Structure updated and intqpipopt files added

12 files changed, 0 insertions, 2347 deletions

diff --git a/build/cpp/cpp_intfminbnd.cpp b/build/cpp/cpp_intfminbnd.cpp
deleted file mode 100644
index 4914111..0000000
--- a/build/cpp/cpp_intfminbnd.cpp
+++ /dev/null
@@ -1,172 +0,0 @@
-// 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
-
-#include "CoinPragma.hpp"
-#include "CoinTime.hpp"
-#include "CoinError.hpp"
-
-#include "BonOsiTMINLPInterface.hpp"
-#include "BonIpoptSolver.hpp"
-#include "minbndTMINLP.hpp"
-#include "BonCbc.hpp"
-#include "BonBonminSetup.hpp"
-
-#include "BonOACutGenerator2.hpp"
-#include "BonEcpCuts.hpp"
-#include "BonOaNlpOptim.hpp"
-
-#include "sci_iofunc.hpp"
-extern  "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-int cpp_intfminbnd(char *fname)
-{
-  	using namespace Ipopt;
-  	using namespace Bonmin;
-
-	CheckInputArgument(pvApiCtx, 8, 8);
-	CheckOutputArgument(pvApiCtx, 3, 3);
-
-	// Input arguments
-	Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL, *lb = NULL, *ub = NULL;
-	static unsigned int nVars = 0;
-	unsigned int temp1 = 0,temp2 = 0, iret = 0;
-	int x0_rows, x0_cols,intconSize;
-	Number *intcon = NULL,*options=NULL, *ifval=NULL;
-	
-	// Output arguments
-	Number *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0;
-	Number dual_inf, constr_viol, complementarity, kkt_error;
-	int rstatus = 0;
-	
-	if(getDoubleMatrixFromScilab(4, &x0_rows, &x0_cols, &lb))
-	{
-		return 1;
-	}
-	
-	if(getDoubleMatrixFromScilab(5, &x0_rows, &x0_cols, &ub))
-	{
-		return 1;
-	}
-
-	// Getting intcon
-	if (getDoubleMatrixFromScilab(6,&intconSize,&temp2,&intcon))
-	{
-		return 1;
-	}
-
-    //Initialization of parameters
-	nVars=x0_rows;
-	temp1 = 1;
-	temp2 = 1;
-
-	//Getting parameters
-	if (getFixedSizeDoubleMatrixInList(7,2,temp1,temp2,&integertolerance))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(7,4,temp1,temp2,&maxnodes))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(7,6,temp1,temp2,&cputime))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(7,8,temp1,temp2,&allowablegap))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(7,10,temp1,temp2,&max_iter))
-	{
-		return 1;
-	}
-
-	SmartPtr<minbndTMINLP> tminlp = new minbndTMINLP(nVars,lb,ub,intconSize,intcon);
-
-	BonminSetup bonmin;
-	bonmin.initializeOptionsAndJournalist();
-
-	bonmin.options()->SetStringValue("mu_oracle","loqo");
-    bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance);
-    bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes);
-    bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime);
-    bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap);
-    bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter);
-
-	//Now initialize from tminlp
-	bonmin.initialize(GetRawPtr(tminlp));
-
-	//Set up done, now let's branch and bound
-	try {
-	Bab bb;
-	bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc
-	}
-	catch(TNLPSolver::UnsolvedError *E) {
-	Scierror(999, "\nIpopt has failed to solve the problem!\n");
-	}
-	catch(OsiTMINLPInterface::SimpleError &E) {
-	  Scierror(999, "\nFailed to solve a problem!\n");
-	}
-	catch(CoinError &E) {
-	  Scierror(999, "\nFailed to solve a problem!\n");
-	}
-	rstatus=tminlp->returnStatus();
-
-	if(rstatus==0 ||rstatus== 3)
-	{
-		fX = tminlp->getX();
-		ObjVal = tminlp->getObjVal();
-		if (returnDoubleMatrixToScilab(1, nVars, 1, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-	
-	}
-	else
-	{
-		if (returnDoubleMatrixToScilab(1, 0, 0, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-	
-	}
-
-	return 0;
-	}
-}
-
diff --git a/build/cpp/cpp_intfmincon.cpp b/build/cpp/cpp_intfmincon.cpp
deleted file mode 100644
index d921128..0000000
--- a/build/cpp/cpp_intfmincon.cpp
+++ /dev/null
@@ -1,189 +0,0 @@
-// 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
-
-#include "CoinPragma.hpp"
-#include "CoinTime.hpp"
-#include "CoinError.hpp"
-
-#include "BonOsiTMINLPInterface.hpp"
-#include "BonIpoptSolver.hpp"
-#include "minconTMINLP.hpp"
-#include "BonCbc.hpp"
-#include "BonBonminSetup.hpp"
-
-#include "BonOACutGenerator2.hpp"
-#include "BonEcpCuts.hpp"
-#include "BonOaNlpOptim.hpp"
-
-#include "sci_iofunc.hpp"
-extern  "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-int cpp_intfmincon(char *fname)
-{
-  	using namespace Ipopt;
-  	using namespace Bonmin;
-
-	CheckInputArgument(pvApiCtx, 13, 13);
-	CheckOutputArgument(pvApiCtx, 3, 3);
-
-	// Input arguments
-	Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL;
-	Number *x0 = NULL, *lb = NULL, *ub = NULL,*conLb = NULL, *conUb = NULL,*LC = NULL;
-	static unsigned int nVars = 0,nCons = 0;
-	unsigned int temp1 = 0,temp2 = 0, iret = 0;
-	int x0_rows, x0_cols,intconSize;
-	Number *intcon = NULL,*options=NULL, *ifval=NULL;
-	
-	// Output arguments
-	Number *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0;
-	Number dual_inf, constr_viol, complementarity, kkt_error;
-	int rstatus = 0;
-	
-	if(getDoubleMatrixFromScilab(6, &nVars, &x0_cols, &x0))
-	{
-		return 1;
-	}
-	
-	if(getDoubleMatrixFromScilab(7, &x0_rows, &x0_cols, &lb))
-	{
-		return 1;
-	}
-
-	if(getDoubleMatrixFromScilab(8, &x0_rows, &x0_cols, &ub))
-	{
-		return 1;
-	}
-
-	if(getDoubleMatrixFromScilab(9, &nCons, &x0_cols, &conLb))
-	{
-		return 1;
-	}
-
-	if(getDoubleMatrixFromScilab(10, &x0_rows, &x0_cols, &conUb))
-	{
-		return 1;
-	}
-
-	// Getting intcon
-	if (getDoubleMatrixFromScilab(11,&intconSize,&temp2,&intcon))
-	{
-		return 1;
-	}
-
-	if (getDoubleMatrixFromScilab(13,&temp1,&temp2,&LC))
-	{
-		return 1;
-	}
-
-    //Initialization of parameters
-	temp1 = 1;
-	temp2 = 1;
-
-	//Getting parameters
-	if (getFixedSizeDoubleMatrixInList(12,2,temp1,temp2,&integertolerance))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(12,4,temp1,temp2,&maxnodes))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(12,6,temp1,temp2,&cputime))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(12,8,temp1,temp2,&allowablegap))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(12,10,temp1,temp2,&max_iter))
-	{
-		return 1;
-	}
-	
-	SmartPtr<minconTMINLP> tminlp = new minconTMINLP(nVars,x0,lb,ub,(unsigned int)LC,nCons,conLb,conUb,intconSize,intcon);
-	
-	BonminSetup bonmin;
-	bonmin.initializeOptionsAndJournalist();
-	bonmin.options()->SetStringValue("mu_oracle","loqo");
-	bonmin.options()->SetIntegerValue("bonmin.print_level",5);
-    bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance);
-    bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes);
-    bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime);
-    bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap);
-    bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter);
-
-	//Now initialize from tminlp
-	bonmin.initialize(GetRawPtr(tminlp));
-
-	//Set up done, now let's branch and bound
-	try {
-	Bab bb;
-	bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc
-	}
-	catch(TNLPSolver::UnsolvedError *E) {
-	}
-	catch(OsiTMINLPInterface::SimpleError &E) {
-	}
-	catch(CoinError &E) {
-	}
-	rstatus=tminlp->returnStatus();
-
-	if(rstatus==0 ||rstatus== 3)
-	{
-		fX = tminlp->getX();
-		ObjVal = tminlp->getObjVal();
-		if (returnDoubleMatrixToScilab(1, nVars, 1, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-	
-	}
-	else
-	{
-		if (returnDoubleMatrixToScilab(1, 0, 0, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-	
-	}
-
-	return 0;
-	}
-}
-
diff --git a/build/cpp/cpp_intfminunc.cpp b/build/cpp/cpp_intfminunc.cpp
deleted file mode 100644
index 233ead3..0000000
--- a/build/cpp/cpp_intfminunc.cpp
+++ /dev/null
@@ -1,174 +0,0 @@
-// 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
-
-#include "CoinPragma.hpp"
-#include "CoinTime.hpp"
-#include "CoinError.hpp"
-
-#include "BonOsiTMINLPInterface.hpp"
-#include "BonIpoptSolver.hpp"
-#include "minuncTMINLP.hpp"
-#include "BonCbc.hpp"
-#include "BonBonminSetup.hpp"
-
-#include "BonOACutGenerator2.hpp"
-#include "BonEcpCuts.hpp"
-#include "BonOaNlpOptim.hpp"
-
-#include "sci_iofunc.hpp"
-extern  "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-int cpp_intfminunc(char *fname)
-{
-  using namespace Ipopt;
-  using namespace Bonmin;
-
-	CheckInputArgument(pvApiCtx, 8, 8); // We need total 12 input arguments.
-	CheckOutputArgument(pvApiCtx, 3, 3);  // 3 output arguments
-	
-	//Function pointers, input matrix(Starting point) pointer, flag variable 
-	int* funptr=NULL;
-	double* x0ptr=NULL;
-	    
-	// Input arguments
-	Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL;
-	static unsigned int nVars = 0,nCons = 0;
-	unsigned int temp1 = 0,temp2 = 0, iret = 0;
-	int x0_rows, x0_cols;
-	double *intcon = NULL,*options=NULL, *ifval=NULL;
-	int intconSize;
-	
-	// Output arguments
-	double *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0;
-	double dual_inf, constr_viol, complementarity, kkt_error;
-	int rstatus = 0;
-	int int_fobj_eval, int_constr_eval, int_fobj_grad_eval, int_constr_jac_eval, int_hess_eval;
-
-	//x0(starting point) matrix from scilab
-	if(getDoubleMatrixFromScilab(4, &x0_rows, &x0_cols, &x0ptr))
-	{
-		return 1;
-	}
-
-	nVars=x0_rows;
-		
-	// Getting intcon
-	if (getDoubleMatrixFromScilab(5,&intconSize,&temp2,&intcon))
-	{
-		return 1;
-	}
-
-	temp1 = 1;
-	temp2 = 1;
-
-	//Getting parameters
-	if (getFixedSizeDoubleMatrixInList(6,2,temp1,temp2,&integertolerance))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(6,4,temp1,temp2,&maxnodes))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(6,6,temp1,temp2,&cputime))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(6,8,temp1,temp2,&allowablegap))
-	{
-		return 1;
-	}
-	if (getFixedSizeDoubleMatrixInList(6,10,temp1,temp2,&max_iter))
-	{
-		return 1;
-	}
-	
-	SmartPtr<minuncTMINLP> tminlp = new minuncTMINLP(nVars, x0ptr, intconSize, intcon);
-
-  BonminSetup bonmin;
-  bonmin.initializeOptionsAndJournalist();
-
-  // Here we can change the default value of some Bonmin or Ipopt option
-	bonmin.options()->SetStringValue("mu_oracle","loqo");
-    bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance);
-    bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes);
-    bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime);
-    bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap);
-    bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter);
-  
-  //Now initialize from tminlp
-  bonmin.initialize(GetRawPtr(tminlp));
-  
-  //Set up done, now let's branch and bound
-  try {
-    Bab bb;
-    bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc
-  }
-  catch(TNLPSolver::UnsolvedError *E) {
-    //There has been a failure to solve a problem with Ipopt.
-    Scierror(999, "\nIpopt has failed to solve the problem!\n");
-  }
-  catch(OsiTMINLPInterface::SimpleError &E) {
-      Scierror(999, "\nFailed to solve a problem!\n");
-	}
-  catch(CoinError &E) {
-      Scierror(999, "\nFailed to solve a problem!\n");
-	}
-	rstatus=tminlp->returnStatus();
-	if(rstatus==0 ||rstatus== 3)
-	{
-		fX = tminlp->getX();
-		ObjVal = tminlp->getObjVal();
-		if (returnDoubleMatrixToScilab(1, nVars, 1, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-		
-	}
-	else
-	{
-		if (returnDoubleMatrixToScilab(1, 0, 0, fX))
-		{
-			return 1;
-		}
-
-		if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal))
-		{
-			return 1;
-		}
-
-		if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus))
-		{
-			return 1;
-		}
-  }
-
-  return 0;
- }
-}
-
diff --git a/build/cpp/minbndTMINLP.hpp b/build/cpp/minbndTMINLP.hpp
deleted file mode 100644
index 581d5ce..0000000
--- a/build/cpp/minbndTMINLP.hpp
+++ /dev/null
@@ -1,114 +0,0 @@
-// 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
diff --git a/build/cpp/minconTMINLP.hpp b/build/cpp/minconTMINLP.hpp
deleted file mode 100644
index 5b3006a..0000000
--- a/build/cpp/minconTMINLP.hpp
+++ /dev/null
@@ -1,124 +0,0 @@
-// 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
-
-#ifndef minconTMINLP_HPP
-#define minconTMINLP_HPP
-
-#include "BonTMINLP.hpp"
-#include "IpTNLP.hpp"
-#include "call_scilab.h"
-
-using namespace  Ipopt;
-using namespace Bonmin;
-    
-class minconTMINLP : public TMINLP
-{
-	private:
-
-    Index numVars_;             //Number of variables
-
-    Index numCons_;             //Number of constraints
-
-    Index numLC_;               //Number of Linear constraints
-  	
-  	Index intconSize_;
- 
-    Number *x0_= NULL;          //lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables.
- 
-    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 *conLb_= NULL;       //conLb_ is a pointer to a matrix of size of numCon_*1 with lower bound of all constraints.
-
-    Number *conUb_= NULL;       //conUb_ is a pointer to a matrix of size of numCon_*1 with upper bound of all constraints.
-
-    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
-  	minconTMINLP(const minconTMINLP&);
-  	minconTMINLP& operator=(const minconTMINLP&);
-
-public:
-	// Constructor
-    	minconTMINLP(Index nV, Number *x0, Number *lb, Number *ub, Index nLC, Index nCons, Number *conlb, Number *conub, Index intconSize, Number *intcon):numVars_(nV),x0_(x0),lb_(lb),ub_(ub),numLC_(nLC),numCons_(nCons),conLb_(conlb),conUb_(conub),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){	}
-  
-	/** default destructor */
-  	virtual ~minconTMINLP();
-
-  	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
diff --git a/build/cpp/minuncTMINLP.hpp b/build/cpp/minuncTMINLP.hpp
deleted file mode 100644
index 2b6e954..0000000
--- a/build/cpp/minuncTMINLP.hpp
+++ /dev/null
@@ -1,113 +0,0 @@
-// 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
diff --git a/build/cpp/sci_iofunc.cpp b/build/cpp/sci_iofunc.cpp
deleted file mode 100644
index f05839c..0000000
--- a/build/cpp/sci_iofunc.cpp
+++ /dev/null
@@ -1,333 +0,0 @@
-// Symphony Toolbox for Scilab
-// (Definition of) Functions for input and output from Scilab
-// By Keyur Joshi
-
-#include "api_scilab.h"
-#include "Scierror.h"
-#include "sciprint.h"
-#include "BOOL.h"
-#include <localization.h>
-#include "call_scilab.h"
-#include <string.h>
-
-
-using namespace std;
-
-int getDoubleFromScilab(int argNum, double *dest)
-{
-	//data declarations
-	SciErr sciErr;
-	int iRet,*varAddress;
-	const char errMsg[]="Wrong type for input argument #%d: A double is expected.\n";
-	const int errNum=999;
-	//get variable address
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	//check that it is a non-complex double
-	if ( !isDoubleType(pvApiCtx,varAddress) ||  isVarComplex(pvApiCtx,varAddress) )
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	//retrieve and store
-	iRet = getScalarDouble(pvApiCtx, varAddress, dest);
-	if(iRet)
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	return 0;
-}
-
-int getUIntFromScilab(int argNum, int *dest)
-{
-	SciErr sciErr;
-	int iRet,*varAddress;
-	double inputDouble;
-	const char errMsg[]="Wrong type for input argument #%d: A nonnegative integer is expected.\n";
-	const int errNum=999;
-	//same steps as above
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	if ( !isDoubleType(pvApiCtx,varAddress) ||  isVarComplex(pvApiCtx,varAddress) )
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	iRet = getScalarDouble(pvApiCtx, varAddress, &inputDouble);
-	//check that an unsigned int is stored in the double by casting and recasting
-	if(iRet || ((double)((unsigned int)inputDouble))!=inputDouble)
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	*dest=(unsigned int)inputDouble;
-	return 0;
-}
-
-int getIntFromScilab(int argNum, int *dest)
-{
-	SciErr sciErr;
-	int iRet,*varAddress;
-	double inputDouble;
-	const char errMsg[]="Wrong type for input argument #%d: An integer is expected.\n";
-	const int errNum=999;
-	//same steps as above
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	if ( !isDoubleType(pvApiCtx,varAddress) ||  isVarComplex(pvApiCtx,varAddress) )
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	iRet = getScalarDouble(pvApiCtx, varAddress, &inputDouble);
-	//check that an int is stored in the double by casting and recasting
-	if(iRet || ((double)((int)inputDouble))!=inputDouble)
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	*dest=(int)inputDouble;
-	return 0;
-}
-
-int getFixedSizeDoubleMatrixFromScilab(int argNum, int rows, int cols, double **dest)
-{
-	int *varAddress,inputMatrixRows,inputMatrixCols;
-	SciErr sciErr;
-	const char errMsg[]="Wrong type for input argument #%d: A matrix of double of size %d by %d is expected.\n";
-	const int errNum=999;
-	//same steps as above
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	if ( !isDoubleType(pvApiCtx,varAddress) ||  isVarComplex(pvApiCtx,varAddress) )
-	{
-		Scierror(errNum,errMsg,argNum,rows,cols);
-		return 1;
-	}
-	sciErr = getMatrixOfDouble(pvApiCtx, varAddress, &inputMatrixRows, &inputMatrixCols,NULL);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	//check that the matrix has the correct number of rows and columns
-	if(inputMatrixRows!=rows || inputMatrixCols!=cols)
-	{
-		Scierror(errNum,errMsg,argNum,rows,cols);
-		return 1;
-	}
-	getMatrixOfDouble(pvApiCtx, varAddress, &inputMatrixRows, &inputMatrixCols, dest);
-	return 0;
-}
-
-int getDoubleMatrixFromScilab(int argNum, int *rows, int *cols, double **dest)
-{
-	int *varAddress;
-	SciErr sciErr;
-	const char errMsg[]="Wrong type for input argument #%d: A matrix of double is expected.\n";
-	const int errNum=999;
-	//same steps as above
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	if ( !isDoubleType(pvApiCtx,varAddress) ||  isVarComplex(pvApiCtx,varAddress) )
-	{
-		Scierror(errNum,errMsg,argNum);
-		return 1;
-	}
-	getMatrixOfDouble(pvApiCtx, varAddress, rows, cols, dest);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	return 0;
-}
-
-int getFixedSizeDoubleMatrixInList(int argNum, int itemPos, int rows, int cols, double **dest)
-{
-	int *varAddress,inputMatrixRows,inputMatrixCols;
-	SciErr sciErr;
-	const char errMsg[]="Wrong type for input argument #%d: A matrix of double of size %d by %d is expected.\n";
-	const int errNum=999;
-	//same steps as above
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-
-	getMatrixOfDoubleInList(pvApiCtx, varAddress, itemPos, &rows, &cols, dest);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-	return 0;
-}
-
-int getStringFromScilab(int argNum,char **dest)
-{
-	int *varAddress,inputMatrixRows,inputMatrixCols;
-	SciErr sciErr;
-	sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress);
-
-	//check whether there is an error or not.
-	if (sciErr.iErr)
-    	{
-        	printError(&sciErr, 0);
-        	return 1;
-		}
-	if ( !isStringType(pvApiCtx,varAddress) )
-		{
-			Scierror(999,"Wrong type for input argument 1: A file name is expected.\n");
-			return 1;
-		}
-    //read the value in that pointer pointing to file name
-	getAllocatedSingleString(pvApiCtx, varAddress, dest);
-    
-}
-
-bool getFunctionFromScilab(int n,char name[], double *x,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest)
-{	
-	double check;
-  	createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF, 1, n, x);  
-  	C2F(scistring)(&posFirstElementOnStackForSF,name,&nOfLhsOnSF,&nOfRhsOnSF,(unsigned long)strlen(name));
-    
-  	if(getDoubleFromScilab(posFirstElementOnStackForSF+1,&check))
-  	{
-		return true;
-	}
-	if (check==1)
-	{
-		return true;
-	}	
-	else
-	{ 
-		int x_rows, x_cols;
-		if(getDoubleMatrixFromScilab(posFirstElementOnStackForSF, &x_rows, &x_cols, dest))
-  		{
-			sciprint("No results ");
-			return true;
-			
-  		}
-	}	
-	return 0;
-}
-
-bool getHessFromScilab(int n,int numConstr_,char name[], double *x,double *obj,double *lambda,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest)
-{	
-	double check;
-  	createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF, 1, n, x);
-  	createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF+1, 1, 1, obj);
-	createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF+2, 1, numConstr_, lambda);
-  	C2F(scistring)(&posFirstElementOnStackForSF,name,&nOfLhsOnSF,&nOfRhsOnSF,(unsigned long)strlen(name));
-                               
-  	if(getDoubleFromScilab(posFirstElementOnStackForSF+1,&check))
-  	{
-		return true;
-	}
-	if (check==1)
-	{
-		return true;
-	}	
-	else
-	{ 
-		int x_rows, x_cols;
-		if(getDoubleMatrixFromScilab(posFirstElementOnStackForSF, &x_rows, &x_cols, dest))
-  		{
-			sciprint("No results ");
-			return 1;	
-  		}
-	}	
-	return 0;
-}
-
-int return0toScilab()
-{
-	int iRet;
-	//create variable in scilab
-	iRet = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1,0);
-	if(iRet)
-	{
-		/* If error, no return variable */
-		AssignOutputVariable(pvApiCtx, 1) = 0;
-		return 1;
-	}
-	//make it the output variable
-	AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1;
-	//return it to scilab
-	//ReturnArguments(pvApiCtx);
-	return 0;
-}
-
-int returnDoubleToScilab(double retVal)
-{
-	int iRet;
-	//same steps as above
-	iRet = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1,retVal);
-	if(iRet)
-	{
-		/* If error, no return variable */
-		AssignOutputVariable(pvApiCtx, 1) = 0;
-		return 1;
-	}
-	AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1;
-	//ReturnArguments(pvApiCtx);
-	return 0;
-}
-
-int returnDoubleMatrixToScilab(int itemPos, int rows, int cols, double *dest)
-{
-	SciErr sciErr;
-	//same steps as above
-	sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + itemPos, rows, cols, dest);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-
-	AssignOutputVariable(pvApiCtx, itemPos) = nbInputArgument(pvApiCtx)+itemPos;
-
-	return 0;
-}
-
-int returnIntegerMatrixToScilab(int itemPos, int rows, int cols, int *dest)
-{
-	SciErr sciErr;
-	//same steps as above
-	sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + itemPos, rows, cols, dest);
-	if (sciErr.iErr)
-	{
-		printError(&sciErr, 0);
-		return 1;
-	}
-
-	AssignOutputVariable(pvApiCtx, itemPos) = nbInputArgument(pvApiCtx)+itemPos;
-
-	return 0;
-}
-
-
diff --git a/build/cpp/sci_iofunc.hpp b/build/cpp/sci_iofunc.hpp
deleted file mode 100644
index 7e18951..0000000
--- a/build/cpp/sci_iofunc.hpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// Symphony Toolbox for Scilab
-// (Declaration of) Functions for input and output from Scilab
-// By Keyur Joshi
-
-#ifndef SCI_IOFUNCHEADER
-#define SCI_IOFUNCHEADER
-
-//input
-int getDoubleFromScilab(int argNum, double *dest);
-int getUIntFromScilab(int argNum, int *dest);
-int getIntFromScilab(int argNum, int *dest);
-int getFixedSizeDoubleMatrixFromScilab(int argNum, int rows, int cols, double **dest);
-int getDoubleMatrixFromScilab(int argNum, int *rows, int *cols, double **dest);
-int getFixedSizeDoubleMatrixInList(int argNum, int itemPos, int rows, int cols, double **dest);
-int getStringFromScilab(int argNum,char** dest);
-bool getFunctionFromScilab(int n,char name[], double *x,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest);
-bool getHessFromScilab(int n,int numConstr_,char name[], double *x,double *obj,double *lambda,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest);
-
-//output
-int return0toScilab();
-int returnDoubleToScilab(double retVal);
-int returnDoubleMatrixToScilab(int itemPos, int rows, int cols, double *dest);
-int returnIntegerMatrixToScilab(int itemPos, int rows, int cols, int *dest);
-
-#endif //SCI_IOFUNCHEADER
diff --git a/build/cpp/sci_minbndTMINLP.cpp b/build/cpp/sci_minbndTMINLP.cpp
deleted file mode 100644
index f26c089..0000000
--- a/build/cpp/sci_minbndTMINLP.cpp
+++ /dev/null
@@ -1,218 +0,0 @@
-// Copyright (C) 2015 - IIT Bombay - FOSSEE
-//
-// Author: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: toolbox@scilab.in
-// 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
-
-#include "minbndTMINLP.hpp"
-#include "sci_iofunc.hpp"
-
-extern "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-#include <string.h>
-#include <assert.h>
-}
-
-using namespace Ipopt;
-using namespace Bonmin;
-
-minbndTMINLP::~minbndTMINLP()
-{
-	if(finalX_) delete[] finalX_;
-}
-
-// Set the type of every variable - CONTINUOUS or INTEGER
-bool minbndTMINLP::get_variables_types(Index n, VariableType* var_types)
-{
-  n = numVars_;
-  for(int i=0; i < n; i++)
-    var_types[i] = CONTINUOUS;
-  for(int i=0 ; i < intconSize_ ; ++i)
-  	var_types[(int)(intcon_[i]-1)] = INTEGER;
-  return true;
-}
-
-// The linearity of the variables - LINEAR or NON_LINEAR
-bool minbndTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
-{  return true; }
-
-// The linearity of the constraints - LINEAR or NON_LINEAR
-bool minbndTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
-{  return true;}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool minbndTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
-{
-	n=numVars_; // Number of variables
-	m=0; // Number of constraints
-	nnz_jac_g = 0; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian.
-	index_style=TNLP::C_STYLE; // Index style of matrices
-	return true;
-}
-
-//get variable and constraint bound info
-bool minbndTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
-{
-	unsigned int i;
-	for(i=0;i<n;i++)
-	{
-		x_l[i]=lb_[i]+0.0000001;
-		x_u[i]=ub_[i]-0.0000001;
-	}
-
-        g_l=NULL;
-        g_u=NULL;
-	return true;
-}
-
-// return the value of the constraints: g(x)
-bool minbndTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
-{
-  	// return the value of the constraints: g(x)
-  	g=NULL;
-  	return true;
-}
-
-// return the structure or values of the jacobian
-bool minbndTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values)
-{
- 	if (values == NULL) 
- 	{
-    		// return the structure of the jacobian of the constraints
-    		iRow=NULL; 
-    		jCol=NULL;
-  	}
-  	else 
-	{
-		values=NULL;
-  	}
-  	return true;
-}
-
-//get value of objective function at vector x
-bool minbndTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
-{	
-  	char name[20]="_f";
-	Number *obj;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
-	{
-		return false;
-	}
-	obj_value = *obj;
-  	return true;
-}
-
-//get value of gradient of objective function at vector x.
-bool minbndTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
-{
-	char name[20]="_gradf";
-  	Number *resg;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg))
-	{
-		return false;
-	}
-	
-	Index i;
-	for(i=0;i<numVars_;i++)
-	{
-		grad_f[i]=resg[i];
-	}
-  	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool minbndTMINLP::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)
-{
- 	assert(init_x == true);
-  	assert(init_z == false);
-  	assert(init_lambda == false);
-	if (init_x == true)
-	{ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			{x[var]=0.0;}//initialize with 0.
-	}
-	return true;
-}
-
-/*
- * Return either the sparsity structure of the Hessian of the Lagrangian, 
- * or the values of the Hessian of the Lagrangian  for the given values for
- * x,lambda,obj_factor.
-*/
-
-bool minbndTMINLP::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)
-{
-	double check;
-	if (values==NULL)
-	{
-		Index idx=0;
-		for (Index row = 0; row < numVars_; row++) 
-		{
-			for (Index col = 0; col <= row; col++)
-			{	iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  	}
-		}
-	}
-
-	else 
-	{	char name[20]="_gradhess";
-	  	Number *resh;
-		if (getFunctionFromScilab(n,name,x, 7, 1,2,&resh))
-		{
-			return false;
-		}
-		Index index=0;
-		for (Index row=0;row < numVars_ ;++row)
-		{
-			for (Index col=0; col <= row; ++col)
-			{
-				values[index++]=obj_factor*(resh[numVars_*row+col]);
-			}
-		}
-	}
-       	return true;
-}
-
-void minbndTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value)
-{
-	finalObjVal_ = obj_value;
-	status_ = status;
-	if(status==0 ||status== 3)
-	{
-		finalX_ =  new double[n];
-		for (Index i=0; i<numVars_; i++) 
-		{
-	    		 finalX_[i] = x[i];
-		}
-	}
-
-}
-
-const double * minbndTMINLP::getX()
-{	
-	return finalX_;
-}
-
-double minbndTMINLP::getObjVal()
-{	
-	return finalObjVal_;
-}
-
-int minbndTMINLP::returnStatus()
-{	
-	return status_;
-}
diff --git a/build/cpp/sci_minconTMINLP.cpp b/build/cpp/sci_minconTMINLP.cpp
deleted file mode 100644
index 350594d..0000000
--- a/build/cpp/sci_minconTMINLP.cpp
+++ /dev/null
@@ -1,324 +0,0 @@
-// Copyright (C) 2015 - IIT Bombay - FOSSEE
-//
-// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani
-// Organization: FOSSEE, IIT Bombay
-// Email: toolbox@scilab.in
-// 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
-
-#include "minconTMINLP.hpp"
-#include "sci_iofunc.hpp"
-
-extern "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-#include <string.h>
-#include <assert.h>
-}
-
-using namespace Ipopt;
-using namespace Bonmin;
-
-//#define DEBUG 0
-
-minconTMINLP::~minconTMINLP()
-{
-	if(finalX_) delete[] finalX_;
-}
-
-// Set the type of every variable - CONTINUOUS or INTEGER
-bool minconTMINLP::get_variables_types(Index n, VariableType* var_types)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_variables_types\n");
-	#endif
-  n = numVars_;
-  for(int i=0; i < n; i++)
-    var_types[i] = CONTINUOUS;
-  for(int i=0 ; i < intconSize_ ; ++i)
-  	var_types[(int)(intcon_[i]-1)] = INTEGER;
-  return true;
-}
-
-// The linearity of the variables - LINEAR or NON_LINEAR
-bool minconTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
-{  
-	#ifdef DEBUG
-  		sciprint("Code is in get_variables_linearity\n");
-	#endif
-	for(int i=0;i<n;i++)
-	{
-		  var_types[i] = Ipopt::TNLP::NON_LINEAR;
-	}
-	return true; }
-
-// The linearity of the constraints - LINEAR or NON_LINEAR
-bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
-{	
-
-	#ifdef DEBUG
-  		sciprint("Code is in get_constraints_linearity\n");
-	#endif
-	for(int i=0;i<numLC_;i++)
-	{
-		const_types[i] = Ipopt::TNLP::LINEAR;
-	}
-
-	for(int i=numLC_;i<m;i++)
-	{
-		const_types[i] = Ipopt::TNLP::NON_LINEAR;
-	}
-	  return true;}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool minconTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_nlp_info\n");
-	#endif
-	n=numVars_; // Number of variables
-	m=numCons_; // Number of constraints
-	nnz_jac_g = n*m; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*n; // No. of elements in Hessian of the Lagrangian.
-	index_style=TNLP::C_STYLE; // Index style of matrices
-	return true;
-}
-
-//get variable and constraint bound info
-bool minconTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_bounds_info\n");
-	#endif
-	unsigned int i;
-	for(i=0;i<n;i++)
-	{
-		x_l[i]=lb_[i];
-		x_u[i]=ub_[i];
-	}
-	for(i=0;i<m;i++)
-	{
-		g_l[i]=conLb_[i];
-        g_u[i]=conUb_[i];
-	}
-	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool minconTMINLP::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)
-{
- 	assert(init_x == true);
-  	assert(init_z == false);
-  	assert(init_lambda == false);
-	if (init_x == true)
-	{ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			{x[var]=x0_[var];}//initialize with 0.
-	}
-	return true;
-}
-
-//get value of objective function at vector x
-bool minconTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
-{	
-	#ifdef DEBUG
-  		sciprint("Code is eval_f\n");
-	#endif	
-  	char name[20]="_f";
-	Number *obj;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
-	{
-		return false;
-	}
-	obj_value = *obj;
-  	return true;
-}
-
-//get value of gradient of objective function at vector x.
-bool minconTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_grad_f\n");
-	#endif	
-	char name[20]="_gradf";
-  	Number *resg;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg))
-	{
-		return false;
-	}
-	
-	Index i;
-	for(i=0;i<numVars_;i++)
-	{
-		grad_f[i]=resg[i];
-	}
-  	return true;
-}
-
-// return the value of the constraints: g(x)
-bool minconTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_g\n");
-	#endif
- 	// return the value of the constraints: g(x)
-  	if(m==0)
-  	{
-  		g=NULL;
-  	}  		
-	else
-	{
-	  	char name[20]="_addnlc";
-	  	Number *con;
-		if (getFunctionFromScilab(n,name,x, 7, 1,2,&con))
-		{
-			return false;
-		}
-		
-		Index i;
-		for(i=0;i<m;i++)
-		{
-			g[i]=con[i];
-		}
-	}
-
-  	return true;
-}
-
-// return the structure or values of the jacobian
-bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_jac_g\n");
-	#endif	
- 	if (values == NULL) 
- 	{
-    	if(m==0)// return the structure of the jacobian of the constraints
-    	{
-			iRow=NULL; 
-    		jCol=NULL;
-  		}
-		else
-		{
-			unsigned int i,j,idx=0;
-			for(i=0;i<m;i++)
-				for(j=0;j<n;j++)
-				{
-					iRow[idx]=i;
-					jCol[idx]=j;
-					idx++;
-				}
-		}
-	}
-	else 
-	{
-		if(m==0)
-		{
-			values=NULL;
-		}
-		else
-		{
-			double* resj;
-			char name[20]="_gradnlc";
-			if (getFunctionFromScilab(n,name,x, 7, 1,2,&resj))
-			{
-				return false;
-			}
-			int c = 0;
-			for(int i=0;i<m;i++)
-			{
-				for(int j=0;j<n;j++)
-					{
-						values[c] = resj[j*(int)m+i];
-						c++;
-					}
-			}
-		}	
-	}
-  	return true;
-}
-
-/*
- * Return either the sparsity structure of the Hessian of the Lagrangian, 
- * or the values of the Hessian of the Lagrangian  for the given values for
- * x,lambda,obj_factor.
-*/
-
-bool minconTMINLP::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)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_h\n");
-	#endif	
-	double check;
-	if (values==NULL)
-	{
-		Index idx=0;
-		for (Index row = 0; row < numVars_; row++) 
-		{
-			for (Index col = 0; col < numVars_; col++)
-			{
-				iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  	}
-		}
-	}	
-	else 
-	{	char name[20]="_gradhess";
-	  	Number *resCh;
-		if (getHessFromScilab(n,m,name,x, &obj_factor, lambda, 7, 3,2,&resCh))
-		{
-			return false;
-		}
-		Index index=0;
-		for (Index row=0;row < numVars_ ;++row)
-		{
-			for (Index col=0; col < numVars_; ++col)
-			{
-				values[index++]=resCh[numVars_*row+col];
-			}
-		}
-	}
-       	return true;
-}
-
-void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in finalize_solution\n");
-  		sciprint("%d",status);
-	#endif	
-	finalObjVal_ = obj_value;
-	status_ = status;
-	if(status==0 ||status== 3)
-	{
-		finalX_ =  new double[n];
-		for (Index i=0; i<numVars_; i++) 
-		{
-	    		 finalX_[i] = x[i];
-		}
-	}
-}
-
-const double * minconTMINLP::getX()
-{	
-	return finalX_;
-}
-
-double minconTMINLP::getObjVal()
-{	
-	return finalObjVal_;
-}
-
-int minconTMINLP::returnStatus()
-{	
-	return status_;
-}
diff --git a/build/cpp/sci_minconTMINLP.cpp~ b/build/cpp/sci_minconTMINLP.cpp~
deleted file mode 100644
index 9d4ccd3..0000000
--- a/build/cpp/sci_minconTMINLP.cpp~
+++ /dev/null
@@ -1,324 +0,0 @@
-// Copyright (C) 2015 - IIT Bombay - FOSSEE
-//
-// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani
-// Organization: FOSSEE, IIT Bombay
-// Email: toolbox@scilab.in
-// 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
-
-#include "minconTMINLP.hpp"
-#include "sci_iofunc.hpp"
-
-extern "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-#include <string.h>
-#include <assert.h>
-}
-
-using namespace Ipopt;
-using namespace Bonmin;
-
-#define DEBUG 0
-
-minconTMINLP::~minconTMINLP()
-{
-	if(finalX_) delete[] finalX_;
-}
-
-// Set the type of every variable - CONTINUOUS or INTEGER
-bool minconTMINLP::get_variables_types(Index n, VariableType* var_types)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_variables_types\n");
-	#endif
-  n = numVars_;
-  for(int i=0; i < n; i++)
-    var_types[i] = CONTINUOUS;
-  for(int i=0 ; i < intconSize_ ; ++i)
-  	var_types[(int)(intcon_[i]-1)] = INTEGER;
-  return true;
-}
-
-// The linearity of the variables - LINEAR or NON_LINEAR
-bool minconTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
-{  
-	#ifdef DEBUG
-  		sciprint("Code is in get_variables_linearity\n");
-	#endif
-	for(int i=0;i<n;i++)
-	{
-		  var_types[i] = Ipopt::TNLP::NON_LINEAR;
-	}
-	return true; }
-
-// The linearity of the constraints - LINEAR or NON_LINEAR
-bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
-{	
-
-	#ifdef DEBUG
-  		sciprint("Code is in get_constraints_linearity\n");
-	#endif
-	for(int i=0;i<numLC_;i++)
-	{
-		const_types[i] = Ipopt::TNLP::LINEAR;
-	}
-
-	for(int i=numLC_;i<m;i++)
-	{
-		const_types[i] = Ipopt::TNLP::NON_LINEAR;
-	}
-	  return true;}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool minconTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_nlp_info\n");
-	#endif
-	n=numVars_; // Number of variables
-	m=numCons_; // Number of constraints
-	nnz_jac_g = n*m; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*n; // No. of elements in Hessian of the Lagrangian.
-	index_style=TNLP::C_STYLE; // Index style of matrices
-	return true;
-}
-
-//get variable and constraint bound info
-bool minconTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in get_bounds_info\n");
-	#endif
-	unsigned int i;
-	for(i=0;i<n;i++)
-	{
-		x_l[i]=lb_[i];
-		x_u[i]=ub_[i];
-	}
-	for(i=0;i<m;i++)
-	{
-		g_l[i]=conLb_[i];
-        g_u[i]=conUb_[i];
-	}
-	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool minconTMINLP::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)
-{
- 	assert(init_x == true);
-  	assert(init_z == false);
-  	assert(init_lambda == false);
-	if (init_x == true)
-	{ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			{x[var]=x0_[var];}//initialize with 0.
-	}
-	return true;
-}
-
-//get value of objective function at vector x
-bool minconTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
-{	
-	#ifdef DEBUG
-  		sciprint("Code is eval_f\n");
-	#endif	
-  	char name[20]="_f";
-	Number *obj;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
-	{
-		return false;
-	}
-	obj_value = *obj;
-  	return true;
-}
-
-//get value of gradient of objective function at vector x.
-bool minconTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_grad_f\n");
-	#endif	
-	char name[20]="_gradf";
-  	Number *resg;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg))
-	{
-		return false;
-	}
-	
-	Index i;
-	for(i=0;i<numVars_;i++)
-	{
-		grad_f[i]=resg[i];
-	}
-  	return true;
-}
-
-// return the value of the constraints: g(x)
-bool minconTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_g\n");
-	#endif
- 	// return the value of the constraints: g(x)
-  	if(m==0)
-  	{
-  		g=NULL;
-  	}  		
-	else
-	{
-	  	char name[20]="_addnlc";
-	  	Number *con;
-		if (getFunctionFromScilab(n,name,x, 7, 1,2,&con))
-		{
-			return false;
-		}
-		
-		Index i;
-		for(i=0;i<m;i++)
-		{
-			g[i]=con[i];
-		}
-	}
-
-  	return true;
-}
-
-// return the structure or values of the jacobian
-bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_jac_g\n");
-	#endif	
- 	if (values == NULL) 
- 	{
-    	if(m==0)// return the structure of the jacobian of the constraints
-    	{
-			iRow=NULL; 
-    		jCol=NULL;
-  		}
-		else
-		{
-			unsigned int i,j,idx=0;
-			for(i=0;i<m;i++)
-				for(j=0;j<n;j++)
-				{
-					iRow[idx]=i;
-					jCol[idx]=j;
-					idx++;
-				}
-		}
-	}
-	else 
-	{
-		if(m==0)
-		{
-			values=NULL;
-		}
-		else
-		{
-			double* resj;
-			char name[20]="_gradnlc";
-			if (getFunctionFromScilab(n,name,x, 7, 1,2,&resj))
-			{
-				return false;
-			}
-			int c = 0;
-			for(int i=0;i<m;i++)
-			{
-				for(int j=0;j<n;j++)
-					{
-						values[c] = resj[j*(int)m+i];
-						c++;
-					}
-			}
-		}	
-	}
-  	return true;
-}
-
-/*
- * Return either the sparsity structure of the Hessian of the Lagrangian, 
- * or the values of the Hessian of the Lagrangian  for the given values for
- * x,lambda,obj_factor.
-*/
-
-bool minconTMINLP::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)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in eval_h\n");
-	#endif	
-	double check;
-	if (values==NULL)
-	{
-		Index idx=0;
-		for (Index row = 0; row < numVars_; row++) 
-		{
-			for (Index col = 0; col < numVars_; col++)
-			{
-				iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  	}
-		}
-	}	
-	else 
-	{	char name[20]="_gradhess";
-	  	Number *resCh;
-		if (getHessFromScilab(n,m,name,x, &obj_factor, lambda, 7, 3,2,&resCh))
-		{
-			return false;
-		}
-		Index index=0;
-		for (Index row=0;row < numVars_ ;++row)
-		{
-			for (Index col=0; col < numVars_; ++col)
-			{
-				values[index++]=resCh[numVars_*row+col];
-			}
-		}
-	}
-       	return true;
-}
-
-void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value)
-{
-	#ifdef DEBUG
-  		sciprint("Code is in finalize_solution\n");
-  		sciprint("%d",status);
-	#endif	
-	finalObjVal_ = obj_value;
-	status_ = status;
-	if(status==0 ||status== 3)
-	{
-		finalX_ =  new double[n];
-		for (Index i=0; i<numVars_; i++) 
-		{
-	    		 finalX_[i] = x[i];
-		}
-	}
-}
-
-const double * minconTMINLP::getX()
-{	
-	return finalX_;
-}
-
-double minconTMINLP::getObjVal()
-{	
-	return finalObjVal_;
-}
-
-int minconTMINLP::returnStatus()
-{	
-	return status_;
-}
diff --git a/build/cpp/sci_minuncTMINLP.cpp b/build/cpp/sci_minuncTMINLP.cpp
deleted file mode 100644
index 696c5ef..0000000
--- a/build/cpp/sci_minuncTMINLP.cpp
+++ /dev/null
@@ -1,237 +0,0 @@
-// Copyright (C) 2015 - IIT Bombay - FOSSEE
-//
-// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani
-// Organization: FOSSEE, IIT Bombay
-// Email: toolbox@scilab.in
-// 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
-
-#include "minuncTMINLP.hpp"
-#include "sci_iofunc.hpp"
-
-extern "C"
-{
-#include "call_scilab.h"
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-#include <string.h>
-#include <assert.h>
-}
-
-using namespace std;
-using namespace Ipopt;
-using namespace Bonmin;
-
-minuncTMINLP::~minuncTMINLP()
-{
-	if(finalX_) delete[] finalX_;
-}
-
-// Set the type of every variable - CONTINUOUS or INTEGER
-bool minuncTMINLP::get_variables_types(Index n, VariableType* var_types)
-{
-  n = numVars_;
-  for(int i=0; i < n; i++)
-    var_types[i] = CONTINUOUS;
-  for(int i=0 ; i < intconSize_ ; ++i)
-  	var_types[(int)(intcon_[i]-1)] = INTEGER;
-  return true;
-}
-
-// The linearity of the variables - LINEAR or NON_LINEAR
-bool minuncTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
-{
-	/*
-	n = numVars_;
-	for(int i = 0; i < n; i++)
-		var_types[i] = Ipopt::TNLP::LINEAR;
-  */
-  return true;
-}
-
-// The linearity of the constraints - LINEAR or NON_LINEAR
-bool minuncTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
-{
- /* m = numConstr_;
-  for(int i = 0; i < m; i++)
-    const_types[i] = Ipopt::TNLP::LINEAR;
-  */
-  return true;
-}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool minuncTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
-{
-	n=numVars_; // Number of variables
-	m=0; // Number of constraints
-	nnz_jac_g = 0; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian.
-	index_style=TNLP::C_STYLE; // Index style of matrices
-	return true;
-}
-
-//get variable and constraint bound info
-bool minuncTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
-{
-	unsigned int i;
-	for(i=0;i<n;i++)
-	{
-		x_l[i]=-1.0e19;
-		x_u[i]=1.0e19;
-	}
-
-        g_l=NULL;
-        g_u=NULL;
-	return true;
-}
-
-// return the value of the constraints: g(x)
-bool minuncTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
-{
-  	// return the value of the constraints: g(x)
-  	g=NULL;
-  	return true;
-}
-
-// return the structure or values of the jacobian
-bool minuncTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values)
-{
- 	if (values == NULL) 
- 	{
-    		// return the structure of the jacobian of the constraints
-    		iRow=NULL; 
-    		jCol=NULL;
-  	}
-  	else 
-	{
-		values=NULL;
-  	}
-
-  	return true;
-}
-
-//get value of objective function at vector x
-bool minuncTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
-{
-  	char name[20]="_f";
-	Number *obj;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
-	{
-		return false;
-	}
-	obj_value = *obj;
-  	return true;
-}
-
-//get value of gradient of objective function at vector x.
-bool minuncTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
-{
-	char name[20]="_gradf";
-  	Number *resg;
-	if (getFunctionFromScilab(n,name,x, 7, 1, 2, &resg))
-	{
-		return false;
-	}
-	
-	Index i;
-	for(i=0;i<numVars_;i++)
-	{
-		grad_f[i]=resg[i];
-	}
-	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool minuncTMINLP::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)
-{
- 	assert(init_x == true);
-  	assert(init_z == false);
-  	assert(init_lambda == false);
-	if (init_x == true)
-	{ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			x[var]=varGuess_[var];//initialize with 0 or we can change.
-	}
-
-	return true;
-}
-
-/*
- * Return either the sparsity structure of the Hessian of the Lagrangian, 
- * or the values of the Hessian of the Lagrangian  for the given values for
- * x,lambda,obj_factor.
-*/
-
-bool minuncTMINLP::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)
-{
-	double check;
-	if (values==NULL)
-	{
-		Index idx=0;
-		for (Index row = 0; row < numVars_; row++) 
-		{
-			for (Index col = 0; col <= row; col++)
-			{
-				iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  	}
-		}
-	}
-
-	else 
-	{			
-		char name[20]="_gradhess";
-	  	Number *resh;
-		if (getFunctionFromScilab(n,name,x, 7, 1,2,&resh))
-		{
-			return false;
-		}
-		Index index=0;
-		for (Index row=0;row < numVars_ ;++row)
-		{
-			for (Index col=0; col <= row; ++col)
-			{
-				values[index++]=obj_factor*(resh[numVars_*row+col]);
-			}
-		}
-       	return true;
-    }
-}
-
-
-void minuncTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value)
-{
-	finalObjVal_ = obj_value;
-	status_ = status;
-	if(status==0 ||status== 3)
-	{
-		finalX_ = new double[n];
-		for (Index i=0; i<numVars_; i++) 
-		{
-	    		 finalX_[i] = x[i];
-		}
-	}
-	
-}
-
-const double * minuncTMINLP::getX()
-{	
-	return finalX_;
-}
-
-double minuncTMINLP::getObjVal()
-{	
-	return finalObjVal_;
-}
-
-int minuncTMINLP::returnStatus()
-{	
-	return status_;
-}