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diff --git a/build/cpp/sci_minconTMINLP.cpp~ b/build/cpp/sci_minconTMINLP.cpp~
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-// 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_;
-}
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