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Diffstat (limited to 'newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp')
| -rw-r--r-- | newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp | 236 |
1 files changed, 0 insertions, 236 deletions
diff --git a/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp deleted file mode 100644 index a3212aa..0000000 --- a/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp +++ /dev/null @@ -1,236 +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 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_; -} |