Vehicle-cpp/zjw/ceres-solver-master/internal/ceres/trust_region_step_evaluator.h

// Ceres Solver - A fast non-linear least squares minimizer
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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_

#include "ceres/internal/export.h"

namespace ceres::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 principled 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 monotonic 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 CERES_NO_EXPORT TrustRegionStepEvaluator {
 public:
  // initial_cost is as the name implies the cost of the starting
  // state of the trust region minimizer.
  //
  // 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
  // monotonic descent algorithm.
  TrustRegionStepEvaluator(double initial_cost,
                           int max_consecutive_nonmonotonic_steps);

  // Return the quality of the step given its cost and the decrease in
  // the cost of the model. model_cost_change has to be positive.
  double StepQuality(double cost, double model_cost_change) const;

  // Inform the step evaluator that a step with the given cost and
  // model_cost_change has been accepted by the trust region
  // minimizer.
  void StepAccepted(double cost, double model_cost_change);

 private:
  const int max_consecutive_nonmonotonic_steps_;
  // The minimum cost encountered up till now.
  double minimum_cost_;
  // The current cost of the trust region minimizer as informed by the
  // last call to StepAccepted.
  double current_cost_;
  double reference_cost_;
  double candidate_cost_;
  // Accumulated model cost since the last time the reference model
  // cost was updated, i.e., when a step with cost less than the
  // current known minimum cost is accepted.
  double accumulated_reference_model_cost_change_;
  // Accumulated model cost since the last time the candidate model
  // cost was updated, i.e., a non-monotonic step was taken with a
  // cost that was greater than the current candidate cost.
  double accumulated_candidate_model_cost_change_;
  // Number of steps taken since the last time minimum_cost was updated.
  int num_consecutive_nonmonotonic_steps_;
};

}  // namespace ceres::internal

#endif  // CERES_INTERNAL_TRUST_REGION_STEP_EVALUATOR_H_