//////////////////////////////////////////////////////////////////////
//
//	CombinatorialOptimizer.h: interface of combinatorial optimizers.
//
//////////////////////////////////////////////////////////////////////

#ifndef _COMBINATORIAL_OPTIMIZER_
#define _COMBINATORIAL_OPTIMIZER_


#include "core/optimization/blackbox/SimpleOptimizer.h"
#include "core/optimization/blackbox/Definitions_core_opt.h"

namespace OPTIMIZATION
{

  ///
  ///	class for the Combinatorial optimizers
  ///
  /// HowToUse:
  /// 
  ///	  * use setMode to specify which algorithm to use (simulated annealing, etc.)
  ///	  * use setStepLength to set the variance for the random number generation 
  ///	  * use setParameters() to set the start point
  ///	  * use setFastMode() to let the random number generation contract with the control sequence
  ///	  * use setControlSeq() to set the parameters for the control sequence
  ///	  * call init()
  ///	  * call optimize()
  ///
  /// 
  ///	Implemented Abort criteria:
  ///		
  ///	  * maximum number of iterations
  ///	  *	time limit
  ///	  * parameter bounds
  ///	  
  ///	Additional return reason:
  ///	
  ///
  ///
  ///
  ///
  class CombinatorialOptimizer : public SimpleOptimizer
  {
    public:

      typedef  SimpleOptimizer SuperClass;
      typedef  OPTIMIZATION::matrix_type matrix_type;

      ///
      ///	Constructor.
      ///	@param loger : OptLogBase * to existing log class
      ///
      CombinatorialOptimizer(OptLogBase *loger=NULL);

      ///
      ///	Copy constructor
      ///	@param opt .. optimizer to copy
      ///
      CombinatorialOptimizer( const CombinatorialOptimizer &opt);

      ///
      ///	operator =
      ///
      CombinatorialOptimizer & operator=(const CombinatorialOptimizer &opt);

            ///
      ///	Destructor.
            ///
            ~CombinatorialOptimizer();

      /*!
        enumeration for the return reasons of an optimizer,
        has all elements of the SuperClass optimizer
      */
    
      ///
      ///	Do internal initializations
      ///
      void init();
      
      ///
      ///	start the optmization
      ///
      int optimize();

      ///
      ///	set mode:
      ///	
      ///	  1 = simulated annealing
      ///	  2 = threshold acceptance
      ///	  3 = great deluge
      ///	  4 = stochastic relaxation
      ///	  5 = mix of simualted annealing and "constant" great deluge
      ///			(that means sa is used but there is a hard acceptance threshold.
      ///			 function values have to be below that threshold to have the 
      ///			 chance to be accepted. This can be used to do some kind of constraint
      ///			 handling when they are penalized enough..)
      ///		
      ///
      bool setMode(int mode);
      

      ///
      ///	set the parameters for the control sequence
      ///	where t_k = t_0 * alpha^k
      ///	
      ///	@param T0 >= 0
      ///	@param alpha > 0
      ///
      ///	@return true in case of success
      ///
      bool setControlSeqParam(double T0, double alpha);

      ///*!
      //	@param steplength double value that is used as variance to generate new points
      //*/
      //bool setSteplength(double steplength);

      ///
      ///	set threshold used in mode 5 as constant deluge control sequence
      ///	@param thres 
      ///
      inline void setThreshold(double thres){m_threshold = thres;};

      ///
      ///	use fast contraction mode: the control sequence is used 
      ///	for the variance to generate new points
      ///
      inline void setFastMode(bool fastmode){m_fast = fastmode;};

    private:

      ///
      ///	generate new point
      ///
      matrix_type generatePoint();

      ///
      ///	accept point
      ///
      bool accepptPoint(double oldValue, double newValue);

      ///
      ///	algorithm mode
      ///
      int m_mode;

      ///
      ///	fast mode point generation?
      ///
      bool m_fast;

      ///
      ///	control sequence Tk= T_0 * m_alpha^m_numIter
      ///
      double m_T0;

      ///
      ///	control sequence Tk= T_0 * m_alpha^m_numIter
      ///
      double m_Tk;

      ///
      ///	control sequence Tk= T_0 * m_alpha^m_numIter
      ///
      double m_alpha;

      ///
      ///	Threshold for mode nr. 5
      ///
      double m_threshold;

      ///
      ///	steplength used as variance to generate new points
      ///
      double m_stepLength;
      
  };//class
}//namespace

#endif