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 // keir (at) google.com (Keir Mierle) 31 32 #ifndef CERES_INTERNAL_EVALUATOR_H_ 33 #define CERES_INTERNAL_EVALUATOR_H_ 34 35 #include <map> 36 #include <string> 37 #include <vector> 38 39 #include "ceres/execution_summary.h" 40 #include "ceres/internal/port.h" 41 #include "ceres/types.h" 42 43 namespace ceres { 44 45 struct CRSMatrix; 46 47 namespace internal { 48 49 class Program; 50 class SparseMatrix; 51 52 // The Evaluator interface offers a way to interact with a least squares cost 53 // function that is useful for an optimizer that wants to minimize the least 54 // squares objective. This insulates the optimizer from issues like Jacobian 55 // storage, parameterization, etc. 56 class Evaluator { 57 public: 58 virtual ~Evaluator(); 59 60 struct Options { 61 Options() 62 : num_threads(1), 63 num_eliminate_blocks(-1), 64 linear_solver_type(DENSE_QR), 65 dynamic_sparsity(false) {} 66 67 int num_threads; 68 int num_eliminate_blocks; 69 LinearSolverType linear_solver_type; 70 bool dynamic_sparsity; 71 }; 72 73 static Evaluator* Create(const Options& options, 74 Program* program, 75 string* error); 76 77 // This is used for computing the cost, residual and Jacobian for 78 // returning to the user. For actually solving the optimization 79 // problem, the optimization algorithm uses the ProgramEvaluator 80 // objects directly. 81 // 82 // The residual, gradients and jacobian pointers can be NULL, in 83 // which case they will not be evaluated. cost cannot be NULL. 84 // 85 // The parallelism of the evaluator is controlled by num_threads; it 86 // should be at least 1. 87 // 88 // Note: That this function does not take a parameter vector as 89 // input. The parameter blocks are evaluated on the values contained 90 // in the arrays pointed to by their user_state pointers. 91 // 92 // Also worth noting is that this function mutates program by 93 // calling Program::SetParameterOffsetsAndIndex() on it so that an 94 // evaluator object can be constructed. 95 static bool Evaluate(Program* program, 96 int num_threads, 97 double* cost, 98 vector<double>* residuals, 99 vector<double>* gradient, 100 CRSMatrix* jacobian); 101 102 // Build and return a sparse matrix for storing and working with the Jacobian 103 // of the objective function. The jacobian has dimensions 104 // NumEffectiveParameters() by NumParameters(), and is typically extremely 105 // sparse. Since the sparsity pattern of the Jacobian remains constant over 106 // the lifetime of the optimization problem, this method is used to 107 // instantiate a SparseMatrix object with the appropriate sparsity structure 108 // (which can be an expensive operation) and then reused by the optimization 109 // algorithm and the various linear solvers. 110 // 111 // It is expected that the classes implementing this interface will be aware 112 // of their client's requirements for the kind of sparse matrix storage and 113 // layout that is needed for an efficient implementation. For example 114 // CompressedRowOptimizationProblem creates a compressed row representation of 115 // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem 116 // creates a BlockSparseMatrix representation of the jacobian for use in the 117 // Schur complement based methods. 118 virtual SparseMatrix* CreateJacobian() const = 0; 119 120 121 // Options struct to control Evaluator::Evaluate; 122 struct EvaluateOptions { 123 EvaluateOptions() 124 : apply_loss_function(true) { 125 } 126 127 // If false, the loss function correction is not applied to the 128 // residual blocks. 129 bool apply_loss_function; 130 }; 131 132 // Evaluate the cost function for the given state. Returns the cost, 133 // residuals, and jacobian in the corresponding arguments. Both residuals and 134 // jacobian are optional; to avoid computing them, pass NULL. 135 // 136 // If non-NULL, the Jacobian must have a suitable sparsity pattern; only the 137 // values array of the jacobian is modified. 138 // 139 // state is an array of size NumParameters(), cost is a pointer to a single 140 // double, and residuals is an array of doubles of size NumResiduals(). 141 virtual bool Evaluate(const EvaluateOptions& evaluate_options, 142 const double* state, 143 double* cost, 144 double* residuals, 145 double* gradient, 146 SparseMatrix* jacobian) = 0; 147 148 // Variant of Evaluator::Evaluate where the user wishes to use the 149 // default EvaluateOptions struct. This is mostly here as a 150 // convenience method. 151 bool Evaluate(const double* state, 152 double* cost, 153 double* residuals, 154 double* gradient, 155 SparseMatrix* jacobian) { 156 return Evaluate(EvaluateOptions(), 157 state, 158 cost, 159 residuals, 160 gradient, 161 jacobian); 162 } 163 164 // Make a change delta (of size NumEffectiveParameters()) to state (of size 165 // NumParameters()) and store the result in state_plus_delta. 166 // 167 // In the case that there are no parameterizations used, this is equivalent to 168 // 169 // state_plus_delta[i] = state[i] + delta[i] ; 170 // 171 // however, the mapping is more complicated in the case of parameterizations 172 // like quaternions. This is the same as the "Plus()" operation in 173 // local_parameterization.h, but operating over the entire state vector for a 174 // problem. 175 virtual bool Plus(const double* state, 176 const double* delta, 177 double* state_plus_delta) const = 0; 178 179 // The number of parameters in the optimization problem. 180 virtual int NumParameters() const = 0; 181 182 // This is the effective number of parameters that the optimizer may adjust. 183 // This applies when there are parameterizations on some of the parameters. 184 virtual int NumEffectiveParameters() const = 0; 185 186 // The number of residuals in the optimization problem. 187 virtual int NumResiduals() const = 0; 188 189 // The following two methods return copies instead of references so 190 // that the base class implementation does not have to worry about 191 // life time issues. Further, these calls are not expected to be 192 // frequent or performance sensitive. 193 virtual map<string, int> CallStatistics() const { 194 return map<string, int>(); 195 } 196 197 virtual map<string, double> TimeStatistics() const { 198 return map<string, double>(); 199 } 200 }; 201 202 } // namespace internal 203 } // namespace ceres 204 205 #endif // CERES_INTERNAL_EVALUATOR_H_ 206
