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Diffstat (limited to 'newstructure/thirdparty/linux/include/coin/IpIpoptNLP.hpp')
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diff --git a/newstructure/thirdparty/linux/include/coin/IpIpoptNLP.hpp b/newstructure/thirdparty/linux/include/coin/IpIpoptNLP.hpp new file mode 100644 index 0000000..21951c3 --- /dev/null +++ b/newstructure/thirdparty/linux/include/coin/IpIpoptNLP.hpp @@ -0,0 +1,261 @@ +// Copyright (C) 2004, 2006 International Business Machines and others. +// All Rights Reserved. +// This code is published under the Eclipse Public License. +// +// $Id: IpIpoptNLP.hpp 2594 2015-08-09 14:31:05Z stefan $ +// +// Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 + +#ifndef __IPIPOPTNLP_HPP__ +#define __IPIPOPTNLP_HPP__ + +#include "IpNLP.hpp" +#include "IpJournalist.hpp" +#include "IpNLPScaling.hpp" + +namespace Ipopt +{ + // forward declarations + class IteratesVector; + + /** This is the abstract base class for classes that map + * the traditional NLP into + * something that is more useful by Ipopt. + * This class takes care of storing the + * calculated model results, handles cacheing, + * and (some day) takes care of addition of slacks. + */ + class IpoptNLP : public ReferencedObject + { + public: + /**@name Constructors/Destructors */ + //@{ + IpoptNLP(const SmartPtr<NLPScalingObject> nlp_scaling) + : + nlp_scaling_(nlp_scaling) + {} + + /** Default destructor */ + virtual ~IpoptNLP() + {} + //@} + + /** Initialization method. Set the internal options and + * initialize internal data structures. */ + virtual bool Initialize(const Journalist& jnlst, + const OptionsList& options, + const std::string& prefix) + { + bool ret = true; + if (IsValid(nlp_scaling_)) { + ret = nlp_scaling_->Initialize(jnlst, options, prefix); + } + return ret; + } + + /**@name Possible Exceptions */ + //@{ + /** thrown if there is any error evaluating values from the nlp */ + DECLARE_STD_EXCEPTION(Eval_Error); + //@} + /** Initialize (create) structures for + * the iteration data */ + virtual bool InitializeStructures(SmartPtr<Vector>& x, + bool init_x, + SmartPtr<Vector>& y_c, + bool init_y_c, + SmartPtr<Vector>& y_d, + bool init_y_d, + SmartPtr<Vector>& z_L, + bool init_z_L, + SmartPtr<Vector>& z_U, + bool init_z_U, + SmartPtr<Vector>& v_L, + SmartPtr<Vector>& v_U + ) = 0; + + /** Method accessing the GetWarmStartIterate of the NLP */ + virtual bool GetWarmStartIterate(IteratesVector& warm_start_iterate)=0; + + /** Accessor methods for model data */ + //@{ + /** Objective value */ + virtual Number f(const Vector& x) = 0; + + /** Gradient of the objective */ + virtual SmartPtr<const Vector> grad_f(const Vector& x) = 0; + + /** Equality constraint residual */ + virtual SmartPtr<const Vector> c(const Vector& x) = 0; + + /** Jacobian Matrix for equality constraints */ + virtual SmartPtr<const Matrix> jac_c(const Vector& x) = 0; + + /** Inequality constraint residual (reformulated + * as equalities with slacks */ + virtual SmartPtr<const Vector> d(const Vector& x) = 0; + + /** Jacobian Matrix for inequality constraints */ + virtual SmartPtr<const Matrix> jac_d(const Vector& x) = 0; + + /** Hessian of the Lagrangian */ + virtual SmartPtr<const SymMatrix> h(const Vector& x, + Number obj_factor, + const Vector& yc, + const Vector& yd + ) = 0; + + /** Lower bounds on x */ + virtual SmartPtr<const Vector> x_L() const = 0; + + /** Permutation matrix (x_L_ -> x) */ + virtual SmartPtr<const Matrix> Px_L() const = 0; + + /** Upper bounds on x */ + virtual SmartPtr<const Vector> x_U() const = 0; + + /** Permutation matrix (x_U_ -> x */ + virtual SmartPtr<const Matrix> Px_U() const = 0; + + /** Lower bounds on d */ + virtual SmartPtr<const Vector> d_L() const = 0; + + /** Permutation matrix (d_L_ -> d) */ + virtual SmartPtr<const Matrix> Pd_L() const = 0; + + /** Upper bounds on d */ + virtual SmartPtr<const Vector> d_U() const = 0; + + /** Permutation matrix (d_U_ -> d */ + virtual SmartPtr<const Matrix> Pd_U() const = 0; + + /** x_space */ + virtual SmartPtr<const VectorSpace> x_space() const = 0; + + /** Accessor method to obtain the MatrixSpace for the Hessian + * matrix (or it's approximation) */ + virtual SmartPtr<const SymMatrixSpace> HessianMatrixSpace() const = 0; + //@} + + /** Accessor method for vector/matrix spaces pointers. */ + virtual void 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 adapting the variable bounds. This is called if + * slacks are becoming too small */ + virtual void AdjustVariableBounds(const Vector& new_x_L, + const Vector& new_x_U, + const Vector& new_d_L, + const Vector& new_d_U)=0; + + /** @name Counters for the number of function evaluations. */ + //@{ + virtual Index f_evals() const = 0; + virtual Index grad_f_evals() const = 0; + virtual Index c_evals() const = 0; + virtual Index jac_c_evals() const = 0; + virtual Index d_evals() const = 0; + virtual Index jac_d_evals() const = 0; + virtual Index h_evals() const = 0; + //@} + + /** @name Special method for dealing with the fact that the + * restoration phase objective function depends on the barrier + * parameter */ + //@{ + /** Method for telling the IpoptCalculatedQuantities class whether + * the objective function depends on the barrier function. This + * is only used for the restoration phase NLP + * formulation. Probably only RestoIpoptNLP should overwrite + * this. */ + virtual bool objective_depends_on_mu() const + { + return false; + } + + /** Replacement for the default objective function method which + * knows about the barrier parameter */ + virtual Number f(const Vector& x, Number mu) = 0; + + /** Replacement for the default objective gradient method which + * knows about the barrier parameter */ + virtual SmartPtr<const Vector> grad_f(const Vector& x, Number mu) = 0; + + /** Replacement for the default Lagrangian Hessian method which + * knows about the barrier parameter */ + virtual SmartPtr<const SymMatrix> h(const Vector& x, + Number obj_factor, + const Vector& yc, + const Vector& yd, + Number mu) = 0; + + /** Provides a Hessian matrix from the correct matrix space with + * uninitialized values. This can be used in LeastSquareMults to + * obtain a "zero Hessian". */ + virtual SmartPtr<const SymMatrix> uninitialized_h() = 0; + //@} + + /**@name solution routines */ + //@{ + 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)=0; + + 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, + SmartPtr<const IpoptData> ip_data, + SmartPtr<IpoptCalculatedQuantities> ip_cq)=0; + //@} + + /** Returns the scaling strategy object */ + SmartPtr<NLPScalingObject> NLP_scaling() const + { + DBG_ASSERT(IsValid(nlp_scaling_)); + return nlp_scaling_; + } + + 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 */ + IpoptNLP(const IpoptNLP&); + + /** Overloaded Equals Operator */ + void operator=(const IpoptNLP&); + //@} + + SmartPtr<NLPScalingObject> nlp_scaling_; + }; + +} // namespace Ipopt + +#endif |