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Diffstat (limited to 'sci_gateway/cpp/minuncNLP.hpp')
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diff --git a/sci_gateway/cpp/minuncNLP.hpp b/sci_gateway/cpp/minuncNLP.hpp new file mode 100644 index 0000000..70910e5 --- /dev/null +++ b/sci_gateway/cpp/minuncNLP.hpp @@ -0,0 +1,113 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: R.Vidyadhar & Vignesh Kannan +// Organization: FOSSEE, IIT Bombay +// Email: rvidhyadar@gmail.com & vignesh2496@gmail.com +// 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 + + +#ifndef __minuncNLP_HPP__ +#define __minuncNLP_HPP__ +#include "IpTNLP.hpp" + +using namespace Ipopt; + +class minuncNLP : public TNLP +{ + private: + + Index numVars_; //Number of input variables + + Index numConstr_; //Number of constraints + + Number flag1_; //Used for Gradient On/OFF + + Number flag2_; //Used for Hessian ON/OFF + + const Number *varGuess_= NULL; //varGuess_ is a pointer to a matrix of size of 1*numVars_ with initial guess 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 *finalGradient_=NULL; //finalGradient_ is a pointer to a matrix of size of numVars_*numVars_ with final value of gradient for the primal variables. + + Number *finalHessian_=NULL; //finalHessian_ is a pointer to a matrix of size of 1*numVar_ with final value of hessian for the primal variables. + + Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. + + int iter_; //Number of iteration. + + int status_; //Solver return status + + + minuncNLP(const minuncNLP&); + minuncNLP& operator=(const minuncNLP&); + + public: + + /** user defined constructor */ + minuncNLP(Index nV, Index nC,Number *x0,Number f1, Number f2):numVars_(nV),numConstr_(nC),varGuess_(x0),flag1_(f1),flag2_(f2),finalX_(0),finalGradient_(0),finalHessian_(0),finalObjVal_(1e20){ } + + /** default destructor */ + virtual ~minuncNLP(); + + /** 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, 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, const Number* z_L, const Number* z_U,Index m, const Number* g, const Number* lambda,Number obj_value,const IpoptData* ip_data,IpoptCalculatedQuantities* ip_cq); + + 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 |