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Diffstat (limited to 'sci_gateway/cpp/minbndTMINLP.hpp')
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diff --git a/sci_gateway/cpp/minbndTMINLP.hpp b/sci_gateway/cpp/minbndTMINLP.hpp new file mode 100644 index 0000000..581d5ce --- /dev/null +++ b/sci_gateway/cpp/minbndTMINLP.hpp @@ -0,0 +1,114 @@ +// Copyright (C) 2016 - IIT Bombay - FOSSEE +// +// 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 +// Author: Harpreet Singh +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +#ifndef minbndTMINLP_HPP +#define minbndTMINLP_HPP + +#include "BonTMINLP.hpp" +#include "IpTNLP.hpp" +#include "call_scilab.h" + +using namespace Ipopt; +using namespace Bonmin; + +class minbndTMINLP : public TMINLP +{ + private: + + Index numVars_; //Number of input variables + + Index intconSize_; + + Number *lb_= NULL; //lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables. + + Number *ub_= NULL; //ub_ is a pointer to a matrix of size of 1*numVars_ with upper bound of all variables. + + Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables. + + Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. + + Number *intcon_ = NULL; + + int status_; //Solver return status + minbndTMINLP(const minbndTMINLP&); + minbndTMINLP& operator=(const minbndTMINLP&); + +public: + // Constructor + minbndTMINLP(Index nV, Number *lb, Number *ub, Index intconSize, Number *intcon):numVars_(nV),lb_(lb),ub_(ub),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){ } + + /** default destructor */ + virtual ~minbndTMINLP(); + + virtual bool get_variables_types(Index n, VariableType* var_types); + + virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types); + + virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types); + + /** Method to return some info about the nlp */ + virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, + Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style); + + /** Method to return the bounds for my problem */ + virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, + Index m, Number* g_l, Number* g_u); + + /** Method to return the starting point for the algorithm */ + virtual bool 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); + + /** Method to return the objective value */ + virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); + + /** Method to return the gradient of the objective */ + virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); + + /** Method to return the constraint residuals */ + virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); + + /** Method to return: + * 1) The structure of the jacobian (if "values" is NULL) + * 2) The values of the jacobian (if "values" is not NULL) + */ + virtual bool eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values); + + /** Method to return: + * 1) The structure of the hessian of the lagrangian (if "values" is NULL) + * 2) The values of the hessian of the lagrangian (if "values" is not NULL) + */ + virtual bool 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); + + /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ + virtual void finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value); + + virtual const SosInfo * sosConstraints() const{return NULL;} + virtual const BranchingInfo* branchingInfo() const{return NULL;} + + const double * getX(); //Returns a pointer to a matrix of size of 1*numVars_ + //with final value for the primal variables. + + const double * getGrad(); //Returns a pointer to a matrix of size of 1*numVars_ + //with final value of gradient for the primal variables. + + const double * getHess(); //Returns a pointer to a matrix of size of numVars_*numVars_ + //with final value of hessian for the primal variables. + + double getObjVal(); //Returns the output of the final value of the objective. + + double iterCount(); //Returns the iteration count + + int returnStatus(); //Returns the status count +}; + +#endif |