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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 new file mode 100644 index 0000000..82137c9 --- /dev/null +++ b/newstructure/thirdparty/linux/include/coin/BonTNLP2FPNLP.hpp @@ -0,0 +1,264 @@ +// 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_*/ |