Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1 | // This file is part of Eigen, a lightweight C++ template library |
| 2 | // for linear algebra. |
| 3 | // |
| 4 | // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> |
| 5 | // |
| 6 | // This Source Code Form is subject to the terms of the Mozilla |
| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed |
| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| 9 | |
| 10 | #ifndef EIGEN_UMFPACKSUPPORT_H |
| 11 | #define EIGEN_UMFPACKSUPPORT_H |
| 12 | |
| 13 | namespace Eigen { |
| 14 | |
| 15 | /* TODO extract L, extract U, compute det, etc... */ |
| 16 | |
| 17 | // generic double/complex<double> wrapper functions: |
| 18 | |
| 19 | inline void umfpack_free_numeric(void **Numeric, double) |
| 20 | { umfpack_di_free_numeric(Numeric); *Numeric = 0; } |
| 21 | |
| 22 | inline void umfpack_free_numeric(void **Numeric, std::complex<double>) |
| 23 | { umfpack_zi_free_numeric(Numeric); *Numeric = 0; } |
| 24 | |
| 25 | inline void umfpack_free_symbolic(void **Symbolic, double) |
| 26 | { umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; } |
| 27 | |
| 28 | inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>) |
| 29 | { umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; } |
| 30 | |
| 31 | inline int umfpack_symbolic(int n_row,int n_col, |
| 32 | const int Ap[], const int Ai[], const double Ax[], void **Symbolic, |
| 33 | const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) |
| 34 | { |
| 35 | return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info); |
| 36 | } |
| 37 | |
| 38 | inline int umfpack_symbolic(int n_row,int n_col, |
| 39 | const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic, |
| 40 | const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) |
| 41 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 42 | return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 43 | } |
| 44 | |
| 45 | inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[], |
| 46 | void *Symbolic, void **Numeric, |
| 47 | const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) |
| 48 | { |
| 49 | return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info); |
| 50 | } |
| 51 | |
| 52 | inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[], |
| 53 | void *Symbolic, void **Numeric, |
| 54 | const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) |
| 55 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 56 | return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 57 | } |
| 58 | |
| 59 | inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[], |
| 60 | double X[], const double B[], void *Numeric, |
| 61 | const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) |
| 62 | { |
| 63 | return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info); |
| 64 | } |
| 65 | |
| 66 | inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[], |
| 67 | std::complex<double> X[], const std::complex<double> B[], void *Numeric, |
| 68 | const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) |
| 69 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 70 | return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 71 | } |
| 72 | |
| 73 | inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double) |
| 74 | { |
| 75 | return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); |
| 76 | } |
| 77 | |
| 78 | inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>) |
| 79 | { |
| 80 | return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); |
| 81 | } |
| 82 | |
| 83 | inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], |
| 84 | int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric) |
| 85 | { |
| 86 | return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric); |
| 87 | } |
| 88 | |
| 89 | inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[], |
| 90 | int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric) |
| 91 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 92 | double& lx0_real = numext::real_ref(Lx[0]); |
| 93 | double& ux0_real = numext::real_ref(Ux[0]); |
| 94 | double& dx0_real = numext::real_ref(Dx[0]); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 95 | return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q, |
| 96 | Dx?&dx0_real:0,0,do_recip,Rs,Numeric); |
| 97 | } |
| 98 | |
| 99 | inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) |
| 100 | { |
| 101 | return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info); |
| 102 | } |
| 103 | |
| 104 | inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) |
| 105 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 106 | double& mx_real = numext::real_ref(*Mx); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 107 | return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info); |
| 108 | } |
| 109 | |
| 110 | /** \ingroup UmfPackSupport_Module |
| 111 | * \brief A sparse LU factorization and solver based on UmfPack |
| 112 | * |
| 113 | * This class allows to solve for A.X = B sparse linear problems via a LU factorization |
| 114 | * using the UmfPack library. The sparse matrix A must be squared and full rank. |
| 115 | * The vectors or matrices X and B can be either dense or sparse. |
| 116 | * |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 117 | * \warning The input matrix A should be in a \b compressed and \b column-major form. |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 118 | * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix. |
| 119 | * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> |
| 120 | * |
| 121 | * \sa \ref TutorialSparseDirectSolvers |
| 122 | */ |
| 123 | template<typename _MatrixType> |
| 124 | class UmfPackLU : internal::noncopyable |
| 125 | { |
| 126 | public: |
| 127 | typedef _MatrixType MatrixType; |
| 128 | typedef typename MatrixType::Scalar Scalar; |
| 129 | typedef typename MatrixType::RealScalar RealScalar; |
| 130 | typedef typename MatrixType::Index Index; |
| 131 | typedef Matrix<Scalar,Dynamic,1> Vector; |
| 132 | typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType; |
| 133 | typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType; |
| 134 | typedef SparseMatrix<Scalar> LUMatrixType; |
| 135 | typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType; |
| 136 | |
| 137 | public: |
| 138 | |
| 139 | UmfPackLU() { init(); } |
| 140 | |
| 141 | UmfPackLU(const MatrixType& matrix) |
| 142 | { |
| 143 | init(); |
| 144 | compute(matrix); |
| 145 | } |
| 146 | |
| 147 | ~UmfPackLU() |
| 148 | { |
| 149 | if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar()); |
| 150 | if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); |
| 151 | } |
| 152 | |
| 153 | inline Index rows() const { return m_copyMatrix.rows(); } |
| 154 | inline Index cols() const { return m_copyMatrix.cols(); } |
| 155 | |
| 156 | /** \brief Reports whether previous computation was successful. |
| 157 | * |
| 158 | * \returns \c Success if computation was succesful, |
| 159 | * \c NumericalIssue if the matrix.appears to be negative. |
| 160 | */ |
| 161 | ComputationInfo info() const |
| 162 | { |
| 163 | eigen_assert(m_isInitialized && "Decomposition is not initialized."); |
| 164 | return m_info; |
| 165 | } |
| 166 | |
| 167 | inline const LUMatrixType& matrixL() const |
| 168 | { |
| 169 | if (m_extractedDataAreDirty) extractData(); |
| 170 | return m_l; |
| 171 | } |
| 172 | |
| 173 | inline const LUMatrixType& matrixU() const |
| 174 | { |
| 175 | if (m_extractedDataAreDirty) extractData(); |
| 176 | return m_u; |
| 177 | } |
| 178 | |
| 179 | inline const IntColVectorType& permutationP() const |
| 180 | { |
| 181 | if (m_extractedDataAreDirty) extractData(); |
| 182 | return m_p; |
| 183 | } |
| 184 | |
| 185 | inline const IntRowVectorType& permutationQ() const |
| 186 | { |
| 187 | if (m_extractedDataAreDirty) extractData(); |
| 188 | return m_q; |
| 189 | } |
| 190 | |
| 191 | /** Computes the sparse Cholesky decomposition of \a matrix |
| 192 | * Note that the matrix should be column-major, and in compressed format for best performance. |
| 193 | * \sa SparseMatrix::makeCompressed(). |
| 194 | */ |
| 195 | void compute(const MatrixType& matrix) |
| 196 | { |
| 197 | analyzePattern(matrix); |
| 198 | factorize(matrix); |
| 199 | } |
| 200 | |
| 201 | /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. |
| 202 | * |
| 203 | * \sa compute() |
| 204 | */ |
| 205 | template<typename Rhs> |
| 206 | inline const internal::solve_retval<UmfPackLU, Rhs> solve(const MatrixBase<Rhs>& b) const |
| 207 | { |
| 208 | eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); |
| 209 | eigen_assert(rows()==b.rows() |
| 210 | && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); |
| 211 | return internal::solve_retval<UmfPackLU, Rhs>(*this, b.derived()); |
| 212 | } |
| 213 | |
| 214 | /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. |
| 215 | * |
| 216 | * \sa compute() |
| 217 | */ |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 218 | template<typename Rhs> |
| 219 | inline const internal::sparse_solve_retval<UmfPackLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const |
| 220 | { |
| 221 | eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); |
| 222 | eigen_assert(rows()==b.rows() |
| 223 | && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); |
| 224 | return internal::sparse_solve_retval<UmfPackLU, Rhs>(*this, b.derived()); |
| 225 | } |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 226 | |
| 227 | /** Performs a symbolic decomposition on the sparcity of \a matrix. |
| 228 | * |
| 229 | * This function is particularly useful when solving for several problems having the same structure. |
| 230 | * |
| 231 | * \sa factorize(), compute() |
| 232 | */ |
| 233 | void analyzePattern(const MatrixType& matrix) |
| 234 | { |
| 235 | if(m_symbolic) |
| 236 | umfpack_free_symbolic(&m_symbolic,Scalar()); |
| 237 | if(m_numeric) |
| 238 | umfpack_free_numeric(&m_numeric,Scalar()); |
| 239 | |
| 240 | grapInput(matrix); |
| 241 | |
| 242 | int errorCode = 0; |
| 243 | errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, |
| 244 | &m_symbolic, 0, 0); |
| 245 | |
| 246 | m_isInitialized = true; |
| 247 | m_info = errorCode ? InvalidInput : Success; |
| 248 | m_analysisIsOk = true; |
| 249 | m_factorizationIsOk = false; |
| 250 | } |
| 251 | |
| 252 | /** Performs a numeric decomposition of \a matrix |
| 253 | * |
| 254 | * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed. |
| 255 | * |
| 256 | * \sa analyzePattern(), compute() |
| 257 | */ |
| 258 | void factorize(const MatrixType& matrix) |
| 259 | { |
| 260 | eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()"); |
| 261 | if(m_numeric) |
| 262 | umfpack_free_numeric(&m_numeric,Scalar()); |
| 263 | |
| 264 | grapInput(matrix); |
| 265 | |
| 266 | int errorCode; |
| 267 | errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, |
| 268 | m_symbolic, &m_numeric, 0, 0); |
| 269 | |
| 270 | m_info = errorCode ? NumericalIssue : Success; |
| 271 | m_factorizationIsOk = true; |
| 272 | } |
| 273 | |
| 274 | #ifndef EIGEN_PARSED_BY_DOXYGEN |
| 275 | /** \internal */ |
| 276 | template<typename BDerived,typename XDerived> |
| 277 | bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const; |
| 278 | #endif |
| 279 | |
| 280 | Scalar determinant() const; |
| 281 | |
| 282 | void extractData() const; |
| 283 | |
| 284 | protected: |
| 285 | |
| 286 | |
| 287 | void init() |
| 288 | { |
| 289 | m_info = InvalidInput; |
| 290 | m_isInitialized = false; |
| 291 | m_numeric = 0; |
| 292 | m_symbolic = 0; |
| 293 | m_outerIndexPtr = 0; |
| 294 | m_innerIndexPtr = 0; |
| 295 | m_valuePtr = 0; |
| 296 | } |
| 297 | |
| 298 | void grapInput(const MatrixType& mat) |
| 299 | { |
| 300 | m_copyMatrix.resize(mat.rows(), mat.cols()); |
| 301 | if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() ) |
| 302 | { |
| 303 | // non supported input -> copy |
| 304 | m_copyMatrix = mat; |
| 305 | m_outerIndexPtr = m_copyMatrix.outerIndexPtr(); |
| 306 | m_innerIndexPtr = m_copyMatrix.innerIndexPtr(); |
| 307 | m_valuePtr = m_copyMatrix.valuePtr(); |
| 308 | } |
| 309 | else |
| 310 | { |
| 311 | m_outerIndexPtr = mat.outerIndexPtr(); |
| 312 | m_innerIndexPtr = mat.innerIndexPtr(); |
| 313 | m_valuePtr = mat.valuePtr(); |
| 314 | } |
| 315 | } |
| 316 | |
| 317 | // cached data to reduce reallocation, etc. |
| 318 | mutable LUMatrixType m_l; |
| 319 | mutable LUMatrixType m_u; |
| 320 | mutable IntColVectorType m_p; |
| 321 | mutable IntRowVectorType m_q; |
| 322 | |
| 323 | UmfpackMatrixType m_copyMatrix; |
| 324 | const Scalar* m_valuePtr; |
| 325 | const int* m_outerIndexPtr; |
| 326 | const int* m_innerIndexPtr; |
| 327 | void* m_numeric; |
| 328 | void* m_symbolic; |
| 329 | |
| 330 | mutable ComputationInfo m_info; |
| 331 | bool m_isInitialized; |
| 332 | int m_factorizationIsOk; |
| 333 | int m_analysisIsOk; |
| 334 | mutable bool m_extractedDataAreDirty; |
| 335 | |
| 336 | private: |
| 337 | UmfPackLU(UmfPackLU& ) { } |
| 338 | }; |
| 339 | |
| 340 | |
| 341 | template<typename MatrixType> |
| 342 | void UmfPackLU<MatrixType>::extractData() const |
| 343 | { |
| 344 | if (m_extractedDataAreDirty) |
| 345 | { |
| 346 | // get size of the data |
| 347 | int lnz, unz, rows, cols, nz_udiag; |
| 348 | umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar()); |
| 349 | |
| 350 | // allocate data |
| 351 | m_l.resize(rows,(std::min)(rows,cols)); |
| 352 | m_l.resizeNonZeros(lnz); |
| 353 | |
| 354 | m_u.resize((std::min)(rows,cols),cols); |
| 355 | m_u.resizeNonZeros(unz); |
| 356 | |
| 357 | m_p.resize(rows); |
| 358 | m_q.resize(cols); |
| 359 | |
| 360 | // extract |
| 361 | umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(), |
| 362 | m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(), |
| 363 | m_p.data(), m_q.data(), 0, 0, 0, m_numeric); |
| 364 | |
| 365 | m_extractedDataAreDirty = false; |
| 366 | } |
| 367 | } |
| 368 | |
| 369 | template<typename MatrixType> |
| 370 | typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const |
| 371 | { |
| 372 | Scalar det; |
| 373 | umfpack_get_determinant(&det, 0, m_numeric, 0); |
| 374 | return det; |
| 375 | } |
| 376 | |
| 377 | template<typename MatrixType> |
| 378 | template<typename BDerived,typename XDerived> |
| 379 | bool UmfPackLU<MatrixType>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const |
| 380 | { |
| 381 | const int rhsCols = b.cols(); |
| 382 | eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet"); |
| 383 | eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet"); |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 384 | eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve"); |
| 385 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 386 | int errorCode; |
| 387 | for (int j=0; j<rhsCols; ++j) |
| 388 | { |
| 389 | errorCode = umfpack_solve(UMFPACK_A, |
| 390 | m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, |
| 391 | &x.col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0); |
| 392 | if (errorCode!=0) |
| 393 | return false; |
| 394 | } |
| 395 | |
| 396 | return true; |
| 397 | } |
| 398 | |
| 399 | |
| 400 | namespace internal { |
| 401 | |
| 402 | template<typename _MatrixType, typename Rhs> |
| 403 | struct solve_retval<UmfPackLU<_MatrixType>, Rhs> |
| 404 | : solve_retval_base<UmfPackLU<_MatrixType>, Rhs> |
| 405 | { |
| 406 | typedef UmfPackLU<_MatrixType> Dec; |
| 407 | EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) |
| 408 | |
| 409 | template<typename Dest> void evalTo(Dest& dst) const |
| 410 | { |
| 411 | dec()._solve(rhs(),dst); |
| 412 | } |
| 413 | }; |
| 414 | |
| 415 | template<typename _MatrixType, typename Rhs> |
| 416 | struct sparse_solve_retval<UmfPackLU<_MatrixType>, Rhs> |
| 417 | : sparse_solve_retval_base<UmfPackLU<_MatrixType>, Rhs> |
| 418 | { |
| 419 | typedef UmfPackLU<_MatrixType> Dec; |
| 420 | EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) |
| 421 | |
| 422 | template<typename Dest> void evalTo(Dest& dst) const |
| 423 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 424 | this->defaultEvalTo(dst); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 425 | } |
| 426 | }; |
| 427 | |
| 428 | } // end namespace internal |
| 429 | |
| 430 | } // end namespace Eigen |
| 431 | |
| 432 | #endif // EIGEN_UMFPACKSUPPORT_H |