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authorGeorgey2017-07-05 11:40:43 +0530
committerGeorgey2017-07-05 11:40:43 +0530
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treeb343c0ee5609433c80e0de1db8b6886c9126dc2d /thirdparty/linux/include/coin1/IpIpoptNLP.hpp
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Added linux shared libraries and header files for int and ecos functions
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+// 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