| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2016 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
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| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #ifndef CERES_INTERNAL_TRUST_REGION_STEP_EVALUATOR_H_ |
| #define CERES_INTERNAL_TRUST_REGION_STEP_EVALUATOR_H_ |
| |
| namespace ceres { |
| namespace internal { |
| |
| // The job of the TrustRegionStepEvaluator is to evaluate the quality |
| // of a step, i.e., how the cost of a step compares with the reduction |
| // in the objective of the trust region problem. |
| // |
| // Classic trust region methods are descent methods, in that they only |
| // accept a point if it strictly reduces the value of the objective |
| // function. They do this by measuring the quality of a step as |
| // |
| // cost_change / model_cost_change. |
| // |
| // Relaxing the monotonic descent requirement allows the algorithm to |
| // be more efficient in the long term at the cost of some local |
| // increase in the value of the objective function. |
| // |
| // This is because allowing for non-decreasing objective function |
| // values in a principaled manner allows the algorithm to "jump over |
| // boulders" as the method is not restricted to move into narrow |
| // valleys while preserving its convergence properties. |
| // |
| // The parameter max_consecutive_nonmonotonic_steps controls the |
| // window size used by the step selection algorithm to accept |
| // non-monotonic steps. Setting this parameter to zero, recovers the |
| // classic montonic descent algorithm. |
| // |
| // Based on algorithm 10.1.2 (page 357) of "Trust Region |
| // Methods" by Conn Gould & Toint, or equations 33-40 of |
| // "Non-monotone trust-region algorithms for nonlinear |
| // optimization subject to convex constraints" by Phil Toint, |
| // Mathematical Programming, 77, 1997. |
| // |
| // Example usage: |
| // |
| // TrustRegionStepEvaluator* step_evaluator = ... |
| // |
| // cost = ... // Compute the non-linear objective function value. |
| // model_cost_change = ... // Change in the value of the trust region objective. |
| // if (step_evaluator->StepQuality(cost, model_cost_change) > threshold) { |
| // x = x + delta; |
| // step_evaluator->StepAccepted(cost, model_cost_change); |
| // } |
| class TrustRegionStepEvaluator { |
| public: |
| TrustRegionStepEvaluator(double initial_cost, |
| int max_consecutive_nonmonotonic_steps); |
| double StepQuality(double cost, double model_cost_change) const; |
| void StepAccepted(double cost, double model_cost_change); |
| |
| private: |
| const int max_consecutive_nonmonotonic_steps_; |
| double minimum_cost_; |
| double current_cost_; |
| double reference_cost_; |
| double candidate_cost_; |
| double accumulated_reference_model_cost_change_; |
| double accumulated_candidate_model_cost_change_; |
| int num_consecutive_nonmonotonic_steps_; |
| }; |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_INTERNAL_TRUST_REGION_STEP_EVALUATOR_H_ |