cmastopcriteria.h

#ifndef CMASTOPCRITERIA_H
#define CMASTOPCRITERIA_H

#include "cmaparameters.h"
#include <functional>
#include <map>

namespace libcmaes
{

  class CMASolutions;

  template <class TGenoPheno>
    using StopCriteriaFunc = std::function<int (const CMAParameters<TGenoPheno> &cmap, const CMASolutions &cmas)>;

  enum CMAStopCritType
  {
    CONT = 0,
    AUTOMAXITER = 7,
    TOLHISTFUN = 1, // convergence
    EQUALFUNVALS = 5, // partial success, user error
    TOLX = 2, // partial success
    TOLUPSIGMA = -13,
    STAGNATION = 6, // partial success
    CONDITIONCOV = -15, // error, user action needed
    NOEFFECTAXIS = 3, // partial success
    NOEFFECTCOOR = 4, // partial success
    MAXFEVALS = 8,
    MAXITER = 9,
    FTARGET = 10 // success
  };

  template <class TGenoPheno=NoBoundStrategy>
  class StopCriteria
  {
  public:
    StopCriteria(const StopCriteriaFunc<TGenoPheno> &sfunc)
      :_sfunc(sfunc)
    {}
    ~StopCriteria() {}

    inline bool active() const { return _active; }
  
    void set_active(const bool &a) { _active = a; }
  
    StopCriteriaFunc<TGenoPheno> _sfunc;
    bool _active = true;
  };
  
  template <class TGenoPheno=NoBoundStrategy>
  class CMAStopCriteria
  {
    friend class CMASolutions;

  public:
    CMAStopCriteria();
    ~CMAStopCriteria();

    int stop(const CMAParameters<TGenoPheno> &cmap, const CMASolutions &cmas) const;

    int set_criteria_active(const int &c, const bool &active);
  
  private:
    std::map<int,StopCriteria<TGenoPheno> > _scriteria; 
    bool _active; 
    static std::map<int,std::string> _scriterias;
  };
  
  template <class TGenoPheno>
    std::map<int,std::string> CMAStopCriteria<TGenoPheno>::_scriterias = {{CONT,"OK"},
                                      {AUTOMAXITER,"The automatically set maximal number of iterations per run has been reached"},
                                      {TOLHISTFUN,"[Success] The optimization has converged"},
                                      {EQUALFUNVALS,"[Partial Success] The objective function values are the same over too many iterations, check the formulation of your objective function"},
                                      {TOLX,"[Partial Success] All components of covariance matrix are very small (e.g. < 1e-12)"},
                                      {TOLUPSIGMA,"[Error] Mismatch between step size increase and decrease of all eigenvalues in covariance matrix. Try to restart the optimization."},
                                      {STAGNATION,"[Partial Success] Median of newest values is not smaller than the median of older values"},
                                      {CONDITIONCOV,"[Error] The covariance matrix's condition number exceeds 1e14. Check out the formulation of your problem"},
                                      {NOEFFECTAXIS,"[Partial Success] Mean remains constant along search axes"},
                                      {NOEFFECTCOOR,"[Partial Success] Mean remains constant in coordinates"},
                                      {MAXFEVALS,"The maximum number of function evaluations allowed for optimization has been reached"},
                                      {MAXITER,"The maximum number of iterations specified for optimization has been reached"},
                                      {FTARGET,"[Success] The objective function target value has been reached"}};

}

#endif