// Copyright (C) 2002, International Business Machines
// Corporation and others.  All Rights Reserved.

/* 
   Authors
   
   John Forrest

 */
#ifndef ClpSimplexPrimal_H
#define ClpSimplexPrimal_H

#include "ClpSimplex.hpp"

/** This solves LPs using the primal simplex method

    It inherits from ClpSimplex.  It has no data of its own and 
    is never created - only cast from a ClpSimplex object at algorithm time. 

*/

class ClpSimplexPrimal : public ClpSimplex {

public:

  /**@name Description of algorithm */
  //@{
  /** Primal algorithm

      Method

     It tries to be a single phase approach with a weight of 1.0 being
     given to getting optimal and a weight of infeasibilityCost_ being
     given to getting primal feasible.  In this version I have tried to
     be clever in a stupid way.  The idea of fake bounds in dual
     seems to work so the primal analogue would be that of getting
     bounds on reduced costs (by a presolve approach) and using
     these for being above or below feasible region.  I decided to waste
     memory and keep these explicitly.  This allows for non-linear
     costs!  I have not tested non-linear costs but will be glad
     to do something if a reasonable example is provided.

     The code is designed to take advantage of sparsity so arrays are
     seldom zeroed out from scratch or gone over in their entirety.
     The only exception is a full scan to find incoming variable for 
     Dantzig row choice.  For steepest edge we keep an updated list 
     of dual infeasibilities (actually squares).  
     On easy problems we don't need full scan - just
     pick first reasonable.  This method has not been coded.

     One problem is how to tackle degeneracy and accuracy.  At present
     I am using the modification of costs which I put in OSL and which was
     extended by Gill et al.  I am still not sure whether we will also
     need explicit perturbation.

     The flow of primal is three while loops as follows:

     while (not finished) {

       while (not clean solution) {

          Factorize and/or clean up solution by changing bounds so
	  primal feasible.  If looks finished check fake primal bounds.
	  Repeat until status is iterating (-1) or finished (0,1,2)

       }

       while (status==-1) {

         Iterate until no pivot in or out or time to re-factorize.

         Flow is:

         choose pivot column (incoming variable).  if none then
	 we are primal feasible so looks as if done but we need to
	 break and check bounds etc.

	 Get pivot column in tableau

         Choose outgoing row.  If we don't find one then we look
	 primal unbounded so break and check bounds etc.  (Also the
	 pivot tolerance is larger after any iterations so that may be
	 reason)

         If we do find outgoing row, we may have to adjust costs to
	 keep going forwards (anti-degeneracy).  Check pivot will be stable
	 and if unstable throw away iteration and break to re-factorize.
	 If minor error re-factorize after iteration.

	 Update everything (this may involve changing bounds on 
	 variables to stay primal feasible.

       }

     }

     TODO's (or maybe not)

     At present we never check we are going forwards.  I overdid that in
     OSL so will try and make a last resort.

     Needs partial scan pivot in option.

     May need other anti-degeneracy measures, especially if we try and use
     loose tolerances as a way to solve in fewer iterations.

     I like idea of dynamic scaling.  This gives opportunity to decouple
     different implications of scaling for accuracy, iteration count and
     feasibility tolerance.

     for use of exotic parameter startFinishoptions see Clpsimplex.hpp
  */

  int primal(int ifValuesPass=0, int startFinishOptions=0);
  //@}

  /**@name For advanced users */
  //@{
  /// Do not change infeasibility cost and always say optimal
  void alwaysOptimal(bool onOff);
  bool alwaysOptimal() const;
  /** Normally outgoing variables can go out to slightly negative
      values (but within tolerance) - this is to help stability and
      and degeneracy.  This can be switched off
  */
  void exactOutgoing(bool onOff);
  bool exactOutgoing() const;
  //@}

  /**@name Functions used in primal */
  //@{
  /** This has the flow between re-factorizations

      Returns a code to say where decision to exit was made
      Problem status set to:

      -2 re-factorize
      -4 Looks optimal/infeasible
      -5 Looks unbounded
      +3 max iterations 
      
      valuesOption has original value of valuesPass
   */
  int whileIterating(int valuesOption); 

  /** Do last half of an iteration.  This is split out so people can
      force incoming variable.  If solveType_ is 2 then this may
      re-factorize while normally it would exit to re-factorize.
      Return codes
      Reasons to come out (normal mode/user mode):
      -1 normal
      -2 factorize now - good iteration/ NA
      -3 slight inaccuracy - refactorize - iteration done/ same but factor done
      -4 inaccuracy - refactorize - no iteration/ NA
      -5 something flagged - go round again/ pivot not possible
      +2 looks unbounded
      +3 max iterations (iteration done)

      With solveType_ ==2 this should
      Pivot in a variable and choose an outgoing one.  Assumes primal
      feasible - will not go through a bound.  Returns step length in theta
      Returns ray in ray_
  */
  int pivotResult(int ifValuesPass=0);


  /** The primals are updated by the given array.
      Returns number of infeasibilities.
      After rowArray will have cost changes for use next iteration
  */
  int updatePrimalsInPrimal(CoinIndexedVector * rowArray,
		  double theta,
		  double & objectiveChange,
			    int valuesPass);
  /** 
      Row array has pivot column
      This chooses pivot row.
      Rhs array is used for distance to next bound (for speed)
      For speed, we may need to go to a bucket approach when many
      variables go through bounds
      If valuesPass non-zero then compute dj for direction
  */
  void primalRow(CoinIndexedVector * rowArray,
		 CoinIndexedVector * rhsArray,
		 CoinIndexedVector * spareArray,
		 CoinIndexedVector * spareArray2,
		 int valuesPass);
  /** 
      Chooses primal pivot column
      updateArray has cost updates (also use pivotRow_ from last iteration)
      Would be faster with separate region to scan
      and will have this (with square of infeasibility) when steepest
      For easy problems we can just choose one of the first columns we look at
  */
  void primalColumn(CoinIndexedVector * updateArray,
		    CoinIndexedVector * spareRow1,
		    CoinIndexedVector * spareRow2,
		    CoinIndexedVector * spareColumn1,
		    CoinIndexedVector * spareColumn2);

  /** Checks if tentative optimal actually means unbounded in primal
      Returns -3 if not, 2 if is unbounded */
  int checkUnbounded(CoinIndexedVector * ray,CoinIndexedVector * spare,
		     double changeCost);
  /**  Refactorizes if necessary 
       Checks if finished.  Updates status.
       lastCleaned refers to iteration at which some objective/feasibility
       cleaning too place.

       type - 0 initial so set up save arrays etc
            - 1 normal -if good update save
	    - 2 restoring from saved 
       saveModel is normally NULL but may not be if doing Sprint
  */
  void statusOfProblemInPrimal(int & lastCleaned, int type,
			     ClpSimplexProgress * progress,
			       bool doFactorization,
			       int ifValuesPass,
			       ClpSimplex * saveModel=NULL);
  /// Perturbs problem (method depends on perturbation())
  void perturb(int type);
  /// Take off effect of perturbation and say whether to try dual
  bool unPerturb();
  /// Unflag all variables and return number unflagged
  int unflag();
  /** Get next superbasic -1 if none,
      Normal type is 1
      If type is 3 then initializes sorted list
      if 2 uses list.
  */
  int nextSuperBasic(int superBasicType,CoinIndexedVector * columnArray);

  /// Create primal ray
  void primalRay(CoinIndexedVector * rowArray);
  /// Clears all bits and clears rowArray[1] etc
  void clearAll();
  
  //@}
};
#endif