//////////////////////////////////////////////////////////////////////
//
//	DerivativeBasedOptimizer.h: interface of DerivativeBasedOptimizer class.
//
//	Written by: Matthias Wacker
//
//////////////////////////////////////////////////////////////////////

#ifndef _DERIVATIVE_BASED_OPTIMIZER_H_
#define _DERIVATIVE_BASED_OPTIMIZER_H_


#include "optimization/SimpleOptimizer.h"

/*!
	class Abstract base class of all derivative based optimizers.
 */
class DerivativeBasedOptimizer : public SimpleOptimizer
{
public:
		typedef  SimpleOptimizer	SuperClass;	

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

		///
		///	Copy constructor
		///	\param opt .. optimizer to copy
		///
		DerivativeBasedOptimizer( const DerivativeBasedOptimizer &opt);

	        ///
		///	Destructor.
	        ///
		virtual ~DerivativeBasedOptimizer();

		///
		///	enumeration for the return reasons of an optimizer,
		///	has all elements of the SuperClass optimizer
		///
		enum {	SUCCESS_GRADIENTTOL = _to_continue_,
				_to_continue_
		};


		///
		///	\brief Set gradient tolerance abort criteria
		///	
		///	Set parameter tolerance abort criteria. While iterating, if the gradientnorm gets
		///	below the given threshold. The optimization stops and returns SUCCESS if 
		///	active is 'true'
		///
		///	\param active : bool to activate the criteria (true == active..)
		///	\param norm : representing the threshold
		///
		void setGradientTol(bool active, double norm);
		
		///
		///	Get the gradient tolerance abort criteria
		///	\return double representing the threshold
		///
		inline double getGradientTol();

		///
		///	Get numerical Gradient on position x; use central difference with a maskwitdh of maskWidth.
		///	
		///	  grad(f(x))_i \approx 
		///	  {
		///						[  f( x + (0, ... ,0,maskWidth(i,0),0, ... ,0 ) ) 
		///						 - f( x - (0, ... ,0,maskWidth(i,0),0, ... ,0 ) )]
		///						 / (2 * maskWidth(i,0))
		///	  }
		///
		///	  \forall i \in [1, ... ,m_numberOfParameters]
		///				
		const optimization::matrix_type  getNumericalGradient(const optimization::matrix_type & x , const optimization::matrix_type & maskWidth);

		///
		///	Get the anylytical Gradient of the costfunction (if available) sign already inverted for maximization.
		///
		const optimization::matrix_type  getAnalyticalGradient(const optimization::matrix_type & x);
		
		///
		///	UseAnalyticalGradients, if possible
		///
		void useAnalyticalGradients(bool useAnalyticalGradients);

		///
		///	Get the analytical Hessian of the costfunction (if available )
		///	sign already inverted for maximization
		///
		const optimization::matrix_type getAnalyticalHessian(const optimization::matrix_type & x);

		
protected:
		///
		///	initialize
		///
		void init();

		///
		///	the gradient
		///
		optimization::matrix_type m_gradient;
		
		///
		///	gradient tolerance threshold
		///
		double m_gradientTol;

		///
		///	gradient tolerance active
		///
		bool m_gradientTolActive;

		///
		///	use numerical or analytical Gradient Computation
		///
		bool m_analyticalGradients;
	
};

#endif