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// 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_;
}
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