1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2015 Google Inc. All rights reserved. 3 // http://ceres-solver.org/ 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 <memory> 40 #include <string> 41 #include <vector> 42 43 #include "ceres/linear_solver.h" 44 #include "ceres/sparse_cholesky.h" 45 #include "cs.h" 46 47 namespace ceres { 48 namespace internal { 49 50 class CompressedRowSparseMatrix; 51 class TripletSparseMatrix; 52 53 // This object provides access to solving linear systems using Cholesky 54 // factorization with a known symbolic factorization. This features does not 55 // explicitly exist in CXSparse. The methods in the class are nonstatic because 56 // the class manages internal scratch space. 57 class CXSparse { 58 public: 59 CXSparse(); 60 ~CXSparse(); 61 62 // Solve the system lhs * solution = rhs in place by using an 63 // approximate minimum degree fill reducing ordering. 64 bool SolveCholesky(cs_di* lhs, double* rhs_and_solution); 65 66 // Solves a linear system given its symbolic and numeric factorization. 67 void Solve(cs_dis* symbolic_factor, 68 csn* numeric_factor, 69 double* rhs_and_solution); 70 71 // Compute the numeric Cholesky factorization of A, given its 72 // symbolic factorization. 73 // 74 // Caller owns the result. 75 csn* Cholesky(cs_di* A, cs_dis* symbolic_factor); 76 77 // Creates a sparse matrix from a compressed-column form. No memory is 78 // allocated or copied; the structure A is filled out with info from the 79 // argument. 80 cs_di CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A); 81 82 // Creates a new matrix from a triplet form. Deallocate the returned matrix 83 // with Free. May return NULL if the compression or allocation fails. 84 cs_di* CreateSparseMatrix(TripletSparseMatrix* A); 85 86 // B = A' 87 // 88 // The returned matrix should be deallocated with Free when not used 89 // anymore. 90 cs_di* TransposeMatrix(cs_di* A); 91 92 // C = A * B 93 // 94 // The returned matrix should be deallocated with Free when not used 95 // anymore. 96 cs_di* MatrixMatrixMultiply(cs_di* A, cs_di* B); 97 98 // Computes a symbolic factorization of A that can be used in SolveCholesky. 99 // 100 // The returned matrix should be deallocated with Free when not used anymore. 101 cs_dis* AnalyzeCholesky(cs_di* A); 102 103 // Computes a symbolic factorization of A that can be used in 104 // SolveCholesky, but does not compute a fill-reducing ordering. 105 // 106 // The returned matrix should be deallocated with Free when not used anymore. 107 cs_dis* AnalyzeCholeskyWithNaturalOrdering(cs_di* A); 108 109 // Computes a symbolic factorization of A that can be used in 110 // SolveCholesky. The difference from AnalyzeCholesky is that this 111 // function first detects the block sparsity of the matrix using 112 // information about the row and column blocks and uses this block 113 // sparse matrix to find a fill-reducing ordering. This ordering is 114 // then used to find a symbolic factorization. This can result in a 115 // significant performance improvement AnalyzeCholesky on block 116 // sparse matrices. 117 // 118 // The returned matrix should be deallocated with Free when not used 119 // anymore. 120 cs_dis* BlockAnalyzeCholesky(cs_di* A, 121 const std::vector<int>& row_blocks, 122 const std::vector<int>& col_blocks); 123 124 // Compute an fill-reducing approximate minimum degree ordering of 125 // the matrix A. ordering should be non-NULL and should point to 126 // enough memory to hold the ordering for the rows of A. 127 void ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering); 128 129 void Free(cs_di* sparse_matrix); 130 void Free(cs_dis* symbolic_factorization); 131 void Free(csn* numeric_factorization); 132 133 private: 134 // Cached scratch space 135 CS_ENTRY* scratch_; 136 int scratch_size_; 137 }; 138 139 // An implementation of SparseCholesky interface using the CXSparse 140 // library. 141 class CXSparseCholesky : public SparseCholesky { 142 public: 143 // Factory 144 static std::unique_ptr<SparseCholesky> Create(OrderingType ordering_type); 145 146 // SparseCholesky interface. 147 virtual ~CXSparseCholesky(); 148 CompressedRowSparseMatrix::StorageType StorageType() const final; 149 LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs, 150 std::string* message) final; 151 LinearSolverTerminationType Solve(const double* rhs, 152 double* solution, 153 std::string* message) final; 154 155 private: 156 CXSparseCholesky(const OrderingType ordering_type); 157 void FreeSymbolicFactorization(); 158 void FreeNumericFactorization(); 159 160 const OrderingType ordering_type_; 161 CXSparse cs_; 162 cs_dis* symbolic_factor_; 163 csn* numeric_factor_; 164 }; 165 166 } // namespace internal 167 } // namespace ceres 168 169 #else 170 171 typedef void cs_dis; 172 173 class CXSparse { 174 public: Free(void * arg)175 void Free(void* arg) {} 176 }; 177 #endif // CERES_NO_CXSPARSE 178 179 #endif // CERES_INTERNAL_CXSPARSE_H_ 180