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+// 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, Pranav Deshpande and Akshay Miterani
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
+#define __USE_DEPRECATED_STACK_FUNCTIONS__
+#ifndef minuncTMINLP_HPP
+#define minuncTMINLP_HPP
+
+#include "BonTMINLP.hpp"
+#include "IpTNLP.hpp"
+#include "call_scilab.h"
+
+using namespace  Ipopt;
+using namespace Bonmin;
+    
+class minuncTMINLP : public TMINLP
+{
+	private:
+
+  	Index numVars_;	                 //Number of input variables
+  	
+  	Index intconSize_;
+ 
+  	const Number *varGuess_= NULL;	 //varGuess_ is a pointer to a matrix of size of 1*numVars_ with initial guess 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
+  	minuncTMINLP(const minuncTMINLP&);
+  	minuncTMINLP& operator=(const minuncTMINLP&);
+
+public:
+  // Constructor
+    	minuncTMINLP(Index nV, Number *x0, Index intconSize, Number *intcon):numVars_(nV),varGuess_(x0),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){	}
+  
+    	/** default destructor */
+  	virtual ~minuncTMINLP();
+
+  	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