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// Copyright (C) 2004, International Business Machines and others.
// All Rights Reserved.
// This code is published under the Common Public License.
//
//
// Authors:  Pierre Bonami 06/10/2005

#ifndef _TNLP2FPNLP_HPP_
#define _TNLP2FPNLP_HPP_

#include "IpTNLP.hpp"
#include "BonTMINLP.hpp"
#include "IpSmartPtr.hpp"
#include "BonTypes.hpp"

namespace Bonmin
{
  class TNLP2FPNLP : public Ipopt::TNLP
  {
  public:
    TNLP2FPNLP(const SmartPtr<TNLP> tnlp, double objectiveScalingFactor = 100);

    TNLP2FPNLP(const SmartPtr<TNLP> tnlp, const SmartPtr<TNLP2FPNLP> other);

    virtual ~TNLP2FPNLP();


    void set_use_feasibility_pump_objective(bool use_feasibility_pump_objective) 
    { use_feasibility_pump_objective_ = use_feasibility_pump_objective; }

    void set_use_cutoff_constraint(bool use_cutoff_constraint)
    { use_cutoff_constraint_ = use_cutoff_constraint; }

    void set_use_local_branching_constraint(bool use_local_branching_constraint)
    { use_local_branching_constraint_ = use_local_branching_constraint; }


    void set_cutoff(Number cutoff);

    void set_rhs_local_branching_constraint(double rhs_local_branching_constraint)
    { assert(rhs_local_branching_constraint >= 0);
      rhs_local_branching_constraint_ = rhs_local_branching_constraint; }

    void set_dist2point_obj(int n, const Number * vals, const Index * inds);

     void setSigma(double sigma){
       assert(sigma >= 0.);
       sigma_ = sigma;}
     void setLambda(double lambda){
       assert(lambda >= 0. && lambda <= 1.);
       lambda_ = lambda;}
     void setNorm(int norm){
       assert(norm >0 && norm < 3);
       norm_ = norm;}

    virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
        Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style);

    virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u,
                                 Index m, Number* g_l, Number* g_u);

    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)
    {
      return tnlp_->get_starting_point(n, init_x, x,
          init_z, z_L, z_U, m, init_lambda, lambda);
    }

    virtual bool eval_f(Index n, const Number* x, bool new_x,
        Number& obj_value);

    virtual bool eval_grad_f(Index n, const Number* x, bool new_x,
        Number* grad_f);

    virtual bool eval_g(Index n, const Number* x, bool new_x,
                        Index m, Number* g);

    virtual bool eval_jac_g(Index n, const Number* x, bool new_x,
                            Index m, Index nele_jac, Index* iRow,
                            Index *jCol, Number* values);

    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);

    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);

    void setObjectiveScaling(double value)
    {
      objectiveScalingFactor_ = value;
    }
    double getObjectiveScaling() const
    {
      return objectiveScalingFactor_;
    }

  private:
    double dist2point(const Number *x);

    TNLP2FPNLP();

    TNLP2FPNLP(const TNLP2FPNLP&);

    void operator=(const TNLP2FPNLP&);

    SmartPtr<TNLP> tnlp_;


    vector<Index> inds_;
    vector<Number> vals_;
    double lambda_;
    double sigma_;
   int norm_;

    double objectiveScalingFactor_;


    bool use_feasibility_pump_objective_;

    bool use_cutoff_constraint_;    

    bool use_local_branching_constraint_;


    double cutoff_;

    double rhs_local_branching_constraint_;

    TNLP::IndexStyleEnum index_style_;

  };

} // namespace Ipopt

#endif /*_TNLP2FPNLP_HPP_*/

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