1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
|
// 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_; //varGuess_ is a pointer to a matrix of size of 1*numVars_ with initial guess of all variables.
Number *finalX_; //finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables.
Number *finalGradient_; //finalGradient_ is a pointer to a matrix of size of numVars_*numVars_ with final value of gradient for the primal variables.
Number *finalHessian_; //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 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.
int returnStatus(); //Returns the status count
};
#endif
|
