// Copyright (C) 2004, 2008 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Common Public License.
//
// $Id: IpTNLP.hpp 1238 2008-05-23 21:02:23Z andreasw $
//
// Authors:  Carl Laird, Andreas Waechter     IBM    2004-08-13

#ifndef __IPTNLP_HPP__
#define __IPTNLP_HPP__

#include "IpUtils.hpp"
#include "IpReferenced.hpp"
#include "IpException.hpp"
#include "IpAlgTypes.hpp"
#include "IpReturnCodes.hpp"

namespace Ipopt
{
  // forward declarations
  class IpoptData;
  class IpoptCalculatedQuantities;
  class IteratesVector;

  /** Base class for all NLP's that use standard triplet matrix form
   *  and dense vectors.  This is the standard base class for all
   *  NLP's that use the standard triplet matrix form (as for Harwell
   *  routines) and dense vectors. The class TNLPAdapter then converts
   *  this interface to an interface that can be used directly by
   *  ipopt.
   *
   *  This interface presents the problem form:
   *  
   *     min f(x)
   *
   *     s.t. gL <= g(x) <= gU
   *
   *          xL <=  x   <= xU
   *
   *  In order to specify an equality constraint, set gL_i = gU_i =
   *  rhs.  The value that indicates "infinity" for the bounds
   *  (i.e. the variable or constraint has no lower bound (-infinity)
   *  or upper bound (+infinity)) is set through the option
   *  nlp_lower_bound_inf and nlp_upper_bound_inf.  To indicate that a
   *  variable has no upper or lower bound, set the bound to
   *  -ipopt_inf or +ipopt_inf respectively
   */
  class TNLP : public ReferencedObject
  {
  public:
    /** Type of the constraints*/
    enum LinearityType
    {
      LINEAR/** Constraint/Variable is linear.*/,
      NON_LINEAR/**Constraint/Varaible is non-linear.*/
    };

    /**@name Constructors/Destructors */
    //@{
    TNLP()
    {}

    /** Default destructor */
    virtual ~TNLP()
    {}
    //@}

    DECLARE_STD_EXCEPTION(INVALID_TNLP);

    /**@name methods to gather information about the NLP */
    //@{
    /** overload this method to return the number of variables
     *  and constraints, and the number of non-zeros in the jacobian and
     *  the hessian. The index_style parameter lets you specify C or Fortran
     *  style indexing for the sparse matrix iRow and jCol parameters.
     *  C_STYLE is 0-based, and FORTRAN_STYLE is 1-based.
     */
    enum IndexStyleEnum { C_STYLE=0, FORTRAN_STYLE=1 };
    virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
                              Index& nnz_h_lag, IndexStyleEnum& index_style)=0;

    /** overload this method to return the information about the bound
     *  on the variables and constraints. The value that indicates
     *  that a bound does not exist is specified in the parameters
     *  nlp_lower_bound_inf and nlp_upper_bound_inf.  By default,
     *  nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is
     *  1e19. (see TNLPAdapter) */
    virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u,
                                 Index m, Number* g_l, Number* g_u)=0;

    /** overload this method to return scaling parameters. This is
     *  only called if the options are set to retrieve user scaling.
     *  There, use_x_scaling (or use_g_scaling) should get set to true
     *  only if the variables (or constraints) are to be scaled.  This
     *  method should return true only if the scaling parameters could
     *  be provided.
     */
    virtual bool get_scaling_parameters(Number& obj_scaling,
                                        bool& use_x_scaling, Index n,
                                        Number* x_scaling,
                                        bool& use_g_scaling, Index m,
                                        Number* g_scaling)
    {
      return false;
    }

    /** overload this method to return the variables linearity
     * (TNLP::Linear or TNLP::NonLinear). The var_types
     *  array should be allocated with length at least n. (default implementation
     *  just return false and does not fill the array).*/
    virtual bool get_variables_linearity(Index n, LinearityType* var_types)
    {
      return false;
    }

    /** overload this method to return the constraint linearity.
     * array should be alocated with length at least n. (default implementation
     *  just return false and does not fill the array).*/
    virtual bool get_constraints_linearity(Index m, LinearityType* const_types)
    {
      return false;
    }

    /** overload this method to return the starting point. The bool
     *  variables indicate whether the algorithm wants you to
     *  initialize x, z_L/z_u, and lambda, respectively.  If, for some
     *  reason, the algorithm wants you to initialize these and you
     *  cannot, return false, which will cause Ipopt to stop.  You
     *  will have to run Ipopt with different options then.
     */
    virtual bool get_starting_point(Index n, bool init_x, Number* x,
                                    bool init_z, Number* z_L, Number* z_U,
                                    Index m, bool init_lambda,
                                    Number* lambda)=0;

    /** overload this method to provide an Ipopt iterate (already in
     *  the form Ipopt requires it internally) for a warm start.
     *  Since this is only for expert users, a default dummy
     *  implementation is provided and returns false. */
    virtual bool get_warm_start_iterate(IteratesVector& warm_start_iterate)
    {
      return false;
    }

    /** overload this method to return the value of the objective function */
    virtual bool eval_f(Index n, const Number* x, bool new_x,
                        Number& obj_value)=0;

    /** overload this method to return the vector of the gradient of
     *  the objective w.r.t. x */
    virtual bool eval_grad_f(Index n, const Number* x, bool new_x,
                             Number* grad_f)=0;

    /** overload this method to return the vector of constraint values */
    virtual bool eval_g(Index n, const Number* x, bool new_x,
                        Index m, Number* g)=0;
    /** overload this method to return the jacobian of the
     *  constraints. The vectors iRow and jCol only need to be set
     *  once. The first call is used to set the structure only (iRow
     *  and jCol will be non-NULL, and values will be NULL) For
     *  subsequent calls, iRow and jCol will be NULL. */
    virtual bool eval_jac_g(Index n, const Number* x, bool new_x,
                            Index m, Index nele_jac, Index* iRow,
                            Index *jCol, Number* values)=0;

    /** overload this method to return the hessian of the
     *  lagrangian. The vectors iRow and jCol only need to be set once
     *  (during the first call). The first call is used to set the
     *  structure only (iRow and jCol will be non-NULL, and values
     *  will be NULL) For subsequent calls, iRow and jCol will be
     *  NULL. This matrix is symmetric - specify the lower diagonal
     *  only.  A default implementation is provided, in case the user
     *  wants to se quasi-Newton approximations to estimate the second
     *  derivatives and doesn't not neet to implement this method. */
    virtual bool eval_h(Index n, const Number* x, bool new_x,
                        Number obj_factor, Index m, const Number* lambda,
                        bool new_lambda, Index nele_hess,
                        Index* iRow, Index* jCol, Number* values)
    {
      return false;
    }

    /** this method is called if option ??? is set.  In that case, the
    algorithm calls this method with the values of variables and
    multipliers that will be used for the next calls of any
    evaluation method.  */
    virtual bool set_variable_values(Index n, const Number* x, bool new_x,
                                     Index m, const Number* lambda,
                                     bool new_lambda)
    {
      return false;
    }
    //@}

    /** @name Solution Methods */
    //@{
    /** This method is called when the algorithm is complete so the TNLP can store/write the solution */
    virtual void finalize_solution(SolverReturn status,
                                   Index n, const Number* x, const Number* z_L, const Number* z_U,
                                   Index m, const Number* g, const Number* lambda,
                                   Number obj_value,
                                   const IpoptData* ip_data,
                                   IpoptCalculatedQuantities* ip_cq)=0;

    /** Intermediate Callback method for the user.  Providing dummy
     *  default implementation.  For details see IntermediateCallBack
     *  in IpNLP.hpp. */
    virtual bool intermediate_callback(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;
    }
    //@}

    /** @name Methods for quasi-Newton approximation.  If the second
     *  derivatives are approximated by Ipopt, it is better to do this
     *  only in the space of nonlinear variables.  The following
     *  methods are call by Ipopt if the quasi-Newton approximation is
     *  selected.  If -1 is returned as number of nonlinear variables,
     *  Ipopt assumes that all variables are nonlinear.  Otherwise, it
     *  calls get_list_of_nonlinear_variables with an array into which
     *  the indices of the nonlinear variables should be written - the
     *  array has the lengths num_nonlin_vars, which is identical with
     *  the return value of get_number_of_nonlinear_variables().  It
     *  is assumed that the indices are counted starting with 1 in the
     *  FORTRAN_STYLE, and 0 for the C_STYLE. */
    //@{
    virtual Index get_number_of_nonlinear_variables()
    {
      return -1;
    }

    virtual bool get_list_of_nonlinear_variables(Index num_nonlin_vars,
        Index* pos_nonlin_vars)
    {
      return false;
    }
    //@}

  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. */
    //@{
    /** Default Constructor */
    //TNLP();

    /** Copy Constructor */
    TNLP(const TNLP&);

    /** Overloaded Equals Operator */
    void operator=(const TNLP&);
    //@}
  };

} // namespace Ipopt

#endif
