// Copyright (C) 2004, 2009 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 __IPIPOPTDATA_HPP__
#define __IPIPOPTDATA_HPP__

#include "IpSymMatrix.hpp"
#include "IpOptionsList.hpp"
#include "IpIteratesVector.hpp"
#include "IpRegOptions.hpp"
#include "IpTimingStatistics.hpp"

namespace Ipopt
{

/* Forward declaration */
class IpoptNLP;

/** Base class for additional data that is special to a particular
 *  type of algorithm, such as the CG penalty function, or using
 *  iterative linear solvers.
 *
 *  The regular IpoptData object should
 *  be given a derivation of this base class when it is created.
 */
class IPOPTLIB_EXPORT IpoptAdditionalData: public ReferencedObject
{
public:
   /**@name Constructors/Destructors */
   ///@{
   /** Default Constructor */
   IpoptAdditionalData()
   { }

   /** Destructor */
   virtual ~IpoptAdditionalData()
   { }
   ///@}

   /** This method is called to initialize the global algorithmic
    *  parameters.
    *
    *  The parameters are taken from the OptionsList object.
    */
   virtual bool Initialize(
      const Journalist&  jnlst,
      const OptionsList& options,
      const std::string& prefix
   ) = 0;

   /** Initialize Data Structures at the beginning. */
   virtual bool InitializeDataStructures() = 0;

   /** Do whatever is necessary to accept a trial point as current iterate.
    *
    *  This is also used to finish an iteration, i.e., to
    *  release memory, and to reset any flags for a new iteration.
    */
   virtual void AcceptTrialPoint() = 0;

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 */
   IpoptAdditionalData(
      const IpoptAdditionalData&
   );

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

/** Class to organize all the data required by the algorithm.
 *
 *  Internally, once this Data object has been initialized, all
 *  internal curr_ vectors must always be set (so that prototypes are
 *  available).  The current values can only be set from the trial
 *  values.  The trial values can be set by copying from a vector or
 *  by adding some fraction of a step to the current values.  This
 *  object also stores steps, which allows to easily communicate the
 *  step from the step computation object to the line search object.
 */
class IPOPTLIB_EXPORT IpoptData: public ReferencedObject
{
public:
   /**@name Constructors/Destructors */
   ///@{
   /** Constructor */
   IpoptData(
      SmartPtr<IpoptAdditionalData> add_data = NULL
   );

   /** Destructor */
   virtual ~IpoptData();
   ///@}

   /** Initialize Data Structures */
   bool InitializeDataStructures(
      IpoptNLP& ip_nlp,
      bool      want_x,
      bool      want_y_c,
      bool      want_y_d,
      bool      want_z_L,
      bool      want_z_U
   );

   /** This method must be called to initialize the global
    *  algorithmic parameters.
    *
    *  The parameters are taken from the OptionsList object.
    */
   bool Initialize(
      const Journalist&  jnlst,
      const OptionsList& options,
      const std::string& prefix
   );

   /** @name Get Methods for Iterates */
   ///@{
   /** Current point */
   inline SmartPtr<const IteratesVector> curr() const;

   /** Get the current point in a copied container that is non-const.
    *
    * The entries in the container cannot be modified, but
    * the container can be modified to point to new entries.
    */
   //    SmartPtr<IteratesVector> curr_container() const;
   /** Get Trial point */
   inline SmartPtr<const IteratesVector> trial() const;

   /** Get Trial point in a copied container that is non-const.
    *
    *  The entries in the container can not be modified, but
    *  the container can be modified to point to new entries.
    */
   //SmartPtr<IteratesVector> trial_container() const;
   /** Set the trial point.
    *
    *  This method copies the pointer for
    *  efficiency (no copy and to keep cache tags the same) so
    *  after you call set you cannot modify the data again.
    */
   inline
   void set_trial(
      SmartPtr<IteratesVector>& trial
   );
   /* ToDo: I may need to add versions of set_trial like the
    *  following, but I am not sure
    */
   // void set_trial(const SmartPtr<IteratesVector>& trial_iterates);
   // void set_trial(SmartPtr<const IteratesVector>& trial_iterates);

   /** Set the values of the primal trial variables (x and s) from
    *  provided Step with step length alpha.
    */
   void SetTrialPrimalVariablesFromStep(
      Number        alpha,
      const Vector& delta_x,
      const Vector& delta_s
   );
   /** Set the values of the trial values for the equality constraint
    *  multipliers (y_c and y_d) from provided step with step length
    *  alpha.
    */
   void SetTrialEqMultipliersFromStep(
      Number        alpha,
      const Vector& delta_y_c,
      const Vector& delta_y_d
   );
   /** Set the value of the trial values for the bound multipliers
    *  (z_L, z_U, v_L, v_U) from provided step with step length
    *  alpha.
    */
   void SetTrialBoundMultipliersFromStep(
      Number        alpha,
      const Vector& delta_z_L,
      const Vector& delta_z_U,
      const Vector& delta_v_L,
      const Vector& delta_v_U
   );

   /** get the current delta */
   inline SmartPtr<const IteratesVector> delta() const;

   /** Set the current delta.
    *
    *  Like the trial point, this method copies
    *  the pointer for efficiency (no copy and to keep cache tags the
    *  same) so after you call set, you cannot modify the data
    */
   inline
   void set_delta(
      SmartPtr<IteratesVector>& delta
   );

   /** Set the current delta.
    *
    *  Like the trial point, this method
    *  copies the pointer for efficiency (no copy and to keep cache
    *  tags the same) so after you call set, you cannot modify the
    *  data.  This is the version that is happy with a pointer to
    *  const IteratesVector.
    */
   inline
   void set_delta(
      SmartPtr<const IteratesVector>& delta
   );

   /** Affine Delta */
   inline SmartPtr<const IteratesVector> delta_aff() const;

   /** Set the affine delta.
    *
    *  Like the trial point, this method copies
    *  the pointer for efficiency (no copy and to keep cache tags the
    *  same) so after you call set, you cannot modify the data
    */
   inline
   void set_delta_aff(
      SmartPtr<IteratesVector>& delta_aff
   );

   /** Hessian or Hessian approximation (do not hold on to it, it might be changed) */
   SmartPtr<const SymMatrix> W()
   {
      return W_;
   }

   /** Set Hessian approximation */
   void Set_W(
      SmartPtr<const SymMatrix> W
   )
   {
      W_ = W;
   }

   /** @name ("Main") Primal-dual search direction.
    *
    *  Those fields are
    *  used to store the search directions computed from solving the
    *  primal-dual system, and can be used in the line search.  They
    *  are overwritten in every iteration, so do not hold on to the
    *  pointers (make copies instead)
    */
   ///@{
   /** Returns true, if the primal-dual step have been already
    *  computed for the current iteration.
    *
    *  This flag is reset after
    *  every call of AcceptTrialPoint().  If the search direction is
    *  computed during the computation of the barrier parameter, the
    *  method computing the barrier parameter should call
    *  SetHaveDeltas(true) to tell the IpoptAlgorithm object that it
    *  doesn't need to recompute the primal-dual step.
    */
   bool HaveDeltas() const
   {
      return have_deltas_;
   }

   /** Method for setting the HaveDeltas flag.
    *
    *  This method should be
    *  called if some method computes the primal-dual step (and
    *  stores it in the delta_ fields of IpoptData) at an early part
    *  of the iteration.  If that flag is set to true, the
    *  IpoptAlgorithm object will not recompute the step.
    */
   void SetHaveDeltas(
      bool have_deltas
   )
   {
      have_deltas_ = have_deltas;
   }
   ///@}

   /** @name Affine-scaling step.
    *
    *  Those fields can be used to store
    *  the affine scaling step.  For example, if the method for
    *  computing the current barrier parameter computes the affine
    *  scaling steps, then the corrector step in the line search does
    *  not have to recompute those solutions of the linear system.
    */
   ///@{
   /** Returns true, if the affine-scaling step have been already
    *  computed for the current iteration.
    *
    *  This flag is reset after
    *  every call of AcceptTrialPoint().  If the search direction is
    *  computed during the computation of the barrier parameter, the
    *  method computing the barrier parameter should call
    *  SetHaveDeltas(true) to tell the line search does not have to
    *  recompute them in case it wants to do a corrector step.
    */
   bool HaveAffineDeltas() const
   {
      return have_affine_deltas_;
   }

   /** Method for setting the HaveDeltas flag.
    *
    *  This method should be
    *  called if some method computes the primal-dual step (and
    *  stores it in the delta_ fields of IpoptData) at an early part
    *  of the iteration.  If that flag is set to true, the
    *  IpoptAlgorithm object will not recompute the step.
    */
   void SetHaveAffineDeltas(
      bool have_affine_deltas
   )
   {
      have_affine_deltas_ = have_affine_deltas;
   }
   ///@}

   /** @name Public Methods for updating iterates */
   ///@{
   /** Copy the trial values to the current values */
   inline
   void CopyTrialToCurrent();

   /** Set the current iterate values from the trial values. */
   void AcceptTrialPoint();
   ///@}

   /** @name General algorithmic data */
   ///@{
   Index iter_count() const
   {
      return iter_count_;
   }
   void Set_iter_count(
      Index iter_count
   )
   {
      iter_count_ = iter_count;
   }

   Number curr_mu() const
   {
      DBG_ASSERT(mu_initialized_);
      return curr_mu_;
   }
   void Set_mu(
      Number mu
   )
   {
      curr_mu_ = mu;
      mu_initialized_ = true;
   }
   bool MuInitialized() const
   {
      return mu_initialized_;
   }

   Number curr_tau() const
   {
      DBG_ASSERT(tau_initialized_);
      return curr_tau_;
   }
   void Set_tau(
      Number tau
   )
   {
      curr_tau_ = tau;
      tau_initialized_ = true;
   }
   bool TauInitialized() const
   {
      return tau_initialized_;
   }

   void SetFreeMuMode(
      bool free_mu_mode
   )
   {
      free_mu_mode_ = free_mu_mode;
   }
   bool FreeMuMode() const
   {
      return free_mu_mode_;
   }

   /** Setting the flag that indicates if a tiny step (below machine
    *  precision) has been detected
    */
   void Set_tiny_step_flag(
      bool flag
   )
   {
      tiny_step_flag_ = flag;
   }
   bool tiny_step_flag()
   {
      return tiny_step_flag_;
   }
   ///@}

   /** Overall convergence tolerance.
    *
    *  It is used in the convergence
    *  test, but also in some other parts of the algorithm that
    *  depend on the specified tolerance, such as the minimum value
    *  for the barrier parameter.
    */
   ///@{
   /** Obtain the tolerance. */
   Number tol() const
   {
      DBG_ASSERT(initialize_called_);
      return tol_;
   }
   /** Set a new value for the tolerance.
    *
    *  One should be very careful
    *  when using this, since changing the predefined tolerance might
    *  have unexpected consequences.  This method is for example used
    *  in the restoration convergence checker to tighten the
    *  restoration phase convergence tolerance, if the restoration
    *  phase converged to a point that has not a large value for the
    *  constraint violation.
    */
   void Set_tol(
      Number tol
   )
   {
      tol_ = tol;
   }
   ///@}

   /** Cpu time counter at the beginning of the optimization.
    *
    *  This is useful to see how much CPU time has been spent in this
    *  optimization run.
    *
    *  Can only be called after beginning of optimization.
    */
   IPOPT_DEPRECATED
   Number cpu_time_start() const
   {
      return timing_statistics_.OverallAlgorithm().StartCpuTime();
   }

   /** @name Information gathered for iteration output */
   ///@{
   Number info_regu_x() const
   {
      return info_regu_x_;
   }
   void Set_info_regu_x(
      Number regu_x
   )
   {
      info_regu_x_ = regu_x;
   }
   Number info_alpha_primal() const
   {
      return info_alpha_primal_;
   }
   void Set_info_alpha_primal(
      Number alpha_primal
   )
   {
      info_alpha_primal_ = alpha_primal;
   }
   char info_alpha_primal_char() const
   {
      return info_alpha_primal_char_;
   }
   void Set_info_alpha_primal_char(
      char info_alpha_primal_char
   )
   {
      info_alpha_primal_char_ = info_alpha_primal_char;
   }
   Number info_alpha_dual() const
   {
      return info_alpha_dual_;
   }
   void Set_info_alpha_dual(
      Number alpha_dual
   )
   {
      info_alpha_dual_ = alpha_dual;
   }
   Index info_ls_count() const
   {
      return info_ls_count_;
   }
   void Set_info_ls_count(
      Index ls_count
   )
   {
      info_ls_count_ = ls_count;
   }
   bool info_skip_output() const
   {
      return info_skip_output_;
   }
   void Append_info_string(
      const std::string& add_str
   )
   {
      info_string_ += add_str;
   }
   const std::string& info_string() const
   {
      return info_string_;
   }
   /** Set this to true, if the next time when output is written, the
    *  summary line should not be printed.
    */
   void Set_info_skip_output(
      bool info_skip_output
   )
   {
      info_skip_output_ = info_skip_output;
   }

   /** gives time when the last summary output line was printed */
   Number info_last_output()
   {
      return info_last_output_;
   }
   /** sets time when the last summary output line was printed */
   void Set_info_last_output(
      Number info_last_output
   )
   {
      info_last_output_ = info_last_output;
   }

   /** gives number of iteration summaries actually printed
    * since last summary header was printed
    */
   int info_iters_since_header()
   {
      return info_iters_since_header_;
   }
   /** increases number of iteration summaries actually printed
    * since last summary header was printed
    */
   void Inc_info_iters_since_header()
   {
      info_iters_since_header_++;
   }
   /** sets number of iteration summaries actually printed
    * since last summary header was printed
    */
   void Set_info_iters_since_header(
      int info_iters_since_header
   )
   {
      info_iters_since_header_ = info_iters_since_header;
   }

   /** Reset all info fields */
   void ResetInfo()
   {
      info_regu_x_ = 0;
      info_alpha_primal_ = 0;
      info_alpha_dual_ = 0.;
      info_alpha_primal_char_ = ' ';
      info_skip_output_ = false;
      info_string_.erase();
   }
   ///@}

   /** Return Timing Statistics Object */
   TimingStatistics& TimingStats()
   {
      return timing_statistics_;
   }

   /** Return Timing Statistics Object
    *
    * @since 3.14.11
    */
   const TimingStatistics& TimingStats() const
   {
      return timing_statistics_;
   }

   /** Check if additional data has been set */
   bool HaveAddData()
   {
      return IsValid(add_data_);
   }

   /** Get access to additional data object */
   IpoptAdditionalData& AdditionalData()
   {
      return *add_data_;
   }

   /** Set a new pointer for additional Ipopt data */
   void SetAddData(
      SmartPtr<IpoptAdditionalData> add_data
   )
   {
      // cppcheck-suppress assertWithSideEffect
      DBG_ASSERT(!HaveAddData());
      add_data_ = add_data;
   }

   /** Set the perturbation of the primal-dual system */
   void setPDPert(
      Number pd_pert_x,
      Number pd_pert_s,
      Number pd_pert_c,
      Number pd_pert_d
   )
   {
      pd_pert_x_ = pd_pert_x;
      pd_pert_s_ = pd_pert_s;
      pd_pert_c_ = pd_pert_c;
      pd_pert_d_ = pd_pert_d;
   }

   /** Get the current perturbation of the primal-dual system */
   void getPDPert(
      Number& pd_pert_x,
      Number& pd_pert_s,
      Number& pd_pert_c,
      Number& pd_pert_d
   )
   {
      pd_pert_x = pd_pert_x_;
      pd_pert_s = pd_pert_s_;
      pd_pert_c = pd_pert_c_;
      pd_pert_d = pd_pert_d_;
   }

   static void RegisterOptions(
      const SmartPtr<RegisteredOptions>& roptions
   );

private:
   /** @name Iterates */
   ///@{
   /** Main iteration variables (current iteration) */
   SmartPtr<const IteratesVector> curr_;

   /** Main iteration variables (trial calculations) */
   SmartPtr<const IteratesVector> trial_;

   /** Hessian (approximation) - might be changed elsewhere! */
   SmartPtr<const SymMatrix> W_;

   /** @name Primal-dual Step */
   ///@{
   SmartPtr<const IteratesVector> delta_;
   /** The following flag is set to true, if some other part of the
    *  algorithm (like the method for computing the barrier
    *  parameter) has already computed the primal-dual search
    *  direction.
    *
    *  This flag is reset when the AcceptTrialPoint method is called.
    */
   // ToDo we could cue off of a null delta_
   bool have_deltas_;
   ///@}

   /** @name Affine-scaling step.
    *
    *  This used to transfer the
    *  information about the affine-scaling step from the computation
    *  of the barrier parameter to the corrector (in the line
    *  search).
    */
   ///@{
   SmartPtr<const IteratesVector> delta_aff_;
   /** The following flag is set to true, if some other part of the
    *  algorithm (like the method for computing the barrier
    *  parameter) has already computed the affine-scaling step.
    *
    *  This flag is reset when the AcceptTrialPoint method is called.
    */
   // ToDo we could cue off of a null delta_aff_
   bool have_affine_deltas_;
   ///@}

   /** iteration count */
   Index iter_count_;

   /** current barrier parameter */
   Number curr_mu_;
   bool mu_initialized_;

   /** current fraction to the boundary parameter */
   Number curr_tau_;
   bool tau_initialized_;

   /** flag indicating if Initialize method has been called (for debugging) */
   bool initialize_called_;

   /** flag for debugging whether we have already curr_ values
    *  available (from which new Vectors can be generated
    */
   bool have_prototypes_;

   /** @name Global algorithm parameters.
    *
    *  Those are options that can
    *  be modified by the user and appear at different places in the
    *  algorithm.  They are set using an OptionsList object in the
    *  Initialize method.
    */
   ///@{
   /** Overall convergence tolerance */
   Number tol_;
   ///@}

   /** @name Status data **/
   ///@{
   /** flag indicating whether the algorithm is in the free mu mode */
   bool free_mu_mode_;
   /** flag indicating if a tiny step has been detected */
   bool tiny_step_flag_;
   ///@}

   /** @name Gathered information for iteration output */
   ///@{
   /** Size of regularization for the Hessian */
   Number info_regu_x_;
   /** Primal step size */
   Number info_alpha_primal_;
   /** Info character for primal step size */
   char info_alpha_primal_char_;
   /** Dual step size */
   Number info_alpha_dual_;
   /** Number of backtracking trial steps */
   Index info_ls_count_;
   /** true, if next summary output line should not be printed (eg
    *  after restoration phase).
    */
   bool info_skip_output_;
   /** any string of characters for the end of the output line */
   std::string info_string_;
   /** time when the last summary output line was printed */
   Number info_last_output_;
   /** number of iteration summaries actually printed since last
    * summary header was printed
    */
   int info_iters_since_header_;
   ///@}

   /** VectorSpace for all the iterates */
   SmartPtr<IteratesVectorSpace> iterates_space_;

   /** TimingStatistics object collecting all Ipopt timing statistics */
   TimingStatistics timing_statistics_;

   /** Object for the data specific for the Chen-Goldfarb penalty
    *  method algorithm
    */
   SmartPtr<IpoptAdditionalData> add_data_;

   /** @name Information about the perturbation of the primal-dual system */
   ///@{
   Number pd_pert_x_;
   Number pd_pert_s_;
   Number pd_pert_c_;
   Number pd_pert_d_;
   ///@}

   /**@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 */
   IpoptData(
      const IpoptData&
   );

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

#if IPOPT_CHECKLEVEL > 0
   /** Some debug flags to make sure vectors are not changed
    *  behind the IpoptData's back
    */
   ///@{
   TaggedObject::Tag debug_curr_tag_;
   TaggedObject::Tag debug_trial_tag_;
   TaggedObject::Tag debug_delta_tag_;
   TaggedObject::Tag debug_delta_aff_tag_;
   TaggedObject::Tag debug_curr_tag_sum_;
   TaggedObject::Tag debug_trial_tag_sum_;
   TaggedObject::Tag debug_delta_tag_sum_;
   TaggedObject::Tag debug_delta_aff_tag_sum_;
   ///@}
#endif

};

inline SmartPtr<const IteratesVector> IpoptData::curr() const
{
   DBG_ASSERT(IsNull(curr_) || (curr_->GetTag() == debug_curr_tag_ && curr_->GetTagSum() == debug_curr_tag_sum_) );

   return curr_;
}

inline SmartPtr<const IteratesVector> IpoptData::trial() const
{
   DBG_ASSERT(IsNull(trial_) || (trial_->GetTag() == debug_trial_tag_ && trial_->GetTagSum() == debug_trial_tag_sum_) );

   return trial_;
}

inline SmartPtr<const IteratesVector> IpoptData::delta() const
{
   DBG_ASSERT(IsNull(delta_) || (delta_->GetTag() == debug_delta_tag_ && delta_->GetTagSum() == debug_delta_tag_sum_) );

   return delta_;
}

inline SmartPtr<const IteratesVector> IpoptData::delta_aff() const
{
   DBG_ASSERT(IsNull(delta_aff_) || (delta_aff_->GetTag() == debug_delta_aff_tag_ && delta_aff_->GetTagSum() == debug_delta_aff_tag_sum_) );

   return delta_aff_;
}

inline
void IpoptData::CopyTrialToCurrent()
{
   curr_ = trial_;
#if IPOPT_CHECKLEVEL > 0

   if (IsValid(curr_))
   {
      debug_curr_tag_ = curr_->GetTag();
      debug_curr_tag_sum_ = curr_->GetTagSum();
   }
   else
   {
      debug_curr_tag_ = 0;
      debug_curr_tag_sum_ = 0;
   }
#endif

}

inline
void IpoptData::set_trial(
   SmartPtr<IteratesVector>& trial
)
{
   trial_ = ConstPtr(trial);

#if IPOPT_CHECKLEVEL > 0
   // verify the correct space
   DBG_ASSERT(trial_->OwnerSpace() == static_cast<VectorSpace*>(GetRawPtr(iterates_space_)));
   if (IsValid(trial))
   {
      debug_trial_tag_ = trial->GetTag();
      debug_trial_tag_sum_ = trial->GetTagSum();
   }
   else
   {
      debug_trial_tag_ = 0;
      debug_trial_tag_sum_ = 0;
   }
#endif

   trial = NULL;
}

inline
void IpoptData::set_delta(
   SmartPtr<IteratesVector>& delta
)
{
   delta_ = ConstPtr(delta);
#if IPOPT_CHECKLEVEL > 0

   if (IsValid(delta))
   {
      debug_delta_tag_ = delta->GetTag();
      debug_delta_tag_sum_ = delta->GetTagSum();
   }
   else
   {
      debug_delta_tag_ = 0;
      debug_delta_tag_sum_ = 0;
   }
#endif

   delta = NULL;
}

inline
void IpoptData::set_delta(
   SmartPtr<const IteratesVector>& delta
)
{
   delta_ = delta;
#if IPOPT_CHECKLEVEL > 0

   if (IsValid(delta))
   {
      debug_delta_tag_ = delta->GetTag();
      debug_delta_tag_sum_ = delta->GetTagSum();
   }
   else
   {
      debug_delta_tag_ = 0;
      debug_delta_tag_sum_ = 0;
   }
#endif

   delta = NULL;
}

inline
void IpoptData::set_delta_aff(
   SmartPtr<IteratesVector>& delta_aff
)
{
   delta_aff_ = ConstPtr(delta_aff);
#if IPOPT_CHECKLEVEL > 0

   if (IsValid(delta_aff))
   {
      debug_delta_aff_tag_ = delta_aff->GetTag();
      debug_delta_aff_tag_sum_ = delta_aff->GetTagSum();
   }
   else
   {
      debug_delta_aff_tag_ = 0;
      debug_delta_aff_tag_sum_ = delta_aff->GetTagSum();
   }
#endif

   delta_aff = NULL;
}

} // namespace Ipopt

#endif