/* $Id: AbcNonLinearCost.hpp 1910 2013-01-27 02:00:13Z stefan $ */
// Copyright (C) 2002, International Business Machines
// Corporation and others, Copyright (C) 2012, FasterCoin.  All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).

#ifndef AbcNonLinearCost_H
#define AbcNonLinearCost_H


#include "CoinPragma.hpp"
#include "AbcCommon.hpp"

class AbcSimplex;
class CoinIndexedVector;

/** Trivial class to deal with non linear costs
    
    I don't make any explicit assumptions about convexity but I am
    sure I do make implicit ones.
    
    One interesting idea for normal LP's will be to allow non-basic
    variables to come into basis as infeasible i.e. if variable at
    lower bound has very large positive reduced cost (when problem
    is infeasible) could it reduce overall problem infeasibility more
    by bringing it into basis below its lower bound.
    
    Another feature would be to automatically discover when problems
    are convex piecewise linear and re-formulate to use non-linear.
    I did some work on this many years ago on "grade" problems, but
    while it improved primal interior point algorithms were much better
    for that particular problem.
*/
/* status has original status and current status
   0 - below lower so stored is upper
   1 - in range
   2 - above upper so stored is lower
   4 - (for current) - same as original
*/
#define CLP_BELOW_LOWER 0
#define CLP_FEASIBLE 1
#define CLP_ABOVE_UPPER 2
#define CLP_SAME 4
inline int originalStatus(unsigned char status)
{
  return (status & 15);
}
inline int currentStatus(unsigned char status)
{
  return (status >> 4);
}
inline void setOriginalStatus(unsigned char & status, int value)
{
  status = static_cast<unsigned char>(status & ~15);
  status = static_cast<unsigned char>(status | value);
}
inline void setCurrentStatus(unsigned char &status, int value)
{
  status = static_cast<unsigned char>(status & ~(15 << 4));
  status = static_cast<unsigned char>(status | (value << 4));
}
inline void setInitialStatus(unsigned char &status)
{
  status = static_cast<unsigned char>(CLP_FEASIBLE | (CLP_SAME << 4));
}
inline void setSameStatus(unsigned char &status)
{
  status = static_cast<unsigned char>(status & ~(15 << 4));
  status = static_cast<unsigned char>(status | (CLP_SAME << 4));
}
class AbcNonLinearCost  {
  
public:
  
  /**@name Constructors, destructor */
  //@{
  /// Default constructor.
  AbcNonLinearCost();
  /** Constructor from simplex.
      This will just set up wasteful arrays for linear, but
      later may do dual analysis and even finding duplicate columns .
  */
  AbcNonLinearCost(AbcSimplex * model);
  /// Destructor
  ~AbcNonLinearCost();
  // Copy
  AbcNonLinearCost(const AbcNonLinearCost&);
  // Assignment
  AbcNonLinearCost& operator=(const AbcNonLinearCost&);
  //@}
  
  
  /**@name Actual work in primal */
  //@{
  /** Changes infeasible costs and computes number and cost of infeas
      Puts all non-basic (non free) variables to bounds
      and all free variables to zero if oldTolerance is non-zero
      - but does not move those <= oldTolerance away*/
  void checkInfeasibilities(double oldTolerance = 0.0);
  /** Changes infeasible costs for each variable
      The indices are row indices and need converting to sequences
  */
  void checkInfeasibilities(int numberInArray, const int * index);
  /** Puts back correct infeasible costs for each variable
      The input indices are row indices and need converting to sequences
      for costs.
      On input array is empty (but indices exist).  On exit just
      changed costs will be stored as normal CoinIndexedVector
  */
  void checkChanged(int numberInArray, CoinIndexedVector * update);
  /** Goes through one bound for each variable.
      If multiplier*work[iRow]>0 goes down, otherwise up.
      The indices are row indices and need converting to sequences
      Temporary offsets may be set
      Rhs entries are increased
  */
  void goThru(int numberInArray, double multiplier,
	      const int * index, const double * work,
	      double * rhs);
  /** Takes off last iteration (i.e. offsets closer to 0)
   */
  void goBack(int numberInArray, const int * index,
	      double * rhs);
  /** Puts back correct infeasible costs for each variable
      The input indices are row indices and need converting to sequences
      for costs.
      At the end of this all temporary offsets are zero
  */
  void goBackAll(const CoinIndexedVector * update);
  /// Temporary zeroing of feasible costs
  void zapCosts();
  /// Refreshes costs always makes row costs zero
  void refreshCosts(const double * columnCosts);
  /// Puts feasible bounds into lower and upper
  void feasibleBounds();
  /// Refresh - assuming regions OK
  void refresh();
  /// Refresh - from original
  void refreshFromPerturbed(double tolerance);
  /** Sets bounds and cost for one variable
      Returns change in cost
      May need to be inline for speed */
  double setOne(int sequence, double solutionValue);
  /** Sets bounds and cost for one variable
      Returns change in cost
      May need to be inline for speed */
  double setOneBasic(int iRow, double solutionValue);
  /** Sets bounds and cost for outgoing variable
      may change value
      Returns direction */
  int setOneOutgoing(int sequence, double &solutionValue);
  /// Returns nearest bound
  double nearest(int iRow, double solutionValue);
  /** Returns change in cost - one down if alpha >0.0, up if <0.0
      Value is current - new
  */
  inline double changeInCost(int /*sequence*/, double alpha) const {
    return (alpha > 0.0) ? infeasibilityWeight_ : -infeasibilityWeight_;
  }
  inline double changeUpInCost(int /*sequence*/) const {
    return -infeasibilityWeight_;
  }
  inline double changeDownInCost(int /*sequence*/) const {
    return infeasibilityWeight_;
  }
  /// This also updates next bound
  inline double changeInCost(int iRow, double alpha, double &rhs) {
    int sequence=model_->pivotVariable()[iRow];
    double returnValue = 0.0;
    unsigned char iStatus = status_[sequence];
    int iWhere = currentStatus(iStatus);
    if (iWhere == CLP_SAME)
      iWhere = originalStatus(iStatus);
    // rhs always increases
    if (iWhere == CLP_FEASIBLE) {
      if (alpha > 0.0) {
	// going below
	iWhere = CLP_BELOW_LOWER;
	rhs = COIN_DBL_MAX;
      } else {
	// going above
	iWhere = CLP_ABOVE_UPPER;
	rhs = COIN_DBL_MAX;
      }
    } else if (iWhere == CLP_BELOW_LOWER) {
      assert (alpha < 0);
      // going feasible
      iWhere = CLP_FEASIBLE;
      rhs += bound_[sequence] - model_->upperRegion()[sequence];
    } else {
      assert (iWhere == CLP_ABOVE_UPPER);
      // going feasible
      iWhere = CLP_FEASIBLE;
      rhs += model_->lowerRegion()[sequence] - bound_[sequence];
    }
    setCurrentStatus(status_[sequence], iWhere);
    returnValue = fabs(alpha) * infeasibilityWeight_;
    return returnValue;
  }
  //@}
  
  
  /**@name Gets and sets */
  //@{
  /// Number of infeasibilities
  inline int numberInfeasibilities() const {
    return numberInfeasibilities_;
  }
  /// Change in cost
  inline double changeInCost() const {
    return changeCost_;
  }
  /// Feasible cost
  inline double feasibleCost() const {
    return feasibleCost_;
  }
  /// Feasible cost with offset and direction (i.e. for reporting)
  double feasibleReportCost() const;
  /// Sum of infeasibilities
  inline double sumInfeasibilities() const {
    return sumInfeasibilities_;
  }
  /// Largest infeasibility
  inline double largestInfeasibility() const {
    return largestInfeasibility_;
  }
  /// Average theta
  inline double averageTheta() const {
    return averageTheta_;
  }
  inline void setAverageTheta(double value) {
    averageTheta_ = value;
  }
  inline void setChangeInCost(double value) {
    changeCost_ = value;
  }
  //@}
  ///@name Private functions to deal with infeasible regions
  inline unsigned char * statusArray() const {
    return status_;
  }
  inline int getCurrentStatus(int sequence)
  {return (status_[sequence] >> 4);}
  /// For debug
  void validate();
  //@}
  
private:
  /**@name Data members */
  //@{
  /// Change in cost because of infeasibilities
  double changeCost_;
  /// Feasible cost
  double feasibleCost_;
  /// Current infeasibility weight
  double infeasibilityWeight_;
  /// Largest infeasibility
  double largestInfeasibility_;
  /// Sum of infeasibilities
  double sumInfeasibilities_;
  /// Average theta - kept here as only for primal
  double averageTheta_;
  /// Number of rows (mainly for checking and copy)
  int numberRows_;
  /// Number of columns (mainly for checking and copy)
  int numberColumns_;
  /// Model
  AbcSimplex * model_;
  /// Number of infeasibilities found
  int numberInfeasibilities_;
  // new stuff
  /// Contains status at beginning and current
  unsigned char * status_;
  /// Bound which has been replaced in lower_ or upper_
  double * bound_;
  /// Feasible cost array
  double * cost_;
  //@}
};

#endif
