| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2016 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: |
| // |
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| // 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" |
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| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #ifndef CERES_INTERNAL_TRUST_REGION_MINIMIZER_H_ |
| #define CERES_INTERNAL_TRUST_REGION_MINIMIZER_H_ |
| |
| #include <memory> |
| #include "ceres/internal/eigen.h" |
| #include "ceres/minimizer.h" |
| #include "ceres/solver.h" |
| #include "ceres/sparse_matrix.h" |
| #include "ceres/trust_region_step_evaluator.h" |
| #include "ceres/trust_region_strategy.h" |
| #include "ceres/types.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| // Generic trust region minimization algorithm. |
| // |
| // For example usage, see SolverImpl::Minimize. |
| class TrustRegionMinimizer : public Minimizer { |
| public: |
| ~TrustRegionMinimizer(); |
| |
| // This method is not thread safe. |
| void Minimize(const Minimizer::Options& options, |
| double* parameters, |
| Solver::Summary* solver_summary) override; |
| |
| private: |
| void Init(const Minimizer::Options& options, |
| double* parameters, |
| Solver::Summary* solver_summary); |
| bool IterationZero(); |
| bool FinalizeIterationAndCheckIfMinimizerCanContinue(); |
| bool ComputeTrustRegionStep(); |
| |
| bool EvaluateGradientAndJacobian(bool new_evaluation_point); |
| void ComputeCandidatePointAndEvaluateCost(); |
| |
| void DoLineSearch(const Vector& x, |
| const Vector& gradient, |
| const double cost, |
| Vector* delta); |
| void DoInnerIterationsIfNeeded(); |
| |
| bool ParameterToleranceReached(); |
| bool FunctionToleranceReached(); |
| bool GradientToleranceReached(); |
| bool MaxSolverTimeReached(); |
| bool MaxSolverIterationsReached(); |
| bool MinTrustRegionRadiusReached(); |
| |
| bool IsStepSuccessful(); |
| void HandleUnsuccessfulStep(); |
| bool HandleSuccessfulStep(); |
| bool HandleInvalidStep(); |
| |
| Minimizer::Options options_; |
| |
| // These pointers are shortcuts to objects passed to the |
| // TrustRegionMinimizer. The TrustRegionMinimizer does not own them. |
| double* parameters_; |
| Solver::Summary* solver_summary_; |
| Evaluator* evaluator_; |
| SparseMatrix* jacobian_; |
| TrustRegionStrategy* strategy_; |
| |
| std::unique_ptr<TrustRegionStepEvaluator> step_evaluator_; |
| |
| bool is_not_silent_; |
| bool inner_iterations_are_enabled_; |
| bool inner_iterations_were_useful_; |
| |
| // Summary of the current iteration. |
| IterationSummary iteration_summary_; |
| |
| // Dimensionality of the problem in the ambient space. |
| int num_parameters_; |
| // Dimensionality of the problem in the tangent space. This is the |
| // number of columns in the Jacobian. |
| int num_effective_parameters_; |
| // Length of the residual vector, also the number of rows in the Jacobian. |
| int num_residuals_; |
| |
| // Current point. |
| Vector x_; |
| // Residuals at x_; |
| Vector residuals_; |
| // Gradient at x_. |
| Vector gradient_; |
| // Solution computed by the inner iterations. |
| Vector inner_iteration_x_; |
| // model_residuals = J * trust_region_step |
| Vector model_residuals_; |
| Vector negative_gradient_; |
| // projected_gradient_step = Plus(x, -gradient), an intermediate |
| // quantity used to compute the projected gradient norm. |
| Vector projected_gradient_step_; |
| // The step computed by the trust region strategy. If Jacobi scaling |
| // is enabled, this is a vector in the scaled space. |
| Vector trust_region_step_; |
| // The current proposal for how far the trust region algorithm |
| // thinks we should move. In the most basic case, it is just the |
| // trust_region_step_ with the Jacobi scaling undone. If bounds |
| // constraints are present, then it is the result of the projected |
| // line search. |
| Vector delta_; |
| // candidate_x = Plus(x, delta) |
| Vector candidate_x_; |
| // Scaling vector to scale the columns of the Jacobian. |
| Vector jacobian_scaling_; |
| |
| // Euclidean norm of x_. |
| double x_norm_; |
| // Cost at x_. |
| double x_cost_; |
| // Minimum cost encountered up till now. |
| double minimum_cost_; |
| // How much did the trust region strategy reduce the cost of the |
| // linearized Gauss-Newton model. |
| double model_cost_change_; |
| // Cost at candidate_x_. |
| double candidate_cost_; |
| |
| // Time at which the minimizer was started. |
| double start_time_in_secs_; |
| // Time at which the current iteration was started. |
| double iteration_start_time_in_secs_; |
| // Number of consecutive steps where the minimizer loop computed a |
| // numerically invalid step. |
| int num_consecutive_invalid_steps_; |
| }; |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_INTERNAL_TRUST_REGION_MINIMIZER_H_ |