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+// Copyright (C) 2004, International Business Machines and others.
+// All Rights Reserved.
+// This code is published under the Eclipse 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
+{
+  /** This is an adapter class to convert an NLP to a Feasibility Pump NLP
+   *  by changing the objective function to the (2-norm) distance to a point.
+   * The extra function is set_dist_to_point_obj(size_t n, const double *, const int *)
+   */
+  class TNLP2FPNLP : public Ipopt::TNLP
+  {
+  public:
+    /**@name Constructors/Destructors */
+    //@{
+    /** Build using tnlp as source problem.*/
+    TNLP2FPNLP(const Ipopt::SmartPtr<Ipopt::TNLP> tnlp, double objectiveScalingFactor = 100);
+
+    /** Build using tnlp as source problem and using other for all other parameters..*/
+    TNLP2FPNLP(const Ipopt::SmartPtr<TNLP> tnlp, const Ipopt::SmartPtr<TNLP2FPNLP> other);
+
+    /** Default destructor */
+    virtual ~TNLP2FPNLP();
+    //@}
+    void use(Ipopt::SmartPtr<TNLP> tnlp){
+      tnlp_ = GetRawPtr(tnlp);}
+    /**@name Methods to select the objective function and extra constraints*/
+    //@{
+    /// Flag to indicate that we want to use the feasibility pump objective
+    void set_use_feasibility_pump_objective(bool use_feasibility_pump_objective) 
+    { use_feasibility_pump_objective_ = use_feasibility_pump_objective; }
+
+    /** Flag to indicate that we want to use a cutoff constraint
+     *	This constraint has the form f(x) <= (1-epsilon) f(x') */ 
+    void set_use_cutoff_constraint(bool use_cutoff_constraint)
+    { use_cutoff_constraint_ = use_cutoff_constraint; }
+
+    /// Flag to indicate that we want to use a local branching constraint
+    void set_use_local_branching_constraint(bool use_local_branching_constraint)
+    { use_local_branching_constraint_ = use_local_branching_constraint; }
+    //@}
+
+    /**@name Methods to provide the rhs of the extra constraints*/
+    //@{
+    /// Set the cutoff value to use in the cutoff constraint
+    void set_cutoff(Ipopt::Number cutoff);
+
+    /// Set the rhs of the local branching constraint
+    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; }
+    //@}
+
+    /**@name Methods to change the objective function*/
+    //@{
+    /** \brief Set the point to which distance is minimized.
+    * The distance is minimize in a subspace define by a subset of coordinates
+     * \param n number of coordinates on which distance is minimized
+     * \param inds indices of the coordinates on which distance is minimized
+     * \param vals values of the point for coordinates in ind
+     */
+    void set_dist_to_point_obj(size_t n, const Ipopt::Number * vals, const Ipopt::Index * inds);
+
+     /** Set the value for sigma */
+     void setSigma(double sigma){
+       assert(sigma >= 0.);
+       sigma_ = sigma;}
+     /** Set the value for lambda*/
+     void setLambda(double lambda){
+       assert(lambda >= 0. && lambda <= 1.);
+       lambda_ = lambda;}
+     /** Set the value for simgma */
+     void setNorm(int norm){
+       assert(norm >0 && norm < 3);
+       norm_ = norm;}
+    //@}
+
+    /**@name methods to gather information about the NLP */
+    //@{
+    /** get info from nlp_ and add hessian information */
+    virtual bool get_nlp_info(Ipopt::Index& n, Ipopt::Index& m, Ipopt::Index& nnz_jac_g,
+        Ipopt::Index& nnz_h_lag, Ipopt::TNLP::IndexStyleEnum& index_style);
+
+    /** This call is just passed onto tnlp_
+     */
+    virtual bool get_bounds_info(Ipopt::Index n, Ipopt::Number* x_l, Ipopt::Number* x_u,
+				 Ipopt::Index m, Ipopt::Number* g_l, Ipopt::Number* g_u);
+
+    /** Passed onto tnlp_
+     */
+    virtual bool get_starting_point(Ipopt::Index n, bool init_x, Ipopt::Number* x,
+        bool init_z, Ipopt::Number* z_L, Ipopt::Number* z_U,
+        Ipopt::Index m, bool init_lambda,
+        Ipopt::Number* lambda)
+    {
+      int m2 = m;
+      if(use_cutoff_constraint_) {
+        m2--;
+        if(lambda!=NULL)lambda[m2] = 0;
+      }
+      if(use_local_branching_constraint_) {
+        m2--;
+        if(lambda!= NULL)lambda[m2] = 0;
+      }
+      int ret_code = tnlp_->get_starting_point(n, init_x, x,
+          init_z, z_L, z_U, m2, init_lambda, lambda);
+      return ret_code;
+    }
+
+    /** overloaded to return the value of the objective function */
+    virtual bool eval_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::Number& obj_value);
+
+    /** overload this method to return the vector of the gradient of
+     *  the objective w.r.t. x */
+    virtual bool eval_grad_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::Number* grad_f);
+
+    /** overload to return the values of the left-hand side of the
+        constraints */
+    virtual bool eval_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+			Ipopt::Index m, Ipopt::Number* g);
+
+    /** overload to return the jacobian of g */
+    virtual bool eval_jac_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+			    Ipopt::Index m, Ipopt::Index nele_jac, Ipopt::Index* iRow,
+			    Ipopt::Index *jCol, Ipopt::Number* values);
+
+    /** Evaluate the modified Hessian of the Lagrangian*/
+    virtual bool eval_h(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::Number obj_factor, Ipopt::Index m, const Ipopt::Number* lambda,
+        bool new_lambda, Ipopt::Index nele_hess,
+        Ipopt::Index* iRow, Ipopt::Index* jCol, Ipopt::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(Ipopt::SolverReturn status,
+        Ipopt::Index n, const Ipopt::Number* x, const Ipopt::Number* z_L, const Ipopt::Number* z_U,
+        Ipopt::Index m, const Ipopt::Number* g, const Ipopt::Number* lambda,
+        Ipopt::Number obj_value,
+        const Ipopt::IpoptData* ip_data,
+        Ipopt::IpoptCalculatedQuantities* ip_cq);
+    //@}
+
+    virtual bool get_variables_linearity(Ipopt::Index n, LinearityType* var_types)
+    {
+      return tnlp_->get_variables_linearity(n, var_types);;
+    }
+
+    /** overload this method to return the constraint linearity.
+     * array should be alocated with length at least n. (default implementation
+     *  just return false and does not fill the array).*/
+    virtual bool get_constraints_linearity(Ipopt::Index m, LinearityType* const_types)
+    {
+      int m2 = m;
+      if(use_cutoff_constraint_) {
+        m2--;
+        const_types[m2] = Ipopt::TNLP::NON_LINEAR;
+      } 
+      if(use_local_branching_constraint_) {
+        m2--;
+        const_types[m2] = Ipopt::TNLP::LINEAR;
+      }
+      return tnlp_->get_constraints_linearity(m2, const_types);
+    }
+    /** @name Scaling of the objective function */
+    //@{
+    void setObjectiveScaling(double value)
+    {
+      objectiveScalingFactor_ = value;
+    }
+    double getObjectiveScaling() const
+    {
+      return objectiveScalingFactor_;
+    }
+
+  private:
+    /** @name Internal methods to help compute the distance, its gradient and hessian */
+    //@{
+    /** Compute the norm-2 distance to the current point to which distance is minimized. */
+    double dist_to_point(const Ipopt::Number *x);
+    //@}
+    /**@name Default Compiler Generated Methods
+     * (Hidden to avoid implicit creation/calling).
+     * These methods are not implemented and
+     * we do not want the compiler to implement
+     * them for us, so we declare them private
+     * and do not define them. This ensures that
+     * they will not be implicitly created/called. */
+    //@{
+    /** Default Constructor */
+    TNLP2FPNLP();
+
+    /** Copy Constructor */
+    TNLP2FPNLP(const TNLP2FPNLP&);
+
+    /** Overloaded Equals Operator */
+    void operator=(const TNLP2FPNLP&);
+    //@}
+
+    /** pointer to the tminlp that is being adapted */
+    Ipopt::SmartPtr<TNLP> tnlp_;
+
+    /** @name Data for storing the point the distance to which is minimized */
+    //@{
+    /// Indices of the variables for which distance is minimized (i.e. indices of integer variables in a feasibility pump setting)
+    vector<Ipopt::Index> inds_;
+    /// Values of the point to which we separate (if x is the point vals_[i] should be x[inds_[i]] )
+    vector<Ipopt::Number> vals_;
+    /** value for the convex combination to take between original objective and distance function.
+      * ( take lambda_ * distance + (1-lambda) sigma f(x).*/
+    double lambda_;
+    /** Scaling for the original objective.*/
+    double sigma_;
+   /** Norm to use (L_1 or L_2).*/
+   int norm_;
+    //@}
+
+    /// Scaling factor for the objective
+    double objectiveScalingFactor_;
+
+    /**@name Flags to  select the objective function and extra constraints*/
+    //@{
+    /// Flag to indicate that we want to use the feasibility pump objective
+    bool use_feasibility_pump_objective_;
+
+    /** Flag to indicate that we want to use a cutoff constraint
+     *	This constraint has the form f(x) <= (1-epsilon) f(x') */ 
+    bool use_cutoff_constraint_;    
+
+    /// Flag to indicate that we want to use a local branching constraint
+    bool use_local_branching_constraint_;
+    //@}
+
+    /**@name Data for storing the rhs of the extra constraints*/
+    //@{
+    /// Value of best solution known
+    double cutoff_;
+
+    /// RHS of local branching constraint
+    double rhs_local_branching_constraint_;
+    //@}
+
+    /// Ipopt::Index style (C++ or Fortran)
+    Ipopt::TNLP::IndexStyleEnum index_style_;
+
+  };
+
+} // namespace Ipopt
+
+#endif /*_TNLP2FPNLP_HPP_*/