// Copyright (C) 2004, 2006 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Common Public License.
//
// $Id: MyNLP.hpp 949 2007-03-27 00:41:26Z andreasw $
//
// Authors:  Carl Laird, Andreas Waechter     IBM    2004-11-05

#ifndef __MYNLP_HPP__
#define __MYNLP_HPP__

#include "IpTNLP.hpp"

using namespace Ipopt;

/** C++ Example NLP for interfacing a problem with IPOPT.
 *  MyNLP implements a C++ example showing how to interface with IPOPT
 *  through the TNLP interface. This example is designed to go along with
 *  the tutorial document (see Examples/CppTutorial/).
 *  This class implements the following NLP.
 *
 * min_x f(x) = -(x2-2)^2
 *  s.t.
 *       0 = x1^2 + x2 - 1
 *       -1 <= x1 <= 1
 *
 */
class MyNLP : public TNLP
{
public:
  /** default constructor */
  MyNLP();

  /** default destructor */
  virtual ~MyNLP();

  /**@name Overloaded from TNLP */
  //@{
  /** 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);

  //@}

  /** @name Solution Methods */
  //@{
  /** 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);
  //@}

private:
  /**@name Methods to block default compiler methods.
   * The compiler automatically generates the following three methods.
   *  Since the default compiler implementation is generally not what
   *  you want (for all but the most simple classes), we usually 
   *  put the declarations of these methods in the private section
   *  and never implement them. This prevents the compiler from
   *  implementing an incorrect "default" behavior without us
   *  knowing. (See Scott Meyers book, "Effective C++")
   *  
   */
  //@{
  //  MyNLP();
  MyNLP(const MyNLP&);
  MyNLP& operator=(const MyNLP&);
  //@}
};


#endif
