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authorHarpreet2016-09-03 00:36:51 +0530
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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_*/