// Copyright (C) 2004, 2006 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// 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 for %Ipopt.
 *
 *  As for NLP, the represented %NLP is
 *  \f{eqnarray*}
 *     \mathrm{min}  && f(x), \\
 *     \mathrm{s.t.} && c(x) = 0,               &\qquad y_c\\
 *                   && d_L \leq d(x) \leq d_U, &\qquad y_d \\
 *                   && x_L \leq  x \leq x_U,   &\qquad z_L, z_U
 *  \f}
 *  where \f$y_c\f$, \f$y_d\f$, \f$z_L\f$, \f$z_U\f$ name the dual variables of the corresponding constraints.
 *
 *  This class takes care of storing the
 *  calculated model results, handles caching, scaling,
 *  and (some day) takes care of addition of slacks.
 */
class IPOPTLIB_EXPORT IpoptNLP: public ReferencedObject
{
public:
   /**@name Constructors/Destructors */
   ///@{
   IpoptNLP(
      const SmartPtr<NLPScalingObject> nlp_scaling
   )
      : nlp_scaling_(nlp_scaling)
   { }

   /** 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&
   );

   /** Default Assignment Operator */
   void operator=(
      const IpoptNLP&
   );
   ///@}

   SmartPtr<NLPScalingObject> nlp_scaling_;
};

} // namespace Ipopt

#endif
