Eigen  3.2.92
CholmodSupport.h
1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2010 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_CHOLMODSUPPORT_H
11 #define EIGEN_CHOLMODSUPPORT_H
12 
13 namespace Eigen {
14 
15 namespace internal {
16 
17 template<typename Scalar, typename CholmodType>
18 void cholmod_configure_matrix(CholmodType& mat)
19 {
20  if (internal::is_same<Scalar,float>::value)
21  {
22  mat.xtype = CHOLMOD_REAL;
23  mat.dtype = CHOLMOD_SINGLE;
24  }
25  else if (internal::is_same<Scalar,double>::value)
26  {
27  mat.xtype = CHOLMOD_REAL;
28  mat.dtype = CHOLMOD_DOUBLE;
29  }
30  else if (internal::is_same<Scalar,std::complex<float> >::value)
31  {
32  mat.xtype = CHOLMOD_COMPLEX;
33  mat.dtype = CHOLMOD_SINGLE;
34  }
35  else if (internal::is_same<Scalar,std::complex<double> >::value)
36  {
37  mat.xtype = CHOLMOD_COMPLEX;
38  mat.dtype = CHOLMOD_DOUBLE;
39  }
40  else
41  {
42  eigen_assert(false && "Scalar type not supported by CHOLMOD");
43  }
44 }
45 
46 } // namespace internal
47 
51 template<typename _Scalar, int _Options, typename _StorageIndex>
52 cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_StorageIndex>& mat)
53 {
54  cholmod_sparse res;
55  res.nzmax = mat.nonZeros();
56  res.nrow = mat.rows();;
57  res.ncol = mat.cols();
58  res.p = mat.outerIndexPtr();
59  res.i = mat.innerIndexPtr();
60  res.x = mat.valuePtr();
61  res.z = 0;
62  res.sorted = 1;
63  if(mat.isCompressed())
64  {
65  res.packed = 1;
66  res.nz = 0;
67  }
68  else
69  {
70  res.packed = 0;
71  res.nz = mat.innerNonZeroPtr();
72  }
73 
74  res.dtype = 0;
75  res.stype = -1;
76 
77  if (internal::is_same<_StorageIndex,int>::value)
78  {
79  res.itype = CHOLMOD_INT;
80  }
81  else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
82  {
83  res.itype = CHOLMOD_LONG;
84  }
85  else
86  {
87  eigen_assert(false && "Index type not supported yet");
88  }
89 
90  // setup res.xtype
91  internal::cholmod_configure_matrix<_Scalar>(res);
92 
93  res.stype = 0;
94 
95  return res;
96 }
97 
98 template<typename _Scalar, int _Options, typename _Index>
99 const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
100 {
101  cholmod_sparse res = viewAsCholmod(mat.const_cast_derived());
102  return res;
103 }
104 
107 template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
108 cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
109 {
110  cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived());
111 
112  if(UpLo==Upper) res.stype = 1;
113  if(UpLo==Lower) res.stype = -1;
114 
115  return res;
116 }
117 
120 template<typename Derived>
121 cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
122 {
123  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
124  typedef typename Derived::Scalar Scalar;
125 
126  cholmod_dense res;
127  res.nrow = mat.rows();
128  res.ncol = mat.cols();
129  res.nzmax = res.nrow * res.ncol;
130  res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
131  res.x = (void*)(mat.derived().data());
132  res.z = 0;
133 
134  internal::cholmod_configure_matrix<Scalar>(res);
135 
136  return res;
137 }
138 
141 template<typename Scalar, int Flags, typename StorageIndex>
142 MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
143 {
144  return MappedSparseMatrix<Scalar,Flags,StorageIndex>
145  (cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
146  static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
147 }
148 
149 enum CholmodMode {
150  CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
151 };
152 
153 
159 template<typename _MatrixType, int _UpLo, typename Derived>
160 class CholmodBase : public SparseSolverBase<Derived>
161 {
162  protected:
164  using Base::derived;
165  using Base::m_isInitialized;
166  public:
167  typedef _MatrixType MatrixType;
168  enum { UpLo = _UpLo };
169  typedef typename MatrixType::Scalar Scalar;
170  typedef typename MatrixType::RealScalar RealScalar;
171  typedef MatrixType CholMatrixType;
172  typedef typename MatrixType::StorageIndex StorageIndex;
173  enum {
174  ColsAtCompileTime = MatrixType::ColsAtCompileTime,
175  MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
176  };
177 
178  public:
179 
180  CholmodBase()
181  : m_cholmodFactor(0), m_info(Success)
182  {
183  m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
184  cholmod_start(&m_cholmod);
185  }
186 
187  explicit CholmodBase(const MatrixType& matrix)
188  : m_cholmodFactor(0), m_info(Success)
189  {
190  m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
191  cholmod_start(&m_cholmod);
192  compute(matrix);
193  }
194 
195  ~CholmodBase()
196  {
197  if(m_cholmodFactor)
198  cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
199  cholmod_finish(&m_cholmod);
200  }
201 
202  inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
203  inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
204 
211  {
212  eigen_assert(m_isInitialized && "Decomposition is not initialized.");
213  return m_info;
214  }
215 
217  Derived& compute(const MatrixType& matrix)
218  {
219  analyzePattern(matrix);
220  factorize(matrix);
221  return derived();
222  }
223 
230  void analyzePattern(const MatrixType& matrix)
231  {
232  if(m_cholmodFactor)
233  {
234  cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
235  m_cholmodFactor = 0;
236  }
237  cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
238  m_cholmodFactor = cholmod_analyze(&A, &m_cholmod);
239 
240  this->m_isInitialized = true;
241  this->m_info = Success;
242  m_analysisIsOk = true;
243  m_factorizationIsOk = false;
244  }
245 
252  void factorize(const MatrixType& matrix)
253  {
254  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
255  cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
256  cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
257 
258  // If the factorization failed, minor is the column at which it did. On success minor == n.
259  this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
260  m_factorizationIsOk = true;
261  }
262 
265  cholmod_common& cholmod() { return m_cholmod; }
266 
267  #ifndef EIGEN_PARSED_BY_DOXYGEN
268 
269  template<typename Rhs,typename Dest>
270  void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
271  {
272  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
273  const Index size = m_cholmodFactor->n;
274  EIGEN_UNUSED_VARIABLE(size);
275  eigen_assert(size==b.rows());
276 
277  // note: cd stands for Cholmod Dense
278  Rhs& b_ref(b.const_cast_derived());
279  cholmod_dense b_cd = viewAsCholmod(b_ref);
280  cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
281  if(!x_cd)
282  {
283  this->m_info = NumericalIssue;
284  return;
285  }
286  // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
287  dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
288  cholmod_free_dense(&x_cd, &m_cholmod);
289  }
290 
292  template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
293  void _solve_impl(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
294  {
295  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
296  const Index size = m_cholmodFactor->n;
297  EIGEN_UNUSED_VARIABLE(size);
298  eigen_assert(size==b.rows());
299 
300  // note: cs stands for Cholmod Sparse
301  cholmod_sparse b_cs = viewAsCholmod(b);
302  cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod);
303  if(!x_cs)
304  {
305  this->m_info = NumericalIssue;
306  return;
307  }
308  // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
309  dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
310  cholmod_free_sparse(&x_cs, &m_cholmod);
311  }
312  #endif // EIGEN_PARSED_BY_DOXYGEN
313 
314 
324  Derived& setShift(const RealScalar& offset)
325  {
326  m_shiftOffset[0] = offset;
327  return derived();
328  }
329 
330  template<typename Stream>
331  void dumpMemory(Stream& /*s*/)
332  {}
333 
334  protected:
335  mutable cholmod_common m_cholmod;
336  cholmod_factor* m_cholmodFactor;
337  RealScalar m_shiftOffset[2];
338  mutable ComputationInfo m_info;
339  int m_factorizationIsOk;
340  int m_analysisIsOk;
341 };
342 
363 template<typename _MatrixType, int _UpLo = Lower>
364 class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
365 {
367  using Base::m_cholmod;
368 
369  public:
370 
371  typedef _MatrixType MatrixType;
372 
373  CholmodSimplicialLLT() : Base() { init(); }
374 
375  CholmodSimplicialLLT(const MatrixType& matrix) : Base()
376  {
377  init();
378  this->compute(matrix);
379  }
380 
382  protected:
383  void init()
384  {
385  m_cholmod.final_asis = 0;
386  m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
387  m_cholmod.final_ll = 1;
388  }
389 };
390 
391 
412 template<typename _MatrixType, int _UpLo = Lower>
413 class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
414 {
416  using Base::m_cholmod;
417 
418  public:
419 
420  typedef _MatrixType MatrixType;
421 
422  CholmodSimplicialLDLT() : Base() { init(); }
423 
424  CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
425  {
426  init();
427  this->compute(matrix);
428  }
429 
431  protected:
432  void init()
433  {
434  m_cholmod.final_asis = 1;
435  m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
436  }
437 };
438 
459 template<typename _MatrixType, int _UpLo = Lower>
460 class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
461 {
463  using Base::m_cholmod;
464 
465  public:
466 
467  typedef _MatrixType MatrixType;
468 
469  CholmodSupernodalLLT() : Base() { init(); }
470 
471  CholmodSupernodalLLT(const MatrixType& matrix) : Base()
472  {
473  init();
474  this->compute(matrix);
475  }
476 
478  protected:
479  void init()
480  {
481  m_cholmod.final_asis = 1;
482  m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
483  }
484 };
485 
508 template<typename _MatrixType, int _UpLo = Lower>
509 class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
510 {
512  using Base::m_cholmod;
513 
514  public:
515 
516  typedef _MatrixType MatrixType;
517 
518  CholmodDecomposition() : Base() { init(); }
519 
520  CholmodDecomposition(const MatrixType& matrix) : Base()
521  {
522  init();
523  this->compute(matrix);
524  }
525 
527 
528  void setMode(CholmodMode mode)
529  {
530  switch(mode)
531  {
532  case CholmodAuto:
533  m_cholmod.final_asis = 1;
534  m_cholmod.supernodal = CHOLMOD_AUTO;
535  break;
536  case CholmodSimplicialLLt:
537  m_cholmod.final_asis = 0;
538  m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
539  m_cholmod.final_ll = 1;
540  break;
541  case CholmodSupernodalLLt:
542  m_cholmod.final_asis = 1;
543  m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
544  break;
545  case CholmodLDLt:
546  m_cholmod.final_asis = 1;
547  m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
548  break;
549  default:
550  break;
551  }
552  }
553  protected:
554  void init()
555  {
556  m_cholmod.final_asis = 1;
557  m_cholmod.supernodal = CHOLMOD_AUTO;
558  }
559 };
560 
561 } // end namespace Eigen
562 
563 #endif // EIGEN_CHOLMODSUPPORT_H
Definition: Constants.h:206
void factorize(const MatrixType &matrix)
Definition: CholmodSupport.h:252
A base class for sparse solvers.
Definition: SparseSolverBase.h:53
Definition: LDLT.h:16
A supernodal Cholesky (LLT) factorization and solver based on Cholmod.
Definition: CholmodSupport.h:460
const unsigned int RowMajorBit
Definition: Constants.h:61
cholmod_common & cholmod()
Definition: CholmodSupport.h:265
Definition: Constants.h:434
void analyzePattern(const MatrixType &matrix)
Definition: CholmodSupport.h:230
A general Cholesky factorization and solver based on Cholmod.
Definition: CholmodSupport.h:509
The base class for the direct Cholesky factorization of Cholmod.
Definition: CholmodSupport.h:160
Definition: Constants.h:432
ComputationInfo info() const
Reports whether previous computation was successful.
Definition: CholmodSupport.h:210
A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod.
Definition: CholmodSupport.h:364
Definition: Constants.h:204
Definition: Eigen_Colamd.h:54
A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod.
Definition: CholmodSupport.h:413
Derived & compute(const MatrixType &matrix)
Definition: CholmodSupport.h:217
Derived & setShift(const RealScalar &offset)
Definition: CholmodSupport.h:324
ComputationInfo
Definition: Constants.h:430
Base class for all dense matrices, vectors, and expressions.
Definition: MatrixBase.h:48