// Copyright (C) 2016 - IIT Bombay - FOSSEE
//
// This file must be used under the terms of the CeCILL.
// This source file is licensed as described in the file COPYING, which
// you should have received as part of this distribution.  The terms
// are also available at
// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
// Author: Harpreet Singh
// Organization: FOSSEE, IIT Bombay
// Email: toolbox@scilab.in

#ifndef minbndTMINLP_HPP
#define minbndTMINLP_HPP

#include "BonTMINLP.hpp"
#include "IpTNLP.hpp"
#include "call_scilab.h"

using namespace  Ipopt;
using namespace Bonmin;
    
class minbndTMINLP : public TMINLP
{
	private:

  	Index numVars_;	            //Number of input variables
  	
  	Index intconSize_;
 
    Number *lb_= NULL;	 		//lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables.

    Number *ub_= NULL;	 		//ub_ is a pointer to a matrix of size of 1*numVars_ with upper bound of all variables.

    Number *finalX_= NULL;		//finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables.

  	Number finalObjVal_;        //finalObjVal_ is a scalar with the final value of the objective.

    Number *intcon_ = NULL;

  	int status_;			 		//Solver return status
  	minbndTMINLP(const minbndTMINLP&);
  	minbndTMINLP& operator=(const minbndTMINLP&);

public:
	// Constructor
    	minbndTMINLP(Index nV, Number *lb, Number *ub, Index intconSize, Number *intcon):numVars_(nV),lb_(lb),ub_(ub),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){	}
  
	/** default destructor */
  	virtual ~minbndTMINLP();

  	virtual bool get_variables_types(Index n, VariableType* var_types);
 
    virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types);

    virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types);
  	
  	/** Method to return some info about the nlp */
  	virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
                            Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style);

  	/** Method to return the bounds for my problem */
  	virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u,
                               Index m, Number* g_l, Number* g_u);

  	/** Method to return the starting point for the algorithm */
  	virtual bool get_starting_point(Index n, bool init_x, Number* x,
                                  bool init_z, Number* z_L, Number* z_U,
                                  Index m, bool init_lambda,
                                  Number* lambda);

  	/** Method to return the objective value */
  	virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value);

  	/** Method to return the gradient of the objective */
  	virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f);

  	/** Method to return the constraint residuals */
  	virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g);

  	/** Method to return:
  	*   1) The structure of the jacobian (if "values" is NULL)
   	*   2) The values of the jacobian (if "values" is not NULL)
   	*/
  	virtual bool eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values);

  	/** Method to return:
   	*   1) The structure of the hessian of the lagrangian (if "values" is NULL)
   	*   2) The values of the hessian of the lagrangian (if "values" is not NULL)
   	*/
  	virtual bool eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values);

  	/** This method is called when the algorithm is complete so the TNLP can store/write the solution */
  	virtual void finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value);
  	
  	virtual const SosInfo * sosConstraints() const{return NULL;}
    virtual const BranchingInfo* branchingInfo() const{return NULL;}
  
  	const double * getX();		//Returns a pointer to a matrix of size of 1*numVars_ 
					//with final value for the primal variables.
  
  	const double * getGrad();       //Returns a pointer to a matrix of size of 1*numVars_ 
					//with final value of gradient for the primal variables.

  	const double * getHess();       //Returns a pointer to a matrix of size of numVars_*numVars_ 
					//with final value of hessian for the primal variables.

  	double getObjVal();		//Returns the output of the final value of the objective.

  	double iterCount();		//Returns the iteration count

  	int returnStatus();		//Returns the status count
};

#endif