ceres-solver/internal/ceres/minimizer.h

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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// 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.
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//   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_MINIMIZER_H_
#define CERES_INTERNAL_MINIMIZER_H_

#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 LinearSolver;
class SparseMatrix;
class TrustRegionStrategy;

// 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;
      line_search_interpolation_type =
          options.line_search_interpolation_type;
      min_line_search_step_size = options.min_line_search_step_size;
      armijo_sufficient_decrease = options.armijo_sufficient_decrease;
      min_armijo_relative_step_size_change =
          options.min_armijo_relative_step_size_change;
      max_armijo_relative_step_size_change =
          options.max_armijo_relative_step_size_change;
      evaluator = NULL;
      trust_region_strategy = NULL;
      jacobian = NULL;
      callbacks = options.callbacks;
      inner_iteration_minimizer = NULL;
      inner_iteration_tolerance = options.inner_iteration_tolerance;
    }

    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;
    vector<int> trust_region_minimizer_iterations_to_dump;
    DumpFormatType trust_region_problem_dump_format_type;
    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;
    LineSearchInterpolationType line_search_interpolation_type;
    double min_line_search_step_size;
    double armijo_sufficient_decrease;
    double min_armijo_relative_step_size_change;
    double max_armijo_relative_step_size_change;


    // List of callbacks that are executed by the Minimizer at the end
    // of each iteration.
    //
    // The Options struct does not own these pointers.
    vector<IterationCallback*> callbacks;

    // Object responsible for evaluating the cost, residuals and
    // Jacobian matrix. The Options struct does not own this pointer.
    Evaluator* evaluator;

    // Object responsible for actually computing the trust region
    // step, and sizing the trust region radius. The Options struct
    // does not own this pointer.
    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. The Options struct does not own this pointer.
    SparseMatrix* jacobian;

    Minimizer* inner_iteration_minimizer;
    double inner_iteration_tolerance;
  };

  static bool RunCallbacks(const vector<IterationCallback*> callbacks,
                           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_