1 // Ceres Solver - A fast non-linear least squares minimizer
2 // Copyright 2015 Google Inc. All rights reserved.
3 // http://ceres-solver.org/
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6 // modification, are permitted provided that the following conditions are met:
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29 // Author: sameeragarwal@google.com (Sameer Agarwal)
31 // When an iteration callback is specified, Ceres calls the callback
32 // after each minimizer step (if the minimizer has not converged) and
33 // passes it an IterationSummary object, defined below.
35 #ifndef CERES_PUBLIC_ITERATION_CALLBACK_H_
36 #define CERES_PUBLIC_ITERATION_CALLBACK_H_
38 #include "ceres/types.h"
39 #include "ceres/internal/disable_warnings.h"
43 // This struct describes the state of the optimizer after each
44 // iteration of the minimization.
45 struct CERES_EXPORT IterationSummary {
49 step_is_nonmonotonic(false),
50 step_is_successful(false),
53 gradient_max_norm(0.0),
58 line_search_function_evaluations(0),
59 line_search_gradient_evaluations(0),
60 line_search_iterations(0),
61 linear_solver_iterations(0),
62 iteration_time_in_seconds(0.0),
63 step_solver_time_in_seconds(0.0),
64 cumulative_time_in_seconds(0.0) {}
66 // Current iteration number.
69 // Step was numerically valid, i.e., all values are finite and the
70 // step reduces the value of the linearized model.
72 // Note: step_is_valid is always true when iteration = 0.
75 // Step did not reduce the value of the objective function
76 // sufficiently, but it was accepted because of the relaxed
77 // acceptance criterion used by the non-monotonic trust region
80 // Note: step_is_nonmonotonic is always false when iteration = 0;
81 bool step_is_nonmonotonic;
83 // Whether or not the minimizer accepted this step or not. If the
84 // ordinary trust region algorithm is used, this means that the
85 // relative reduction in the objective function value was greater
86 // than Solver::Options::min_relative_decrease. However, if the
87 // non-monotonic trust region algorithm is used
88 // (Solver::Options:use_nonmonotonic_steps = true), then even if the
89 // relative decrease is not sufficient, the algorithm may accept the
90 // step and the step is declared successful.
92 // Note: step_is_successful is always true when iteration = 0.
93 bool step_is_successful;
95 // Value of the objective function.
98 // Change in the value of the objective function in this
99 // iteration. This can be positive or negative.
102 // Infinity norm of the gradient vector.
103 double gradient_max_norm;
105 // 2-norm of the gradient vector.
106 double gradient_norm;
108 // 2-norm of the size of the step computed by the optimization
112 // For trust region algorithms, the ratio of the actual change in
113 // cost and the change in the cost of the linearized approximation.
114 double relative_decrease;
116 // Size of the trust region at the end of the current iteration. For
117 // the Levenberg-Marquardt algorithm, the regularization parameter
118 // mu = 1.0 / trust_region_radius.
119 double trust_region_radius;
121 // For the inexact step Levenberg-Marquardt algorithm, this is the
122 // relative accuracy with which the Newton(LM) step is solved. This
123 // number affects only the iterative solvers capable of solving
124 // linear systems inexactly. Factorization-based exact solvers
128 // Step sized computed by the line search algorithm.
131 // Number of function value evaluations used by the line search algorithm.
132 int line_search_function_evaluations;
134 // Number of function gradient evaluations used by the line search algorithm.
135 int line_search_gradient_evaluations;
137 // Number of iterations taken by the line search algorithm.
138 int line_search_iterations;
140 // Number of iterations taken by the linear solver to solve for the
142 int linear_solver_iterations;
144 // All times reported below are wall times.
146 // Time (in seconds) spent inside the minimizer loop in the current
148 double iteration_time_in_seconds;
150 // Time (in seconds) spent inside the trust region step solver.
151 double step_solver_time_in_seconds;
153 // Time (in seconds) since the user called Solve().
154 double cumulative_time_in_seconds;
157 // Interface for specifying callbacks that are executed at the end of
158 // each iteration of the Minimizer. The solver uses the return value
159 // of operator() to decide whether to continue solving or to
160 // terminate. The user can return three values.
162 // SOLVER_ABORT indicates that the callback detected an abnormal
163 // situation. The solver returns without updating the parameter blocks
164 // (unless Solver::Options::update_state_every_iteration is set
165 // true). Solver returns with Solver::Summary::termination_type set to
168 // SOLVER_TERMINATE_SUCCESSFULLY indicates that there is no need to
169 // optimize anymore (some user specified termination criterion has
170 // been met). Solver returns with Solver::Summary::termination_type
171 // set to USER_SUCCESS.
173 // SOLVER_CONTINUE indicates that the solver should continue
176 // For example, the following Callback is used internally by Ceres to
177 // log the progress of the optimization.
179 // Callback for logging the state of the minimizer to STDERR or STDOUT
180 // depending on the user's preferences and logging level.
182 // class LoggingCallback : public IterationCallback {
184 // explicit LoggingCallback(bool log_to_stdout)
185 // : log_to_stdout_(log_to_stdout) {}
187 // ~LoggingCallback() {}
189 // CallbackReturnType operator()(const IterationSummary& summary) {
190 // const char* kReportRowFormat =
191 // "% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e "
192 // "rho:% 3.2e mu:% 3.2e eta:% 3.2e li:% 3d";
193 // string output = StringPrintf(kReportRowFormat,
194 // summary.iteration,
196 // summary.cost_change,
197 // summary.gradient_max_norm,
198 // summary.step_norm,
199 // summary.relative_decrease,
200 // summary.trust_region_radius,
202 // summary.linear_solver_iterations);
203 // if (log_to_stdout_) {
204 // cout << output << endl;
206 // VLOG(1) << output;
208 // return SOLVER_CONTINUE;
212 // const bool log_to_stdout_;
215 class CERES_EXPORT IterationCallback {
217 virtual ~IterationCallback() {}
218 virtual CallbackReturnType operator()(const IterationSummary& summary) = 0;
223 #include "ceres/internal/reenable_warnings.h"
225 #endif // CERES_PUBLIC_ITERATION_CALLBACK_H_