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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 #ifndef CERES_NO_CXSPARSE
35 
36 #include <vector>
37 #include "cs.h"
38 #include "ceres/internal/port.h"
39 
40 namespace ceres {
41 namespace internal {
42 
43 class CompressedRowSparseMatrix;
44 class TripletSparseMatrix;
45 
46 // This object provides access to solving linear systems using Cholesky
47 // factorization with a known symbolic factorization. This features does not
48 // explicity exist in CXSparse. The methods in the class are nonstatic because
49 // the class manages internal scratch space.
50 class CXSparse {
51  public:
52   CXSparse();
53   ~CXSparse();
54 
55   // Solves a symmetric linear system A * x = b using Cholesky factorization.
56   //  A                      - The system matrix.
57   //  symbolic_factorization - The symbolic factorization of A. This is obtained
58   //                           from AnalyzeCholesky.
59   //  b                      - The right hand size of the linear equation. This
60   //                           array will also recieve the solution.
61   // Returns false if Cholesky factorization of A fails.
62   bool SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b);
63 
64   // Creates a sparse matrix from a compressed-column form. No memory is
65   // allocated or copied; the structure A is filled out with info from the
66   // argument.
67   cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
68 
69   // Creates a new matrix from a triplet form. Deallocate the returned matrix
70   // with Free. May return NULL if the compression or allocation fails.
71   cs_di* CreateSparseMatrix(TripletSparseMatrix* A);
72 
73   // B = A'
74   //
75   // The returned matrix should be deallocated with Free when not used
76   // anymore.
77   cs_di* TransposeMatrix(cs_di* A);
78 
79   // C = A * B
80   //
81   // The returned matrix should be deallocated with Free when not used
82   // anymore.
83   cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B);
84 
85   // Computes a symbolic factorization of A that can be used in SolveCholesky.
86   //
87   // The returned matrix should be deallocated with Free when not used anymore.
88   cs_dis* AnalyzeCholesky(cs_di* A);
89 
90   // Computes a symbolic factorization of A that can be used in
91   // SolveCholesky, but does not compute a fill-reducing ordering.
92   //
93   // The returned matrix should be deallocated with Free when not used anymore.
94   cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A);
95 
96   // Computes a symbolic factorization of A that can be used in
97   // SolveCholesky. The difference from AnalyzeCholesky is that this
98   // function first detects the block sparsity of the matrix using
99   // information about the row and column blocks and uses this block
100   // sparse matrix to find a fill-reducing ordering. This ordering is
101   // then used to find a symbolic factorization. This can result in a
102   // significant performance improvement AnalyzeCholesky on block
103   // sparse matrices.
104   //
105   // The returned matrix should be deallocated with Free when not used
106   // anymore.
107   cs_dis* BlockAnalyzeCholesky(cs_di* A,
108                                const vector<int>& row_blocks,
109                                const vector<int>& col_blocks);
110 
111   // Compute an fill-reducing approximate minimum degree ordering of
112   // the matrix A. ordering should be non-NULL and should point to
113   // enough memory to hold the ordering for the rows of A.
114   void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering);
115 
116   void Free(cs_di* sparse_matrix);
117   void Free(cs_dis* symbolic_factorization);
118 
119  private:
120   // Cached scratch space
121   CS_ENTRY* scratch_;
122   int scratch_size_;
123 };
124 
125 }  // namespace internal
126 }  // namespace ceres
127 
128 #else  // CERES_NO_CXSPARSE
129 
130 class CXSparse {};
131 typedef void cs_dis;
132 
133 #endif  // CERES_NO_CXSPARSE
134 
135 #endif  // CERES_INTERNAL_CXSPARSE_H_
136