1// Ceres Solver - A fast non-linear least squares minimizer
2// Copyright 2012 Google Inc. All rights reserved.
3// http://code.google.com/p/ceres-solver/
4//
5// Redistribution and use in source and binary forms, with or without
6// modification, are permitted provided that the following conditions are met:
7//
8// * Redistributions of source code must retain the above copyright notice,
9//   this list of conditions and the following disclaimer.
10// * Redistributions in binary form must reproduce the above copyright notice,
11//   this list of conditions and the following disclaimer in the documentation
12//   and/or other materials provided with the distribution.
13// * Neither the name of Google Inc. nor the names of its contributors may be
14//   used to endorse or promote products derived from this software without
15//   specific prior written permission.
16//
17// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
18// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
19// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
20// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
22// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
23// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
24// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
25// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
26// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
27// POSSIBILITY OF SUCH DAMAGE.
28//
29// Author: strandmark@google.com (Petter Strandmark)
30
31#ifndef CERES_INTERNAL_CXSPARSE_H_
32#define CERES_INTERNAL_CXSPARSE_H_
33
34// This include must come before any #ifndef check on Ceres compile options.
35#include "ceres/internal/port.h"
36
37#ifndef CERES_NO_CXSPARSE
38
39#include <vector>
40#include "cs.h"
41
42namespace ceres {
43namespace internal {
44
45class CompressedRowSparseMatrix;
46class TripletSparseMatrix;
47
48// This object provides access to solving linear systems using Cholesky
49// factorization with a known symbolic factorization. This features does not
50// explicity exist in CXSparse. The methods in the class are nonstatic because
51// the class manages internal scratch space.
52class CXSparse {
53 public:
54  CXSparse();
55  ~CXSparse();
56
57  // Solves a symmetric linear system A * x = b using Cholesky factorization.
58  //  A                      - The system matrix.
59  //  symbolic_factorization - The symbolic factorization of A. This is obtained
60  //                           from AnalyzeCholesky.
61  //  b                      - The right hand size of the linear equation. This
62  //                           array will also recieve the solution.
63  // Returns false if Cholesky factorization of A fails.
64  bool SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b);
65
66  // Creates a sparse matrix from a compressed-column form. No memory is
67  // allocated or copied; the structure A is filled out with info from the
68  // argument.
69  cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
70
71  // Creates a new matrix from a triplet form. Deallocate the returned matrix
72  // with Free. May return NULL if the compression or allocation fails.
73  cs_di* CreateSparseMatrix(TripletSparseMatrix* A);
74
75  // B = A'
76  //
77  // The returned matrix should be deallocated with Free when not used
78  // anymore.
79  cs_di* TransposeMatrix(cs_di* A);
80
81  // C = A * B
82  //
83  // The returned matrix should be deallocated with Free when not used
84  // anymore.
85  cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);
86
87  // Computes a symbolic factorization of A that can be used in SolveCholesky.
88  //
89  // The returned matrix should be deallocated with Free when not used anymore.
90  cs_dis* AnalyzeCholesky(cs_di* A);
91
92  // Computes a symbolic factorization of A that can be used in
93  // SolveCholesky, but does not compute a fill-reducing ordering.
94  //
95  // The returned matrix should be deallocated with Free when not used anymore.
96  cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);
97
98  // Computes a symbolic factorization of A that can be used in
99  // SolveCholesky. The difference from AnalyzeCholesky is that this
100  // function first detects the block sparsity of the matrix using
101  // information about the row and column blocks and uses this block
102  // sparse matrix to find a fill-reducing ordering. This ordering is
103  // then used to find a symbolic factorization. This can result in a
104  // significant performance improvement AnalyzeCholesky on block
105  // sparse matrices.
106  //
107  // The returned matrix should be deallocated with Free when not used
108  // anymore.
109  cs_dis* BlockAnalyzeCholesky(cs_di* A,
110                               const vector<int>& row_blocks,
111                               const vector<int>& col_blocks);
112
113  // Compute an fill-reducing approximate minimum degree ordering of
114  // the matrix A. ordering should be non-NULL and should point to
115  // enough memory to hold the ordering for the rows of A.
116  void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);
117
118  void Free(cs_di* sparse_matrix);
119  void Free(cs_dis* symbolic_factorization);
120
121 private:
122  // Cached scratch space
123  CS_ENTRY* scratch_;
124  int scratch_size_;
125};
126
127}  // namespace internal
128}  // namespace ceres
129
130#else  // CERES_NO_CXSPARSE
131
132typedef void cs_dis;
133
134class CXSparse {
135 public:
136  void Free(void*) {};
137
138};
139#endif  // CERES_NO_CXSPARSE
140
141#endif  // CERES_INTERNAL_CXSPARSE_H_
142