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diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp~ b/sci_gateway/cpp/sci_QuadNLP.cpp~
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--- a/sci_gateway/cpp/sci_QuadNLP.cpp~
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@@ -1,255 +0,0 @@
-/*
- * Quadratic Programming Toolbox for Scilab using IPOPT library
- * Authors :
-	Sai Kiran
-	Keyur Joshi
-	Iswarya
- */
-
-#include "QuadNLP.hpp"
-#include "IpIpoptData.hpp"
-
-extern "C"{
-#include <api_scilab.h>
-#include <Scierror.h>
-#include <BOOL.h>
-#include <localization.h>
-#include <sciprint.h>
-
-
-double x_static,i, *op_obj_x = NULL,*op_obj_value = NULL;
-
-using namespace Ipopt;
-
-QuadNLP::~QuadNLP()
-		 {
-			free(finalX_);
-			free(finalZl_);
-			free(finalZu_);}
-
-//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
-bool QuadNLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style){
-	n=numVars_; // Number of variables
-	m=numConstr_; // Number of constraints
-	nnz_jac_g = n*m; // 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=C_STYLE; // Index style of matrices
-	return true;
-	}
-
-//get variable and constraint bound info
-bool QuadNLP::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]=varLB_[i];
-		x_u[i]=varUB_[i];
-		sciprint("VarLU %lf %lf \n",x_l[i],x_u[i]);
-		}
-
-	for(i=0;i<m;i++){
-		g_l[i]=conLB_[i];
-		g_u[i]=conUB_[i];
-		sciprint("conLU %lf %lf \n",g_l[i],g_u[i]);
-		}
-	return true;
-	}
-
-//get value of objective function at vector x
-bool QuadNLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value){
-	unsigned int i,j;
-	obj_value=0;
-
-	for (i=0;i<=n;i++){
-		for (j=0;j<=n;j++){
-			obj_value+=0.5*x[i]*x[j]*qMatrix_[n*i+j];		
-			}
-		obj_value+=x[i]*lMatrix_[i];
-		}
-	return true;
-	}
-
-//get value of gradient of objective function at vector x.
-bool QuadNLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f){
-	unsigned int i,j;
-	for(i=0;i<n;i++)
-	{
-		grad_f[i]=lMatrix_[i];
-		for(j=0;j<n;j++)
-			{
-				grad_f[i]+=(qMatrix_[n*i+j])*x[j];
-			}
-	}
-	return true;
-}
-
-//Get the values of constraints at vector x.
-bool QuadNLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g){
-	unsigned int i,j;
-	for(i=0;i<m;i++)
-	{
-		g[i]=0;
-		for(j=0;j<n;j++)
-		{
-			g[i]+=x[j]*conMatrix_[i+j*m];	
-		}
-	}
-	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool QuadNLP::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){
-	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 or we can change.
-		}
-
-	if (init_z == true){ //we need to provide initial values for vector bound multipliers
-		for (Index var=0;var<n;++var){
-			z_L[var]=0.0; //initialize with 0 or we can change.
-			z_U[var]=0.0;//initialize with 0 or we can change.
-			}
-		}
-	
-	if (init_lambda == true){ //we need to provide initial values for lambda values.
-		for (Index var=0;var<m;++var){
-			lambda[var]=0.0; //initialize with 0 or we can change.
-			}
-		}
-
-	return true;
-	}
-/* Return either the sparsity structure of the Jacobian of the constraints, or the values for the Jacobian of the constraints at the point x.
-
-*/ 
-bool QuadNLP::eval_jac_g(Index n, const Number* x, bool new_x,
-			 Index m, Index nele_jac, Index* iRow, Index *jCol,
-			 Number* values){
-	
-	//It asked for structure of jacobian.
-	if (values==NULL){ //Structure of jacobian (full structure)
-		int index=0;
-		for (int var=0;var<m;++var)//no. of constraints
-			for (int flag=0;flag<n;++flag){//no. of variables
-				iRow[index]=var;
-				jCol[index]=flag;
-				index++;
-				}
-		}
-	//It asked for values
-	else { 
-		int index=0;
-		for (int var=0;var<m;++var)
-			for (int flag=0;flag<n;++flag)
-				values[index++]=conMatrix_[var+flag*m];
-		}
-	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 QuadNLP::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){
-
-	if (values==NULL){
-		Index idx=0;
-		for (Index row = 0; row < n; row++) {
-			for (Index col = 0; col <= row; col++) {
-				iRow[idx] = row;
-				jCol[idx] = col;
-				idx++;
-		  		}
-			}
-		}
-	else {
-		Index index=0;
-		for (Index row=0;row < n;++row){
-			for (Index col=0; col <= row; ++col){
-				values[index++]=obj_factor*(qMatrix_[n*row+col]);
-				}
-			}
-		}
-	return true;
-	}
-
-
-void QuadNLP::finalize_solution(SolverReturn status,
-				Index n, const Number* x, const Number* z_L, const Number* z_U,
-				Index m, const Number* g, const Number* lambda, Number obj_value,
-				const IpoptData* ip_data,
-				IpoptCalculatedQuantities* ip_cq){
-	
-	finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalX_[i] = x[i];
-	}
-	
-	finalZl_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalZl_[i] = z_L[i];
-	}
-
-	finalZu_ = (double*)malloc(sizeof(double) * numVars_ * 1);
-	for (Index i=0; i<n; i++) 
-	{
-    		 finalZu_[i] = z_U[i];
-	}
-
-	finalLambda_ = (double*)malloc(sizeof(double) * numConstr_ * 1);
-	for (Index i=0; i<m; i++) 
-	{
-    		 finalLambda_[i] = lambda[i];
-	}
-
-	iter_ = ip_data->iter_count();
-	finalObjVal_ = obj_value;
-	status_ = status;
-	
-   }
-
-	const double * QuadNLP::getX()
-	{	
-		return finalX_;
-	}
-
-	const double * QuadNLP::getZl()
-	{	
-		return finalZl_;
-	}
-
-	const double * QuadNLP::getZu()
-	{	
-		return finalZu_;
-	}
-
-	const double * QuadNLP::getLambda()
-	{	
-		return finalLambda_;
-	}
-
-	double QuadNLP::getObjVal()
-	{	
-		return finalObjVal_;
-	}
-
-	double QuadNLP::iterCount()
-	{	
-		return (double)iter_;
-	}
-
-	int QuadNLP::returnStatus()
-	{	
-		return status_;
-	}
-
-}