1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2010, 2011, 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: sameeragarwal (at) google.com (Sameer Agarwal) 30 31 #ifndef CERES_INTERNAL_MINIMIZER_H_ 32 #define CERES_INTERNAL_MINIMIZER_H_ 33 34 #include <string> 35 #include <vector> 36 #include "ceres/internal/port.h" 37 #include "ceres/iteration_callback.h" 38 #include "ceres/solver.h" 39 40 namespace ceres { 41 namespace internal { 42 43 class Evaluator; 44 class LinearSolver; 45 class SparseMatrix; 46 class TrustRegionStrategy; 47 48 // Interface for non-linear least squares solvers. 49 class Minimizer { 50 public: 51 // Options struct to control the behaviour of the Minimizer. Please 52 // see solver.h for detailed information about the meaning and 53 // default values of each of these parameters. 54 struct Options { 55 Options() { 56 Init(Solver::Options()); 57 } 58 59 explicit Options(const Solver::Options& options) { 60 Init(options); 61 } 62 63 void Init(const Solver::Options& options) { 64 num_threads = options.num_threads; 65 max_num_iterations = options.max_num_iterations; 66 max_solver_time_in_seconds = options.max_solver_time_in_seconds; 67 max_step_solver_retries = 5; 68 gradient_tolerance = options.gradient_tolerance; 69 parameter_tolerance = options.parameter_tolerance; 70 function_tolerance = options.function_tolerance; 71 min_relative_decrease = options.min_relative_decrease; 72 eta = options.eta; 73 jacobi_scaling = options.jacobi_scaling; 74 use_nonmonotonic_steps = options.use_nonmonotonic_steps; 75 max_consecutive_nonmonotonic_steps = 76 options.max_consecutive_nonmonotonic_steps; 77 trust_region_problem_dump_directory = 78 options.trust_region_problem_dump_directory; 79 trust_region_minimizer_iterations_to_dump = 80 options.trust_region_minimizer_iterations_to_dump; 81 trust_region_problem_dump_format_type = 82 options.trust_region_problem_dump_format_type; 83 max_num_consecutive_invalid_steps = 84 options.max_num_consecutive_invalid_steps; 85 min_trust_region_radius = options.min_trust_region_radius; 86 line_search_direction_type = options.line_search_direction_type; 87 line_search_type = options.line_search_type; 88 nonlinear_conjugate_gradient_type = 89 options.nonlinear_conjugate_gradient_type; 90 max_lbfgs_rank = options.max_lbfgs_rank; 91 use_approximate_eigenvalue_bfgs_scaling = 92 options.use_approximate_eigenvalue_bfgs_scaling; 93 line_search_interpolation_type = 94 options.line_search_interpolation_type; 95 min_line_search_step_size = options.min_line_search_step_size; 96 line_search_sufficient_function_decrease = 97 options.line_search_sufficient_function_decrease; 98 max_line_search_step_contraction = 99 options.max_line_search_step_contraction; 100 min_line_search_step_contraction = 101 options.min_line_search_step_contraction; 102 max_num_line_search_step_size_iterations = 103 options.max_num_line_search_step_size_iterations; 104 max_num_line_search_direction_restarts = 105 options.max_num_line_search_direction_restarts; 106 line_search_sufficient_curvature_decrease = 107 options.line_search_sufficient_curvature_decrease; 108 max_line_search_step_expansion = 109 options.max_line_search_step_expansion; 110 is_silent = (options.logging_type == SILENT); 111 evaluator = NULL; 112 trust_region_strategy = NULL; 113 jacobian = NULL; 114 callbacks = options.callbacks; 115 inner_iteration_minimizer = NULL; 116 inner_iteration_tolerance = options.inner_iteration_tolerance; 117 is_constrained = false; 118 } 119 120 int max_num_iterations; 121 double max_solver_time_in_seconds; 122 int num_threads; 123 124 // Number of times the linear solver should be retried in case of 125 // numerical failure. The retries are done by exponentially scaling up 126 // mu at each retry. This leads to stronger and stronger 127 // regularization making the linear least squares problem better 128 // conditioned at each retry. 129 int max_step_solver_retries; 130 double gradient_tolerance; 131 double parameter_tolerance; 132 double function_tolerance; 133 double min_relative_decrease; 134 double eta; 135 bool jacobi_scaling; 136 bool use_nonmonotonic_steps; 137 int max_consecutive_nonmonotonic_steps; 138 vector<int> trust_region_minimizer_iterations_to_dump; 139 DumpFormatType trust_region_problem_dump_format_type; 140 string trust_region_problem_dump_directory; 141 int max_num_consecutive_invalid_steps; 142 double min_trust_region_radius; 143 LineSearchDirectionType line_search_direction_type; 144 LineSearchType line_search_type; 145 NonlinearConjugateGradientType nonlinear_conjugate_gradient_type; 146 int max_lbfgs_rank; 147 bool use_approximate_eigenvalue_bfgs_scaling; 148 LineSearchInterpolationType line_search_interpolation_type; 149 double min_line_search_step_size; 150 double line_search_sufficient_function_decrease; 151 double max_line_search_step_contraction; 152 double min_line_search_step_contraction; 153 int max_num_line_search_step_size_iterations; 154 int max_num_line_search_direction_restarts; 155 double line_search_sufficient_curvature_decrease; 156 double max_line_search_step_expansion; 157 158 // If true, then all logging is disabled. 159 bool is_silent; 160 161 // List of callbacks that are executed by the Minimizer at the end 162 // of each iteration. 163 // 164 // The Options struct does not own these pointers. 165 vector<IterationCallback*> callbacks; 166 167 // Object responsible for evaluating the cost, residuals and 168 // Jacobian matrix. The Options struct does not own this pointer. 169 Evaluator* evaluator; 170 171 // Object responsible for actually computing the trust region 172 // step, and sizing the trust region radius. The Options struct 173 // does not own this pointer. 174 TrustRegionStrategy* trust_region_strategy; 175 176 // Object holding the Jacobian matrix. It is assumed that the 177 // sparsity structure of the matrix has already been initialized 178 // and will remain constant for the life time of the 179 // optimization. The Options struct does not own this pointer. 180 SparseMatrix* jacobian; 181 182 Minimizer* inner_iteration_minimizer; 183 double inner_iteration_tolerance; 184 185 // Use a bounds constrained optimization algorithm. 186 bool is_constrained; 187 }; 188 189 static bool RunCallbacks(const Options& options, 190 const IterationSummary& iteration_summary, 191 Solver::Summary* summary); 192 193 virtual ~Minimizer(); 194 // Note: The minimizer is expected to update the state of the 195 // parameters array every iteration. This is required for the 196 // StateUpdatingCallback to work. 197 virtual void Minimize(const Options& options, 198 double* parameters, 199 Solver::Summary* summary) = 0; 200 }; 201 202 } // namespace internal 203 } // namespace ceres 204 205 #endif // CERES_INTERNAL_MINIMIZER_H_ 206
