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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/disable_warnings.h"
#include "ceres/internal/export.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 CERES_NO_EXPORT 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 std::unique_ptr<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
#include "ceres/internal/reenable_warnings.h"
#endif // CERES_INTERNAL_MINIMIZER_H_
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