ClpPrimalColumnSteepest.hpp

/* $Id$ */
// Copyright (C) 2002, International Business Machines
// Corporation and others.  All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).

#ifndef ClpPrimalColumnSteepest_H
#define ClpPrimalColumnSteepest_H

#include "ClpPrimalColumnPivot.hpp"
#include <bitset>

//#############################################################################
class CoinIndexedVector;

/** Primal Column Pivot Steepest Edge Algorithm Class

See Forrest-Goldfarb paper for algorithm

*/

class ClpPrimalColumnSteepest : public ClpPrimalColumnPivot {

public:
  ///@name Algorithmic methods
  //@{

  /** Returns pivot column, -1 if none.
         The Packed CoinIndexedVector updates has cost updates - for normal LP
         that is just +-weight where a feasibility changed.  It also has
         reduced cost from last iteration in pivot row
         Parts of operation split out into separate functions for
         profiling and speed
     */
  virtual int pivotColumn(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow1,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// For quadratic or funny nonlinearities
  int pivotColumnOldMethod(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow1,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Just update djs
  void justDjs(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Update djs doing partial pricing (dantzig)
  int partialPricing(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    int numberWanted,
    int numberLook);
  /// Update djs, weights for Devex using djs
  void djsAndDevex(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /** Update djs, weights for Steepest using djs
	 sets best sequence (possibly) */
  void djsAndSteepest(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Update djs, weights for Devex using pivot row
  void djsAndDevex2(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Update djs, weights for Steepest using pivot row
  void djsAndSteepest2(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Update weights for Devex
  void justDevex(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Update weights for Steepest
  void justSteepest(CoinIndexedVector *updates,
    CoinIndexedVector *spareRow2,
    CoinIndexedVector *spareColumn1,
    CoinIndexedVector *spareColumn2);
  /// Updates two arrays for steepest
  int transposeTimes2(const CoinIndexedVector *pi1, CoinIndexedVector *dj1,
    const CoinIndexedVector *pi2, CoinIndexedVector *dj2,
    CoinIndexedVector *spare, double scaleFactor);

  /// Updates weights - part 1 - also checks accuracy
  virtual void updateWeights(CoinIndexedVector *input);

  /// Checks accuracy - just for debug
  void checkAccuracy(int sequence, double relativeTolerance,
    CoinIndexedVector *rowArray1,
    CoinIndexedVector *rowArray2);

  /// Initialize weights
  void initializeWeights();

  /** Save weights - this may initialize weights as well
         mode is -
         1) before factorization
         2) after factorization
         3) just redo infeasibilities
         4) restore weights
         5) at end of values pass (so need initialization)
     */
  virtual void saveWeights(ClpSimplex *model, int mode);
  /// redo infeasibilities
  void redoInfeasibilities();
  /// Gets rid of last update
  virtual void unrollWeights();
  /// Gets rid of all arrays
  virtual void clearArrays();
  /// Returns true if would not find any column
  virtual bool looksOptimal() const;
  /// Called when maximum pivots changes
  virtual void maximumPivotsChanged();
  //@}

  /**@name gets and sets */
  //@{
  /// Mode
  inline int mode() const
  {
    return mode_;
  }
  /// Set mode
  inline void setMode(int mode)
  {
    mode_ = mode;
  }
  /// square of infeasibility array (just for infeasible columns)
  inline CoinIndexedVector *infeasible() const
  {
    return infeasible_;
  }
  /// Weights
  inline const double *weights() const
  {
    return weights_;
  }
  /// alternate weight array
  inline CoinIndexedVector *alternateWeights() const
  {
    return alternateWeights_;
  }
  /** Returns number of extra columns for sprint algorithm - 0 means off.
         Also number of iterations before recompute
     */
  virtual int numberSprintColumns(int &numberIterations) const;
  /// Switch off sprint idea
  virtual void switchOffSprint();

  //@}

  /** enums for persistence
     */
  enum Persistence {
    normal = 0x00, // create (if necessary) and destroy
    keep = 0x01 // create (if necessary) and leave
  };

  ///@name Constructors and destructors
  //@{
  /** Default Constructor
         0 is exact devex, 1 full steepest, 2 is partial exact devex
         3 switches between 0 and 2 depending on factorization
         4 starts as partial dantzig/devex but then may switch between 0 and 2.
         By partial exact devex is meant that the weights are updated as normal
         but only part of the nonbasic variables are scanned.
         This can be faster on very easy problems.
     */
  ClpPrimalColumnSteepest(int mode = 3);

  /// Copy constructor
  ClpPrimalColumnSteepest(const ClpPrimalColumnSteepest &rhs);

  /// Assignment operator
  ClpPrimalColumnSteepest &operator=(const ClpPrimalColumnSteepest &rhs);

  /// Destructor
  virtual ~ClpPrimalColumnSteepest();

  /// Clone
  virtual ClpPrimalColumnPivot *clone(bool copyData = true) const;

  //@}

  ///@name Private functions to deal with devex
  /** reference would be faster using ClpSimplex's status_,
         but I prefer to keep modularity.
     */
  inline bool reference(int i) const
  {
    return ((reference_[i >> 5] >> (i & 31)) & 1) != 0;
  }
  inline void setReference(int i, bool trueFalse)
  {
    unsigned int &value = reference_[i >> 5];
    int bit = i & 31;
    if (trueFalse)
      value |= (1 << bit);
    else
      value &= ~(1 << bit);
  }
  /// Set/ get persistence
  inline void setPersistence(Persistence life)
  {
    persistence_ = life;
  }
  inline Persistence persistence() const
  {
    return persistence_;
  }

  //@}
  //---------------------------------------------------------------------------

protected:
  ///@name Protected member data
  // Update weight
  double devex_;
  /// weight array
  double *weights_;
  /// square of infeasibility array (just for infeasible columns)
  CoinIndexedVector *infeasible_;
  /// alternate weight array (so we can unroll)
  CoinIndexedVector *alternateWeights_;
  /// save weight array (so we can use checkpoint)
  double *savedWeights_;
  // Array for exact devex to say what is in reference framework
  unsigned int *reference_;
  /** Status
         0) Normal
         -1) Needs initialization
         1) Weights are stored by sequence number
     */
  int state_;
  /**
         0 is exact devex, 1 full steepest, 2 is partial exact devex
         3 switches between 0 and 2 depending on factorization
         4 starts as partial dantzig/devex but then may switch between 0 and 2.
         5 is always partial dantzig
         By partial exact devex is meant that the weights are updated as normal
         but only part of the nonbasic variables are scanned.
         This can be faster on very easy problems.

         New dubious option is >=10 which does mini-sprint

     */
  int mode_;
  /* Infeasibility state i.e. array of infeasibilities and sequenceIn-
	0 - correct
	1 - needs correcting
	2 - not known but column sequence has been chosen
     */
  int infeasibilitiesState_;
  /// Life of weights
  Persistence persistence_;
  /// Number of times switched from partial dantzig to 0/2
  int numberSwitched_;
  // This is pivot row (or pivot sequence round re-factorization)
  int pivotSequence_;
  // This is saved pivot sequence
  int savedPivotSequence_;
  // This is saved outgoing variable
  int savedSequenceOut_;
  // Iteration when last rectified
  int lastRectified_;
  // Size of factorization at invert (used to decide algorithm)
  int sizeFactorization_;
  //@}
};

#endif

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