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Diffstat (limited to 'newstructure/thirdparty/linux/include/coin/BonTNLP2FPNLP.hpp')
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diff --git a/newstructure/thirdparty/linux/include/coin/BonTNLP2FPNLP.hpp b/newstructure/thirdparty/linux/include/coin/BonTNLP2FPNLP.hpp deleted file mode 100644 index 82137c9..0000000 --- a/newstructure/thirdparty/linux/include/coin/BonTNLP2FPNLP.hpp +++ /dev/null @@ -1,264 +0,0 @@ -// 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_*/ |