|
---|
| 633 | // FIXME how to better check for errors ???
|
---|
| 634 | m_info = info == 0 ? Success : NumericalIssue;
|
---|
| 635 | m_factorizationIsOk = true;
|
---|
| 636 | }
|
---|
| 637 |
|
---|
| 638 | template<typename MatrixType>
|
---|
| 639 | template<typename Rhs,typename Dest>
|
---|
| 640 | void SuperLU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
|
---|
| 641 | {
|
---|
| 642 | eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
|
---|
| 643 |
|
---|
| 644 | const int size = m_matrix.rows();
|
---|
| 645 | const int rhsCols = b.cols();
|
---|
| 646 | eigen_assert(size==b.rows());
|
---|
| 647 |
|
---|
| 648 | m_sluOptions.Trans = NOTRANS;
|
---|
| 649 | m_sluOptions.Fact = FACTORED;
|
---|
| 650 | m_sluOptions.IterRefine = NOREFINE;
|
---|
| 651 |
|
---|
| 652 |
|
---|
| 653 | m_sluFerr.resize(rhsCols);
|
---|
| 654 | m_sluBerr.resize(rhsCols);
|
---|
| 655 | m_sluB = SluMatrix::Map(b.const_cast_derived());
|
---|
| 656 | m_sluX = SluMatrix::Map(x.derived());
|
---|
| 657 |
|
---|
| 658 | typename Rhs::PlainObject b_cpy;
|
---|
| 659 | if(m_sluEqued!='N')
|
---|
| 660 | {
|
---|
| 661 | b_cpy = b;
|
---|
| 662 | m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());
|
---|
| 663 | }
|
---|
| 664 |
|
---|
| 665 | StatInit(&m_sluStat);
|
---|
| 666 | int info = 0;
|
---|
| 667 | RealScalar recip_pivot_growth, rcond;
|
---|
| 668 | SuperLU_gssvx(&m_sluOptions, &m_sluA,
|
---|
| 669 | m_q.data(), m_p.data(),
|
---|
| 670 | &m_sluEtree[0], &m_sluEqued,
|
---|
| 671 | &m_sluRscale[0], &m_sluCscale[0],
|
---|
| 672 | &m_sluL, &m_sluU,
|
---|
| 673 | NULL, 0,
|
---|
| 674 | &m_sluB, &m_sluX,
|
---|
| 675 | &recip_pivot_growth, &rcond,
|
---|
| 676 | &m_sluFerr[0], &m_sluBerr[0],
|
---|
| 677 | &m_sluStat, &info, Scalar());
|
---|
| 678 | StatFree(&m_sluStat);
|
---|
| 679 | m_info = info==0 ? Success : NumericalIssue;
|
---|
| 680 | }
|
---|
| 681 |
|
---|
| 682 | // the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
|
---|
| 683 | //
|
---|
| 684 | // Copyright (c) 1994 by Xerox Corporation. All rights reserved.
|
---|
| 685 | //
|
---|
| 686 | // THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
|
---|
| 687 | // EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK.
|
---|
| 688 | //
|
---|
| 689 | template<typename MatrixType, typename Derived>
|
---|
| 690 | void SuperLUBase<MatrixType,Derived>::extractData() const
|
---|
| 691 | {
|
---|
| 692 | eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
|
---|
| 693 | if (m_extractedDataAreDirty)
|
---|
| 694 | {
|
---|
| 695 | int upper;
|
---|
| 696 | int fsupc, istart, nsupr;
|
---|
| 697 | int lastl = 0, lastu = 0;
|
---|
| 698 | SCformat *Lstore = static_cast<SCformat*>(m_sluL.Store);
|
---|
| 699 | NCformat *Ustore = static_cast<NCformat*>(m_sluU.Store);
|
---|
| 700 | Scalar *SNptr;
|
---|
| 701 |
|
---|
| 702 | const int size = m_matrix.rows();
|
---|
| 703 | m_l.resize(size,size);
|
---|
| 704 | m_l.resizeNonZeros(Lstore->nnz);
|
---|
| 705 | m_u.resize(size,size);
|
---|
| 706 | m_u.resizeNonZeros(Ustore->nnz);
|
---|
| 707 |
|
---|
| 708 | int* Lcol = m_l.outerIndexPtr();
|
---|
| 709 | int* Lrow = m_l.innerIndexPtr();
|
---|
| 710 | Scalar* Lval = m_l.valuePtr();
|
---|
| 711 |
|
---|
| 712 | int* Ucol = m_u.outerIndexPtr();
|
---|
| 713 | int* Urow = m_u.innerIndexPtr();
|
---|
| 714 | Scalar* Uval = m_u.valuePtr();
|
---|
| 715 |
|
---|
| 716 | Ucol[0] = 0;
|
---|
| 717 | Ucol[0] = 0;
|
---|
| 718 |
|
---|
| 719 | /* for each supernode */
|
---|
| 720 | for (int k = 0; k <= Lstore->nsuper; ++k)
|
---|
| 721 | {
|
---|
| 722 | fsupc = L_FST_SUPC(k);
|
---|
| 723 | istart = L_SUB_START(fsupc);
|
---|
| 724 | nsupr = L_SUB_START(fsupc+1) - istart;
|
---|
| 725 | upper = 1;
|
---|
| 726 |
|
---|
| 727 | /* for each column in the supernode */
|
---|
| 728 | for (int j = fsupc; j < L_FST_SUPC(k+1); ++j)
|
---|
| 729 | {
|
---|
| 730 | SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)];
|
---|
| 731 |
|
---|
| 732 | /* Extract U */
|
---|
| 733 | for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i)
|
---|
| 734 | {
|
---|
| 735 | Uval[lastu] = ((Scalar*)Ustore->nzval)[i];
|
---|
| 736 | /* Matlab doesn't like explicit zero. */
|
---|
| 737 | if (Uval[lastu] != 0.0)
|
---|
| 738 | Urow[lastu++] = U_SUB(i);
|
---|
| 739 | }
|
---|
| 740 | for (int i = 0; i < upper; ++i)
|
---|
| 741 | {
|
---|
| 742 | /* upper triangle in the supernode */
|
---|
| 743 | Uval[lastu] = SNptr[i];
|
---|
| 744 | /* Matlab doesn't like explicit zero. */
|
---|
| 745 | if (Uval[lastu] != 0.0)
|
---|
| 746 | Urow[lastu++] = L_SUB(istart+i);
|
---|
| 747 | }
|
---|
| 748 | Ucol[j+1] = lastu;
|
---|
| 749 |
|
---|
| 750 | /* Extract L */
|
---|
| 751 | Lval[lastl] = 1.0; /* unit diagonal */
|
---|
| 752 | Lrow[lastl++] = L_SUB(istart + upper - 1);
|
---|
| 753 | for (int i = upper; i < nsupr; ++i)
|
---|
| 754 | {
|
---|
| 755 | Lval[lastl] = SNptr[i];
|
---|
| 756 | /* Matlab doesn't like explicit zero. */
|
---|
| 757 | if (Lval[lastl] != 0.0)
|
---|
| 758 | Lrow[lastl++] = L_SUB(istart+i);
|
---|
| 759 | }
|
---|
| 760 | Lcol[j+1] = lastl;
|
---|
| 761 |
|
---|
| 762 | ++upper;
|
---|
| 763 | } /* for j ... */
|
---|
| 764 |
|
---|
| 765 | } /* for k ... */
|
---|
| 766 |
|
---|
| 767 | // squeeze the matrices :
|
---|
| 768 | m_l.resizeNonZeros(lastl);
|
---|
| 769 | m_u.resizeNonZeros(lastu);
|
---|
| 770 |
|
---|
| 771 | m_extractedDataAreDirty = false;
|
---|
| 772 | }
|
---|
| 773 | }
|
---|
| 774 |
|
---|
| 775 | template<typename MatrixType>
|
---|
| 776 | typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const
|
---|
| 777 | {
|
---|
| 778 | eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
|
---|
| 779 |
|
---|
| 780 | if (m_extractedDataAreDirty)
|
---|
| 781 | this->extractData();
|
---|
| 782 |
|
---|
| 783 | Scalar det = Scalar(1);
|
---|
| 784 | for (int j=0; j<m_u.cols(); ++j)
|
---|
| 785 | {
|
---|
| 786 | if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0)
|
---|
| 787 | {
|
---|
| 788 | int lastId = m_u.outerIndexPtr()[j+1]-1;
|
---|
| 789 | eigen_assert(m_u.innerIndexPtr()[lastId]<=j);
|
---|
| 790 | if (m_u.innerIndexPtr()[lastId]==j)
|
---|
| 791 | det *= m_u.valuePtr()[lastId];
|
---|
| 792 | }
|
---|
| 793 | }
|
---|
| 794 | if(m_sluEqued!='N')
|
---|
| 795 | return det/m_sluRscale.prod()/m_sluCscale.prod();
|
---|
| 796 | else
|
---|
| 797 | return det;
|
---|
| 798 | }
|
---|
| 799 |
|
---|
| 800 | #ifdef EIGEN_PARSED_BY_DOXYGEN
|
---|
| 801 | #define EIGEN_SUPERLU_HAS_ILU
|
---|
| 802 | #endif
|
---|
| 803 |
|
---|
| 804 | #ifdef EIGEN_SUPERLU_HAS_ILU
|
---|
| 805 |
|
---|
| 806 | /** \ingroup SuperLUSupport_Module
|
---|
| 807 | * \class SuperILU
|
---|
| 808 | * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
|
---|
| 809 | *
|
---|
| 810 | * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization
|
---|
| 811 | * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers.
|
---|
| 812 | *
|
---|
| 813 | * \warning This class requires SuperLU 4 or later.
|
---|
| 814 | *
|
---|
| 815 | * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
|
---|
| 816 | *
|
---|
| 817 | * \sa \ref TutorialSparseDirectSolvers, class ConjugateGradient, class BiCGSTAB
|
---|
| 818 | */
|
---|
| 819 |
|
---|
| 820 | template<typename _MatrixType>
|
---|
| 821 | class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> >
|
---|
| 822 | {
|
---|
| 823 | public:
|
---|
| 824 | typedef SuperLUBase<_MatrixType,SuperILU> Base;
|
---|
| 825 | typedef _MatrixType MatrixType;
|
---|
| 826 | typedef typename Base::Scalar Scalar;
|
---|
| 827 | typedef typename Base::RealScalar RealScalar;
|
---|
| 828 | typedef typename Base::Index Index;
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| 829 |
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| 830 | public:
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| 831 |
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| 832 | SuperILU() : Base() { init(); }
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| 833 |
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| 834 | SuperILU(const MatrixType& matrix) : Base()
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| 835 | {
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| 836 | init();
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| 837 | Base::compute(matrix);
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| 838 | }
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| 839 |
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| 840 | ~SuperILU()
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| 841 | {
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| 842 | }
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| 843 |
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| 844 | /** Performs a symbolic decomposition on the sparcity of \a matrix.
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| 845 | *
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| 846 | * This function is particularly useful when solving for several problems having the same structure.
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| 847 | *
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| 848 | * \sa factorize()
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| 849 | */
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| 850 | void analyzePattern(const MatrixType& matrix)
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| 851 | {
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| 852 | Base::analyzePattern(matrix);
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| 853 | }
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| 854 |
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| 855 | /** Performs a numeric decomposition of \a matrix
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| 856 | *
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| 857 | * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
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| 858 | *
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| 859 | * \sa analyzePattern()
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| 860 | */
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| 861 | void factorize(const MatrixType& matrix);
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| 862 |
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| 863 | #ifndef EIGEN_PARSED_BY_DOXYGEN
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| 864 | /** \internal */
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| 865 | template<typename Rhs,typename Dest>
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| 866 | void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
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| 867 | #endif // EIGEN_PARSED_BY_DOXYGEN
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| 868 |
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| 869 | protected:
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| 870 |
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| 871 | using Base::m_matrix;
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| 872 | using Base::m_sluOptions;
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| 873 | using Base::m_sluA;
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| 874 | using Base::m_sluB;
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| 875 | using Base::m_sluX;
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| 876 | using Base::m_p;
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| 877 | using Base::m_q;
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| 878 | using Base::m_sluEtree;
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| 879 | using Base::m_sluEqued;
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| 880 | using Base::m_sluRscale;
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| 881 | using Base::m_sluCscale;
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| 882 | using Base::m_sluL;
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| 883 | using Base::m_sluU;
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| 884 | using Base::m_sluStat;
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| 885 | using Base::m_sluFerr;
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| 886 | using Base::m_sluBerr;
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| 887 | using Base::m_l;
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| 888 | using Base::m_u;
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| 889 |
|
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| 890 | using Base::m_analysisIsOk;
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| 891 | using Base::m_factorizationIsOk;
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| 892 | using Base::m_extractedDataAreDirty;
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| 893 | using Base::m_isInitialized;
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| 894 | using Base::m_info;
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| 895 |
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| 896 | void init()
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| 897 | {
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| 898 | Base::init();
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| 899 |
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| 900 | ilu_set_default_options(&m_sluOptions);
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| 901 | m_sluOptions.PrintStat = NO;
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| 902 | m_sluOptions.ConditionNumber = NO;
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| 903 | m_sluOptions.Trans = NOTRANS;
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| 904 | m_sluOptions.ColPerm = MMD_AT_PLUS_A;
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| 905 |
|
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| 906 | // no attempt to preserve column sum
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| 907 | m_sluOptions.ILU_MILU = SILU;
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| 908 | // only basic ILU(k) support -- no direct control over memory consumption
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---|
| 909 | // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
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| 910 | // and set ILU_FillFactor to max memory growth
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---|
| 911 | m_sluOptions.ILU_DropRule = DROP_BASIC;
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| 912 | m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10;
|
---|
| 913 | }
|
---|
| 914 |
|
---|
| 915 | private:
|
---|
| 916 | SuperILU(SuperILU& ) { }
|
---|
| 917 | };
|
---|
| 918 |
|
---|
| 919 | template<typename MatrixType>
|
---|
| 920 | void SuperILU<MatrixType>::factorize(const MatrixType& a)
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| 921 | {
|
---|
| 922 | eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
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| 923 | if(!m_analysisIsOk)
|
---|
| 924 | {
|
---|
| 925 | m_info = InvalidInput;
|
---|
| 926 | return;
|
---|
| 927 | }
|
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| 928 |
|
---|
| 929 | this->initFactorization(a);
|
---|
| 930 |
|
---|
| 931 | int info = 0;
|
---|
| 932 | RealScalar recip_pivot_growth, rcond;
|
---|
| 933 |
|
---|
| 934 | StatInit(&m_sluStat);
|
---|
| 935 | SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
|
---|
| 936 | &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
|
---|
| 937 | &m_sluL, &m_sluU,
|
---|
| 938 | NULL, 0,
|
---|
| 939 | &m_sluB, &m_sluX,
|
---|
| 940 | &recip_pivot_growth, &rcond,
|
---|
| 941 | &m_sluStat, &info, Scalar());
|
---|
| 942 | StatFree(&m_sluStat);
|
---|
| 943 |
|
---|
| 944 | // FIXME how to better check for errors ???
|
---|
| 945 | m_info = info == 0 ? Success : NumericalIssue;
|
---|
| 946 | m_factorizationIsOk = true;
|
---|
| 947 | }
|
---|
| 948 |
|
---|
| 949 | template<typename MatrixType>
|
---|
| 950 | template<typename Rhs,typename Dest>
|
---|
| 951 | void SuperILU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
|
---|
| 952 | {
|
---|
| 953 | eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
|
---|
| 954 |
|
---|
| 955 | const int size = m_matrix.rows();
|
---|
| 956 | const int rhsCols = b.cols();
|
---|
| 957 | eigen_assert(size==b.rows());
|
---|
| 958 |
|
---|
| 959 | m_sluOptions.Trans = NOTRANS;
|
---|
| 960 | m_sluOptions.Fact = FACTORED;
|
---|
| 961 | m_sluOptions.IterRefine = NOREFINE;
|
---|
| 962 |
|
---|
| 963 | m_sluFerr.resize(rhsCols);
|
---|
| 964 | m_sluBerr.resize(rhsCols);
|
---|
| 965 | m_sluB = SluMatrix::Map(b.const_cast_derived());
|
---|
| 966 | m_sluX = SluMatrix::Map(x.derived());
|
---|
| 967 |
|
---|
| 968 | typename Rhs::PlainObject b_cpy;
|
---|
| 969 | if(m_sluEqued!='N')
|
---|
| 970 | {
|
---|
| 971 | b_cpy = b;
|
---|
| 972 | m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());
|
---|
| 973 | }
|
---|
| 974 |
|
---|
| 975 | int info = 0;
|
---|
| 976 | RealScalar recip_pivot_growth, rcond;
|
---|
| 977 |
|
---|
| 978 | StatInit(&m_sluStat);
|
---|
| 979 | SuperLU_gsisx(&m_sluOptions, &m_sluA,
|
---|
| 980 | m_q.data(), m_p.data(),
|
---|
| 981 | &m_sluEtree[0], &m_sluEqued,
|
---|
| 982 | &m_sluRscale[0], &m_sluCscale[0],
|
---|
| 983 | &m_sluL, &m_sluU,
|
---|
| 984 | NULL, 0,
|
---|
| 985 | &m_sluB, &m_sluX,
|
---|
| 986 | &recip_pivot_growth, &rcond,
|
---|
| 987 | &m_sluStat, &info, Scalar());
|
---|
| 988 | StatFree(&m_sluStat);
|
---|
| 989 |
|
---|
| 990 | m_info = info==0 ? Success : NumericalIssue;
|
---|
| 991 | }
|
---|
| 992 | #endif
|
---|
| 993 |
|
---|
| 994 | namespace internal {
|
---|
| 995 |
|
---|
| 996 | template<typename _MatrixType, typename Derived, typename Rhs>
|
---|
| 997 | struct solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs>
|
---|
| 998 | : solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs>
|
---|
| 999 | {
|
---|
| 1000 | typedef SuperLUBase<_MatrixType,Derived> Dec;
|
---|
| 1001 | EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
|
---|
| 1002 |
|
---|
| 1003 | template<typename Dest> void evalTo(Dest& dst) const
|
---|
| 1004 | {
|
---|
| 1005 | dec().derived()._solve(rhs(),dst);
|
---|
| 1006 | }
|
---|
| 1007 | };
|
---|
| 1008 |
|
---|
| 1009 | template<typename _MatrixType, typename Derived, typename Rhs>
|
---|
| 1010 | struct sparse_solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs>
|
---|
| 1011 | : sparse_solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs>
|
---|
| 1012 | {
|
---|
| 1013 | typedef SuperLUBase<_MatrixType,Derived> Dec;
|
---|
| 1014 | EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
|
---|
| 1015 |
|
---|
| 1016 | template<typename Dest> void evalTo(Dest& dst) const
|
---|
| 1017 | {
|
---|
| 1018 | this->defaultEvalTo(dst);
|
---|
| 1019 | }
|
---|
| 1020 | };
|
---|
| 1021 |
|
---|
| 1022 | } // end namespace internal
|
---|
| 1023 |
|
---|
| 1024 | } // end namespace Eigen
|
---|
| 1025 |
|
---|
| 1026 | #endif // EIGEN_SUPERLUSUPPORT_H
|
---|