diff options
Diffstat (limited to 'build/cpp/sci_minconTMINLP.cpp')
| -rw-r--r-- | build/cpp/sci_minconTMINLP.cpp | 324 |
1 files changed, 324 insertions, 0 deletions
diff --git a/build/cpp/sci_minconTMINLP.cpp b/build/cpp/sci_minconTMINLP.cpp new file mode 100644 index 0000000..7885083 --- /dev/null +++ b/build/cpp/sci_minconTMINLP.cpp @@ -0,0 +1,324 @@ +// 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() +{ + free(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_ = (double*)malloc(sizeof(double) * numVars_*1); + 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_; +} |