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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+// used to endorse or promote products derived from this software without
+// specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#ifndef CERES_INTERNAL_MINIMIZER_H_
+#define CERES_INTERNAL_MINIMIZER_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "ceres/internal/port.h"
+#include "ceres/iteration_callback.h"
+#include "ceres/solver.h"
+
+namespace ceres {
+namespace internal {
+
+class Evaluator;
+class SparseMatrix;
+class TrustRegionStrategy;
+class CoordinateDescentMinimizer;
+class LinearSolver;
+
+// Interface for non-linear least squares solvers.
+class Minimizer {
+ public:
+ // Options struct to control the behaviour of the Minimizer. Please
+ // see solver.h for detailed information about the meaning and
+ // default values of each of these parameters.
+ struct Options {
+ Options() {
+ Init(Solver::Options());
+ }
+
+ explicit Options(const Solver::Options& options) {
+ Init(options);
+ }
+
+ void Init(const Solver::Options& options) {
+ num_threads = options.num_threads;
+ max_num_iterations = options.max_num_iterations;
+ max_solver_time_in_seconds = options.max_solver_time_in_seconds;
+ max_step_solver_retries = 5;
+ gradient_tolerance = options.gradient_tolerance;
+ parameter_tolerance = options.parameter_tolerance;
+ function_tolerance = options.function_tolerance;
+ min_relative_decrease = options.min_relative_decrease;
+ eta = options.eta;
+ jacobi_scaling = options.jacobi_scaling;
+ use_nonmonotonic_steps = options.use_nonmonotonic_steps;
+ max_consecutive_nonmonotonic_steps =
+ options.max_consecutive_nonmonotonic_steps;
+ trust_region_problem_dump_directory =
+ options.trust_region_problem_dump_directory;
+ trust_region_minimizer_iterations_to_dump =
+ options.trust_region_minimizer_iterations_to_dump;
+ trust_region_problem_dump_format_type =
+ options.trust_region_problem_dump_format_type;
+ max_num_consecutive_invalid_steps =
+ options.max_num_consecutive_invalid_steps;
+ min_trust_region_radius = options.min_trust_region_radius;
+ line_search_direction_type = options.line_search_direction_type;
+ line_search_type = options.line_search_type;
+ nonlinear_conjugate_gradient_type =
+ options.nonlinear_conjugate_gradient_type;
+ max_lbfgs_rank = options.max_lbfgs_rank;
+ use_approximate_eigenvalue_bfgs_scaling =
+ options.use_approximate_eigenvalue_bfgs_scaling;
+ line_search_interpolation_type =
+ options.line_search_interpolation_type;
+ min_line_search_step_size = options.min_line_search_step_size;
+ line_search_sufficient_function_decrease =
+ options.line_search_sufficient_function_decrease;
+ max_line_search_step_contraction =
+ options.max_line_search_step_contraction;
+ min_line_search_step_contraction =
+ options.min_line_search_step_contraction;
+ max_num_line_search_step_size_iterations =
+ options.max_num_line_search_step_size_iterations;
+ max_num_line_search_direction_restarts =
+ options.max_num_line_search_direction_restarts;
+ line_search_sufficient_curvature_decrease =
+ options.line_search_sufficient_curvature_decrease;
+ max_line_search_step_expansion =
+ options.max_line_search_step_expansion;
+ inner_iteration_tolerance = options.inner_iteration_tolerance;
+ is_silent = (options.logging_type == SILENT);
+ is_constrained = false;
+ callbacks = options.callbacks;
+ }
+
+ int max_num_iterations;
+ double max_solver_time_in_seconds;
+ int num_threads;
+
+ // Number of times the linear solver should be retried in case of
+ // numerical failure. The retries are done by exponentially scaling up
+ // mu at each retry. This leads to stronger and stronger
+ // regularization making the linear least squares problem better
+ // conditioned at each retry.
+ int max_step_solver_retries;
+ double gradient_tolerance;
+ double parameter_tolerance;
+ double function_tolerance;
+ double min_relative_decrease;
+ double eta;
+ bool jacobi_scaling;
+ bool use_nonmonotonic_steps;
+ int max_consecutive_nonmonotonic_steps;
+ std::vector<int> trust_region_minimizer_iterations_to_dump;
+ DumpFormatType trust_region_problem_dump_format_type;
+ std::string trust_region_problem_dump_directory;
+ int max_num_consecutive_invalid_steps;
+ double min_trust_region_radius;
+ LineSearchDirectionType line_search_direction_type;
+ LineSearchType line_search_type;
+ NonlinearConjugateGradientType nonlinear_conjugate_gradient_type;
+ int max_lbfgs_rank;
+ bool use_approximate_eigenvalue_bfgs_scaling;
+ LineSearchInterpolationType line_search_interpolation_type;
+ double min_line_search_step_size;
+ double line_search_sufficient_function_decrease;
+ double max_line_search_step_contraction;
+ double min_line_search_step_contraction;
+ int max_num_line_search_step_size_iterations;
+ int max_num_line_search_direction_restarts;
+ double line_search_sufficient_curvature_decrease;
+ double max_line_search_step_expansion;
+ double inner_iteration_tolerance;
+
+ // If true, then all logging is disabled.
+ bool is_silent;
+
+ // Use a bounds constrained optimization algorithm.
+ bool is_constrained;
+
+ // List of callbacks that are executed by the Minimizer at the end
+ // of each iteration.
+ //
+ // The Options struct does not own these pointers.
+ std::vector<IterationCallback*> callbacks;
+
+ // Object responsible for evaluating the cost, residuals and
+ // Jacobian matrix.
+ std::shared_ptr<Evaluator> evaluator;
+
+ // Object responsible for actually computing the trust region
+ // step, and sizing the trust region radius.
+ std::shared_ptr<TrustRegionStrategy> trust_region_strategy;
+
+ // Object holding the Jacobian matrix. It is assumed that the
+ // sparsity structure of the matrix has already been initialized
+ // and will remain constant for the life time of the
+ // optimization.
+ std::shared_ptr<SparseMatrix> jacobian;
+
+ std::shared_ptr<CoordinateDescentMinimizer> inner_iteration_minimizer;
+ };
+
+ static Minimizer* Create(MinimizerType minimizer_type);
+ static bool RunCallbacks(const Options& options,
+ const IterationSummary& iteration_summary,
+ Solver::Summary* summary);
+
+ virtual ~Minimizer();
+ // Note: The minimizer is expected to update the state of the
+ // parameters array every iteration. This is required for the
+ // StateUpdatingCallback to work.
+ virtual void Minimize(const Options& options,
+ double* parameters,
+ Solver::Summary* summary) = 0;
+};
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_INTERNAL_MINIMIZER_H_