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Diffstat (limited to 'build/Bonmin/include/coin/IpNLP.hpp')
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diff --git a/build/Bonmin/include/coin/IpNLP.hpp b/build/Bonmin/include/coin/IpNLP.hpp new file mode 100644 index 0000000..1063c01 --- /dev/null +++ b/build/Bonmin/include/coin/IpNLP.hpp @@ -0,0 +1,243 @@ +// Copyright (C) 2004, 2006 International Business Machines and others. +// All Rights Reserved. +// This code is published under the Eclipse Public License. +// +// $Id: IpNLP.hpp 2269 2013-05-05 11:32:40Z stefan $ +// +// Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 + +#ifndef __IPNLP_HPP__ +#define __IPNLP_HPP__ + +#include "IpUtils.hpp" +#include "IpVector.hpp" +#include "IpSmartPtr.hpp" +#include "IpMatrix.hpp" +#include "IpSymMatrix.hpp" +#include "IpOptionsList.hpp" +#include "IpAlgTypes.hpp" +#include "IpReturnCodes.hpp" + +namespace Ipopt +{ + // forward declarations + class IpoptData; + class IpoptCalculatedQuantities; + class IteratesVector; + + /** Brief Class Description. + * Detailed Class Description. + */ + class NLP : public ReferencedObject + { + public: + /**@name Constructors/Destructors */ + //@{ + /** Default constructor */ + NLP() + {} + + /** Default destructor */ + virtual ~NLP() + {} + //@} + + /** Exceptions */ + //@{ + DECLARE_STD_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED); + DECLARE_STD_EXCEPTION(INVALID_NLP); + //@} + + /** @name NLP Initialization (overload in + * derived classes).*/ + //@{ + /** Overload if you want the chance to process options or parameters that + * may be specific to the NLP */ + virtual bool ProcessOptions(const OptionsList& options, + const std::string& prefix) + { + return true; + } + + /** Method for creating the derived vector / matrix types. The + * Hess_lagrangian_space pointer can be NULL if a quasi-Newton + * options is chosen. */ + virtual bool GetSpaces(SmartPtr<const VectorSpace>& x_space, + SmartPtr<const VectorSpace>& c_space, + SmartPtr<const VectorSpace>& d_space, + SmartPtr<const VectorSpace>& x_l_space, + SmartPtr<const MatrixSpace>& px_l_space, + SmartPtr<const VectorSpace>& x_u_space, + SmartPtr<const MatrixSpace>& px_u_space, + SmartPtr<const VectorSpace>& d_l_space, + SmartPtr<const MatrixSpace>& pd_l_space, + SmartPtr<const VectorSpace>& d_u_space, + SmartPtr<const MatrixSpace>& pd_u_space, + SmartPtr<const MatrixSpace>& Jac_c_space, + SmartPtr<const MatrixSpace>& Jac_d_space, + SmartPtr<const SymMatrixSpace>& Hess_lagrangian_space)=0; + + /** Method for obtaining the bounds information */ + virtual bool GetBoundsInformation(const Matrix& Px_L, + Vector& x_L, + const Matrix& Px_U, + Vector& x_U, + const Matrix& Pd_L, + Vector& d_L, + const Matrix& Pd_U, + Vector& d_U)=0; + + /** Method for obtaining the starting point for all the + * iterates. ToDo it might not make sense to ask for initial + * values for v_L and v_U? */ + virtual bool GetStartingPoint( + SmartPtr<Vector> x, + bool need_x, + SmartPtr<Vector> y_c, + bool need_y_c, + SmartPtr<Vector> y_d, + bool need_y_d, + SmartPtr<Vector> z_L, + bool need_z_L, + SmartPtr<Vector> z_U, + bool need_z_U + )=0; + + /** Method for obtaining an entire iterate as a warmstart point. + * The incoming IteratesVector has to be filled. The default + * dummy implementation returns false. */ + virtual bool GetWarmStartIterate(IteratesVector& warm_start_iterate) + { + return false; + } + //@} + + /** @name NLP evaluation routines (overload + * in derived classes. */ + //@{ + virtual bool Eval_f(const Vector& x, Number& f) = 0; + + virtual bool Eval_grad_f(const Vector& x, Vector& g_f) = 0; + + virtual bool Eval_c(const Vector& x, Vector& c) = 0; + + virtual bool Eval_jac_c(const Vector& x, Matrix& jac_c) = 0; + + virtual bool Eval_d(const Vector& x, Vector& d) = 0; + + virtual bool Eval_jac_d(const Vector& x, Matrix& jac_d) = 0; + + virtual bool Eval_h(const Vector& x, + Number obj_factor, + const Vector& yc, + const Vector& yd, + SymMatrix& h) = 0; + //@} + + /** @name NLP solution routines. Have default dummy + * implementations that can be overloaded. */ + //@{ + /** This method is called at the very end of the optimization. It + * provides the final iterate to the user, so that it can be + * stored as the solution. The status flag indicates the outcome + * of the optimization, where SolverReturn is defined in + * IpAlgTypes.hpp. */ + virtual void FinalizeSolution(SolverReturn status, + const Vector& x, const Vector& z_L, + const Vector& z_U, + const Vector& c, const Vector& d, + const Vector& y_c, const Vector& y_d, + Number obj_value, + const IpoptData* ip_data, + IpoptCalculatedQuantities* ip_cq) + {} + + /** This method is called once per iteration, after the iteration + * summary output has been printed. It provides the current + * information to the user to do with it anything she wants. It + * also allows the user to ask for a premature termination of the + * optimization by returning false, in which case Ipopt will + * terminate with a corresponding return status. The basic + * information provided in the argument list has the quantities + * values printed in the iteration summary line. If more + * information is required, a user can obtain it from the IpData + * and IpCalculatedQuantities objects. However, note that the + * provided quantities are all for the problem that Ipopt sees, + * i.e., the quantities might be scaled, fixed variables might be + * sorted out, etc. The status indicates things like whether the + * algorithm is in the restoration phase... In the restoration + * phase, the dual variables are probably not not changing. */ + virtual bool IntermediateCallBack(AlgorithmMode mode, + Index iter, Number obj_value, + Number inf_pr, Number inf_du, + Number mu, Number d_norm, + Number regularization_size, + Number alpha_du, Number alpha_pr, + Index ls_trials, + const IpoptData* ip_data, + IpoptCalculatedQuantities* ip_cq) + { + return true; + } + //@} + + /** Routines to get the scaling parameters. These do not need to + * be overloaded unless the options are set for User scaling + */ + //@{ + virtual void GetScalingParameters( + const SmartPtr<const VectorSpace> x_space, + const SmartPtr<const VectorSpace> c_space, + const SmartPtr<const VectorSpace> d_space, + Number& obj_scaling, + SmartPtr<Vector>& x_scaling, + SmartPtr<Vector>& c_scaling, + SmartPtr<Vector>& d_scaling) const + { + THROW_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED, + "You have set options for user provided scaling, but have" + " not implemented GetScalingParameters in the NLP interface"); + } + //@} + + /** Method for obtaining the subspace in which the limited-memory + * Hessian approximation should be done. This is only called if + * the limited-memory Hessian approximation is chosen. Since the + * Hessian is zero in the space of all variables that appear in + * the problem functions only linearly, this allows the user to + * provide a VectorSpace for all nonlinear variables, and an + * ExpansionMatrix to lift from this VectorSpace to the + * VectorSpace of the primal variables x. If the returned values + * are NULL, it is assumed that the Hessian is to be approximated + * in the space of all x variables. The default instantiation of + * this method returns NULL, and a user only has to overwrite + * this method if the approximation is to be done only in a + * subspace. */ + virtual void + GetQuasiNewtonApproximationSpaces(SmartPtr<VectorSpace>& approx_space, + SmartPtr<Matrix>& P_approx) + { + approx_space = NULL; + P_approx = NULL; + } + + private: + /**@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. */ + //@{ + /** Copy Constructor */ + NLP(const NLP&); + + /** Overloaded Equals Operator */ + void operator=(const NLP&); + //@} + }; + +} // namespace Ipopt + +#endif |