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 #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
