Structure updated and intqpipopt files added

1 files changed, 0 insertions, 324 deletions

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_;
-}