Structure updated and intqpipopt files added

1 files changed, 0 insertions, 237 deletions

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_;
-}