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 (at) 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 42 namespace ceres { 43 namespace internal { 44 45 class CompressedRowSparseMatrix; 46 class 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. 52 class 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 131 132 typedef void cs_dis; 133 134 class CXSparse { 135 public: 136 void Free(void*) {}; 137 138 }; 139 #endif // CERES_NO_CXSPARSE 140 141 #endif // CERES_INTERNAL_CXSPARSE_H_ 142
