source: stable/1.7/Clp/src/ClpSimplex.hpp @ 1213

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for Lou Hafer to update Osi stable

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1// Copyright (C) 2002, International Business Machines
2// Corporation and others.  All Rights Reserved.
3
4/*
5   Authors
6 
7   John Forrest
8
9 */
10#ifndef ClpSimplex_H
11#define ClpSimplex_H
12
13#include <iostream>
14#include <cfloat>
15#include "ClpModel.hpp"
16#include "ClpMatrixBase.hpp"
17#include "ClpSolve.hpp"
18class ClpDualRowPivot;
19class ClpPrimalColumnPivot;
20class ClpFactorization;
21class CoinIndexedVector;
22class ClpNonLinearCost;
23class CoinModel;
24class OsiClpSolverInterface;
25class CoinWarmStartBasis;
26class ClpDisasterHandler;
27class ClpConstraint;
28class ClpNodeStuff;
29
30/** This solves LPs using the simplex method
31
32    It inherits from ClpModel and all its arrays are created at
33    algorithm time. Originally I tried to work with model arrays
34    but for simplicity of coding I changed to single arrays with
35    structural variables then row variables.  Some coding is still
36    based on old style and needs cleaning up.
37
38    For a description of algorithms:
39
40    for dual see ClpSimplexDual.hpp and at top of ClpSimplexDual.cpp
41    for primal see ClpSimplexPrimal.hpp and at top of ClpSimplexPrimal.cpp
42
43    There is an algorithm data member.  + for primal variations
44    and - for dual variations
45
46*/
47
48class ClpSimplex : public ClpModel {
49  friend void ClpSimplexUnitTest(const std::string & mpsDir);
50
51public:
52  /** enums for status of various sorts.
53      First 4 match CoinWarmStartBasis,
54      isFixed means fixed at lower bound and out of basis
55  */
56  enum Status {
57    isFree = 0x00,
58    basic = 0x01,
59    atUpperBound = 0x02,
60    atLowerBound = 0x03,
61    superBasic = 0x04,
62    isFixed = 0x05
63  };
64  // For Dual
65  enum FakeBound {
66    noFake = 0x00,
67    bothFake = 0x01,
68    upperFake = 0x02,
69    lowerFake = 0x03
70  };
71
72  /**@name Constructors and destructor and copy */
73  //@{
74  /// Default constructor
75    ClpSimplex (bool emptyMessages = false  );
76
77  /** Copy constructor. May scale depending on mode
78      -1 leave mode as is
79      0 -off, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic(later)
80  */
81  ClpSimplex(const ClpSimplex & rhs, int scalingMode =-1);
82  /** Copy constructor from model. May scale depending on mode
83      -1 leave mode as is
84      0 -off, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic(later)
85  */
86  ClpSimplex(const ClpModel & rhs, int scalingMode=-1);
87  /** Subproblem constructor.  A subset of whole model is created from the
88      row and column lists given.  The new order is given by list order and
89      duplicates are allowed.  Name and integer information can be dropped
90      Can optionally modify rhs to take into account variables NOT in list
91      in this case duplicates are not allowed (also see getbackSolution)
92  */
93  ClpSimplex (const ClpModel * wholeModel,
94              int numberRows, const int * whichRows,
95              int numberColumns, const int * whichColumns,
96              bool dropNames=true, bool dropIntegers=true,
97              bool fixOthers=false);
98  /** Subproblem constructor.  A subset of whole model is created from the
99      row and column lists given.  The new order is given by list order and
100      duplicates are allowed.  Name and integer information can be dropped
101      Can optionally modify rhs to take into account variables NOT in list
102      in this case duplicates are not allowed (also see getbackSolution)
103  */
104  ClpSimplex (const ClpSimplex * wholeModel,
105              int numberRows, const int * whichRows,
106              int numberColumns, const int * whichColumns,
107              bool dropNames=true, bool dropIntegers=true,
108              bool fixOthers=false);
109  /** This constructor modifies original ClpSimplex and stores
110      original stuff in created ClpSimplex.  It is only to be used in
111      conjunction with originalModel */
112  ClpSimplex (ClpSimplex * wholeModel,
113              int numberColumns, const int * whichColumns);
114  /** This copies back stuff from miniModel and then deletes miniModel.
115      Only to be used with mini constructor */
116  void originalModel(ClpSimplex * miniModel);
117  /** Array persistence flag
118      If 0 then as now (delete/new)
119      1 then only do arrays if bigger needed
120      2 as 1 but give a bit extra if bigger needed
121  */
122  void setPersistenceFlag(int value);
123  /**
124     If you are re-using the same matrix again and again then the setup time
125     to do scaling may be significant.  Also you may not want to initialize all values
126     or return all values (especially if infeasible).  While an auxiliary model exists
127     it will be faster.  If options -1 then model is switched off.  Otherwise switched on
128     with following options.
129     1 - rhs is constant
130     2 - bounds are constant
131     4 - objective is constant
132     8 - solution in by basis and no djs etc in
133     16 - no duals out (but reduced costs)
134     32 - no output if infeasible
135  */
136  void auxiliaryModel(int options);
137  /// Switch off e.g. if people using presolve
138  void deleteAuxiliaryModel();
139  /// See if we have auxiliary model
140  inline bool usingAuxiliaryModel() const
141  { return auxiliaryModel_!=NULL;}
142  /// Save a copy of model with certain state - normally without cuts
143  void makeBaseModel();
144  /// Switch off base model
145  void deleteBaseModel();
146  /// See if we have base model
147  inline ClpSimplex *  baseModel() const
148  { return baseModel_;}
149  /** Reset to base model (just size and arrays needed)
150      If model NULL use internal copy
151  */
152  void setToBaseModel(ClpSimplex * model=NULL);
153  /// Assignment operator. This copies the data
154    ClpSimplex & operator=(const ClpSimplex & rhs);
155  /// Destructor
156   ~ClpSimplex (  );
157  // Ones below are just ClpModel with some changes
158  /** Loads a problem (the constraints on the
159        rows are given by lower and upper bounds). If a pointer is 0 then the
160        following values are the default:
161        <ul>
162          <li> <code>colub</code>: all columns have upper bound infinity
163          <li> <code>collb</code>: all columns have lower bound 0
164          <li> <code>rowub</code>: all rows have upper bound infinity
165          <li> <code>rowlb</code>: all rows have lower bound -infinity
166          <li> <code>obj</code>: all variables have 0 objective coefficient
167        </ul>
168    */
169  void loadProblem (  const ClpMatrixBase& matrix,
170                     const double* collb, const double* colub,   
171                     const double* obj,
172                     const double* rowlb, const double* rowub,
173                      const double * rowObjective=NULL);
174  void loadProblem (  const CoinPackedMatrix& matrix,
175                     const double* collb, const double* colub,   
176                     const double* obj,
177                     const double* rowlb, const double* rowub,
178                      const double * rowObjective=NULL);
179
180  /** Just like the other loadProblem() method except that the matrix is
181        given in a standard column major ordered format (without gaps). */
182  void loadProblem (  const int numcols, const int numrows,
183                     const CoinBigIndex* start, const int* index,
184                     const double* value,
185                     const double* collb, const double* colub,   
186                     const double* obj,
187                      const double* rowlb, const double* rowub,
188                      const double * rowObjective=NULL);
189  /// This one is for after presolve to save memory
190  void loadProblem (  const int numcols, const int numrows,
191                     const CoinBigIndex* start, const int* index,
192                      const double* value,const int * length,
193                     const double* collb, const double* colub,   
194                     const double* obj,
195                      const double* rowlb, const double* rowub,
196                      const double * rowObjective=NULL);
197  /** This loads a model from a coinModel object - returns number of errors.
198      If keepSolution true and size is same as current then
199      keeps current status and solution
200  */
201  int loadProblem (  CoinModel & modelObject,bool keepSolution=false);
202  /// Read an mps file from the given filename
203  int readMps(const char *filename,
204              bool keepNames=false,
205              bool ignoreErrors = false);
206  /// Read GMPL files from the given filenames
207  int readGMPL(const char *filename,const char * dataName,
208               bool keepNames=false);
209  /// Read file in LP format from file with name filename.
210  /// See class CoinLpIO for description of this format.
211  int readLp(const char *filename, const double epsilon = 1e-5);
212  /** Borrow model.  This is so we dont have to copy large amounts
213      of data around.  It assumes a derived class wants to overwrite
214      an empty model with a real one - while it does an algorithm.
215      This is same as ClpModel one, but sets scaling on etc. */
216  void borrowModel(ClpModel & otherModel);
217  void borrowModel(ClpSimplex & otherModel);
218   /// Pass in Event handler (cloned and deleted at end)
219   void passInEventHandler(const ClpEventHandler * eventHandler);
220  /// Puts solution back into small model
221  void getbackSolution(const ClpSimplex & smallModel,const int * whichRow, const int * whichColumn);
222  /** Load nonlinear part of problem from AMPL info
223      Returns 0 if linear
224      1 if quadratic objective
225      2 if quadratic constraints
226      3 if nonlinear objective
227      4 if nonlinear constraints
228      -1 on failure
229  */
230  int loadNonLinear(void * info, int & numberConstraints, 
231                    ClpConstraint ** & constraints);
232  //@}
233
234  /**@name Functions most useful to user */
235  //@{
236  /** General solve algorithm which can do presolve.
237      See  ClpSolve.hpp for options
238   */
239  int initialSolve(ClpSolve & options);
240  /// Default initial solve
241  int initialSolve();
242  /// Dual initial solve
243  int initialDualSolve();
244  /// Primal initial solve
245  int initialPrimalSolve();
246 /// Barrier initial solve
247  int initialBarrierSolve();
248  /// Barrier initial solve, not to be followed by crossover
249  int initialBarrierNoCrossSolve();
250  /** Dual algorithm - see ClpSimplexDual.hpp for method.
251      ifValuesPass==2 just does values pass and then stops.
252
253      startFinishOptions - bits
254      1 - do not delete work areas and factorization at end
255      2 - use old factorization if same number of rows
256      4 - skip as much initialization of work areas as possible
257          (based on whatsChanged in clpmodel.hpp) ** work in progress
258      maybe other bits later
259  */
260  int dual(int ifValuesPass=0, int startFinishOptions=0);
261  // If using Debug
262  int dualDebug(int ifValuesPass=0, int startFinishOptions=0);
263  /** Primal algorithm - see ClpSimplexPrimal.hpp for method.
264      ifValuesPass==2 just does values pass and then stops.
265
266      startFinishOptions - bits
267      1 - do not delete work areas and factorization at end
268      2 - use old factorization if same number of rows
269      4 - skip as much initialization of work areas as possible
270          (based on whatsChanged in clpmodel.hpp) ** work in progress
271      maybe other bits later
272  */
273  int primal(int ifValuesPass=0, int startFinishOptions=0);
274  /** Solves nonlinear problem using SLP - may be used as crash
275      for other algorithms when number of iterations small.
276      Also exits if all problematical variables are changing
277      less than deltaTolerance
278  */
279  int nonlinearSLP(int numberPasses,double deltaTolerance);
280  /** Solves problem with nonlinear constraints using SLP - may be used as crash
281      for other algorithms when number of iterations small.
282      Also exits if all problematical variables are changing
283      less than deltaTolerance
284  */
285  int nonlinearSLP(int numberConstraints, ClpConstraint ** constraints,
286                   int numberPasses,double deltaTolerance);
287  /** Solves using barrier (assumes you have good cholesky factor code).
288      Does crossover to simplex if asked*/
289  int barrier(bool crossover=true);
290  /** Solves non-linear using reduced gradient.  Phase = 0 get feasible,
291      =1 use solution */
292  int reducedGradient(int phase=0);
293  /**
294     When scaling is on it is possible that the scaled problem
295     is feasible but the unscaled is not.  Clp returns a secondary
296     status code to that effect.  This option allows for a cleanup.
297     If you use it I would suggest 1.
298     This only affects actions when scaled optimal
299     0 - no action
300     1 - clean up using dual if primal infeasibility
301     2 - clean up using dual if dual infeasibility
302     3 - clean up using dual if primal or dual infeasibility
303     11,12,13 - as 1,2,3 but use primal
304
305     return code as dual/primal
306  */
307  int cleanup(int cleanupScaling);
308  /** Dual ranging.
309      This computes increase/decrease in cost for each given variable and corresponding
310      sequence numbers which would change basis.  Sequence numbers are 0..numberColumns
311      and numberColumns.. for artificials/slacks.
312      For non-basic variables the information is trivial to compute and the change in cost is just minus the
313      reduced cost and the sequence number will be that of the non-basic variables.
314      For basic variables a ratio test is between the reduced costs for non-basic variables
315      and the row of the tableau corresponding to the basic variable.
316      The increase/decrease value is always >= 0.0
317
318      Up to user to provide correct length arrays where each array is of length numberCheck.
319      which contains list of variables for which information is desired.  All other
320      arrays will be filled in by function.  If fifth entry in which is variable 7 then fifth entry in output arrays
321      will be information for variable 7.
322
323      If valueIncrease/Decrease not NULL (both must be NULL or both non NULL) then these are filled with
324      the value of variable if such a change in cost were made (the existing bounds are ignored)
325
326      Returns non-zero if infeasible unbounded etc
327  */
328  int dualRanging(int numberCheck,const int * which,
329                  double * costIncrease, int * sequenceIncrease,
330                  double * costDecrease, int * sequenceDecrease,
331                  double * valueIncrease=NULL, double * valueDecrease=NULL);
332  /** Primal ranging.
333      This computes increase/decrease in value for each given variable and corresponding
334      sequence numbers which would change basis.  Sequence numbers are 0..numberColumns
335      and numberColumns.. for artificials/slacks.
336      This should only be used for non-basic variabls as otherwise information is pretty useless
337      For basic variables the sequence number will be that of the basic variables.
338
339      Up to user to provide correct length arrays where each array is of length numberCheck.
340      which contains list of variables for which information is desired.  All other
341      arrays will be filled in by function.  If fifth entry in which is variable 7 then fifth entry in output arrays
342      will be information for variable 7.
343
344      Returns non-zero if infeasible unbounded etc
345  */
346  int primalRanging(int numberCheck,const int * which,
347                  double * valueIncrease, int * sequenceIncrease,
348                  double * valueDecrease, int * sequenceDecrease);
349  /** Write the basis in MPS format to the specified file.
350      If writeValues true writes values of structurals
351      (and adds VALUES to end of NAME card)
352     
353      Row and column names may be null.
354      formatType is
355      <ul>
356      <li> 0 - normal
357      <li> 1 - extra accuracy
358      <li> 2 - IEEE hex (later)
359      </ul>
360     
361      Returns non-zero on I/O error
362  */
363  int writeBasis(const char *filename,
364                 bool writeValues=false,
365                 int formatType=0) const;
366  /** Read a basis from the given filename,
367      returns -1 on file error, 0 if no values, 1 if values */
368  int readBasis(const char *filename);
369  /// Returns a basis (to be deleted by user)
370  CoinWarmStartBasis * getBasis() const;
371  /// Passes in factorization
372  void setFactorization( ClpFactorization & factorization);
373  /** Tightens primal bounds to make dual faster.  Unless
374      fixed or doTight>10, bounds are slightly looser than they could be.
375      This is to make dual go faster and is probably not needed
376      with a presolve.  Returns non-zero if problem infeasible.
377
378      Fudge for branch and bound - put bounds on columns of factor *
379      largest value (at continuous) - should improve stability
380      in branch and bound on infeasible branches (0.0 is off)
381  */
382  int tightenPrimalBounds(double factor=0.0,int doTight=0,bool tightIntegers=false);
383  /** Crash - at present just aimed at dual, returns
384      -2 if dual preferred and crash basis created
385      -1 if dual preferred and all slack basis preferred
386       0 if basis going in was not all slack
387       1 if primal preferred and all slack basis preferred
388       2 if primal preferred and crash basis created.
389       
390       if gap between bounds <="gap" variables can be flipped
391       ( If pivot -1 then can be made super basic!)
392
393       If "pivot" is
394       -1 No pivoting - always primal
395       0 No pivoting (so will just be choice of algorithm)
396       1 Simple pivoting e.g. gub
397       2 Mini iterations
398  */
399  int crash(double gap,int pivot);
400  /// Sets row pivot choice algorithm in dual
401  void setDualRowPivotAlgorithm(ClpDualRowPivot & choice);
402  /// Sets column pivot choice algorithm in primal
403  void setPrimalColumnPivotAlgorithm(ClpPrimalColumnPivot & choice);
404  /** For strong branching.  On input lower and upper are new bounds
405      while on output they are change in objective function values
406      (>1.0e50 infeasible).
407      Return code is 0 if nothing interesting, -1 if infeasible both
408      ways and +1 if infeasible one way (check values to see which one(s))
409      Solutions are filled in as well - even down, odd up - also
410      status and number of iterations
411  */
412  int strongBranching(int numberVariables,const int * variables,
413                      double * newLower, double * newUpper,
414                      double ** outputSolution,
415                      int * outputStatus, int * outputIterations,
416                      bool stopOnFirstInfeasible=true,
417                      bool alwaysFinish=false,
418                      int startFinishOptions=0);
419  /// Starts Fast dual2
420  int startFastDual2(ClpNodeStuff * stuff);
421  /// Like Fast dual
422  int fastDual2(ClpNodeStuff * stuff);
423  /// Stops Fast dual2
424  void stopFastDual2(ClpNodeStuff * stuff);
425  //@}
426
427  /**@name Needed for functionality of OsiSimplexInterface */
428  //@{
429  /** Pivot in a variable and out a variable.  Returns 0 if okay,
430      1 if inaccuracy forced re-factorization, -1 if would be singular.
431      Also updates primal/dual infeasibilities.
432      Assumes sequenceIn_ and pivotRow_ set and also directionIn and Out.
433  */
434  int pivot();
435
436  /** Pivot in a variable and choose an outgoing one.  Assumes primal
437      feasible - will not go through a bound.  Returns step length in theta
438      Returns ray in ray_ (or NULL if no pivot)
439      Return codes as before but -1 means no acceptable pivot
440  */
441  int primalPivotResult();
442 
443  /** Pivot out a variable and choose an incoing one.  Assumes dual
444      feasible - will not go through a reduced cost. 
445      Returns step length in theta
446      Returns ray in ray_ (or NULL if no pivot)
447      Return codes as before but -1 means no acceptable pivot
448  */
449  int dualPivotResult();
450
451  /** Common bits of coding for dual and primal.  Return 0 if okay,
452      1 if bad matrix, 2 if very bad factorization
453
454      startFinishOptions - bits
455      1 - do not delete work areas and factorization at end
456      2 - use old factorization if same number of rows
457      4 - skip as much initialization of work areas as possible
458          (based on whatsChanged in clpmodel.hpp) ** work in progress
459      maybe other bits later
460     
461  */
462  int startup(int ifValuesPass,int startFinishOptions=0);
463  void finish(int startFinishOptions=0);
464 
465  /** Factorizes and returns true if optimal.  Used by user */
466  bool statusOfProblem(bool initial=false);
467  /// If user left factorization frequency then compute
468  void defaultFactorizationFrequency();
469  //@}
470
471  /**@name most useful gets and sets */
472  //@{
473  /// If problem is primal feasible
474  inline bool primalFeasible() const
475         { return (numberPrimalInfeasibilities_==0);}
476  /// If problem is dual feasible
477  inline bool dualFeasible() const
478         { return (numberDualInfeasibilities_==0);}
479  /// factorization
480  inline ClpFactorization * factorization() const 
481          { return factorization_;}
482  /// Sparsity on or off
483  bool sparseFactorization() const;
484  void setSparseFactorization(bool value);
485  /// Factorization frequency
486  int factorizationFrequency() const;
487  void setFactorizationFrequency(int value);
488  /// Dual bound
489  inline double dualBound() const
490          { return dualBound_;}
491  void setDualBound(double value);
492  /// Infeasibility cost
493  inline double infeasibilityCost() const
494          { return infeasibilityCost_;}
495  void setInfeasibilityCost(double value);
496  /** Amount of print out:
497      0 - none
498      1 - just final
499      2 - just factorizations
500      3 - as 2 plus a bit more
501      4 - verbose
502      above that 8,16,32 etc just for selective debug
503  */
504  /** Perturbation:
505      50  - switch on perturbation
506      100 - auto perturb if takes too long (1.0e-6 largest nonzero)
507      101 - we are perturbed
508      102 - don't try perturbing again
509      default is 100
510      others are for playing
511  */
512  inline int perturbation() const
513    { return perturbation_;}
514  void setPerturbation(int value);
515  /// Current (or last) algorithm
516  inline int algorithm() const 
517  {return algorithm_; } 
518  /// Set algorithm
519  inline void setAlgorithm(int value)
520  {algorithm_=value; } 
521  /// Sum of dual infeasibilities
522  inline double sumDualInfeasibilities() const 
523          { return sumDualInfeasibilities_;} 
524  inline void setSumDualInfeasibilities(double value)
525          { sumDualInfeasibilities_=value;} 
526  /// Sum of relaxed dual infeasibilities
527  inline double sumOfRelaxedDualInfeasibilities() const 
528          { return sumOfRelaxedDualInfeasibilities_;} 
529  inline void setSumOfRelaxedDualInfeasibilities(double value)
530          { sumOfRelaxedDualInfeasibilities_=value;} 
531  /// Number of dual infeasibilities
532  inline int numberDualInfeasibilities() const 
533          { return numberDualInfeasibilities_;} 
534  inline void setNumberDualInfeasibilities(int value)
535          { numberDualInfeasibilities_=value;} 
536  /// Number of dual infeasibilities (without free)
537  inline int numberDualInfeasibilitiesWithoutFree() const 
538          { return numberDualInfeasibilitiesWithoutFree_;} 
539  /// Sum of primal infeasibilities
540  inline double sumPrimalInfeasibilities() const 
541          { return sumPrimalInfeasibilities_;} 
542  inline void setSumPrimalInfeasibilities(double value)
543          { sumPrimalInfeasibilities_=value;} 
544  /// Sum of relaxed primal infeasibilities
545  inline double sumOfRelaxedPrimalInfeasibilities() const 
546          { return sumOfRelaxedPrimalInfeasibilities_;} 
547  inline void setSumOfRelaxedPrimalInfeasibilities(double value)
548          { sumOfRelaxedPrimalInfeasibilities_=value;} 
549  /// Number of primal infeasibilities
550  inline int numberPrimalInfeasibilities() const 
551          { return numberPrimalInfeasibilities_;} 
552  inline void setNumberPrimalInfeasibilities(int value)
553          { numberPrimalInfeasibilities_=value;} 
554  /** Save model to file, returns 0 if success.  This is designed for
555      use outside algorithms so does not save iterating arrays etc.
556  It does not save any messaging information.
557  Does not save scaling values.
558  It does not know about all types of virtual functions.
559  */
560  int saveModel(const char * fileName);
561  /** Restore model from file, returns 0 if success,
562      deletes current model */
563  int restoreModel(const char * fileName);
564 
565  /** Just check solution (for external use) - sets sum of
566      infeasibilities etc.
567      If setToBounds 0 then primal column values not changed
568      and used to compute primal row activity values.  If 1 or 2
569      then status used - so all nonbasic variables set to
570      indicated bound and if any values changed (or ==2)  basic values re-computed.
571  */
572  void checkSolution(int setToBounds=false);
573  /** Just check solution (for internal use) - sets sum of
574      infeasibilities etc. */
575  void checkSolutionInternal();
576  /// Useful row length arrays (0,1,2,3,4,5)
577  inline CoinIndexedVector * rowArray(int index) const
578  { return rowArray_[index];}
579  /// Useful column length arrays (0,1,2,3,4,5)
580  inline CoinIndexedVector * columnArray(int index) const
581  { return columnArray_[index];}
582  //@}
583
584  /******************** End of most useful part **************/
585  /**@name Functions less likely to be useful to casual user */
586  //@{
587  /** Given an existing factorization computes and checks
588      primal and dual solutions.  Uses input arrays for variables at
589      bounds.  Returns feasibility states */
590  int getSolution (  const double * rowActivities,
591                     const double * columnActivities);
592  /** Given an existing factorization computes and checks
593      primal and dual solutions.  Uses current problem arrays for
594      bounds.  Returns feasibility states */
595  int getSolution ();
596  /** Constructs a non linear cost from list of non-linearities (columns only)
597      First lower of each column is taken as real lower
598      Last lower is taken as real upper and cost ignored
599
600      Returns nonzero if bad data e.g. lowers not monotonic
601  */
602  int createPiecewiseLinearCosts(const int * starts,
603                   const double * lower, const double * gradient);
604  /// dual row pivot choice
605  ClpDualRowPivot * dualRowPivot() const
606  { return dualRowPivot_;}
607  /** Return model - updates any scalars */
608  void returnModel(ClpSimplex & otherModel);
609  /** Factorizes using current basis. 
610      solveType - 1 iterating, 0 initial, -1 external
611      If 10 added then in primal values pass
612      Return codes are as from ClpFactorization unless initial factorization
613      when total number of singularities is returned.
614      Special case is numberRows_+1 -> all slack basis.
615  */
616  int internalFactorize(int solveType);
617  /// Save data
618  ClpDataSave saveData() ;
619  /// Restore data
620  void restoreData(ClpDataSave saved);
621  /// Clean up status
622  void cleanStatus();
623  /// Factorizes using current basis. For external use
624  int factorize();
625  /** Computes duals from scratch. If givenDjs then
626      allows for nonzero basic djs */
627  void computeDuals(double * givenDjs);
628  /// Computes primals from scratch
629  void computePrimals (  const double * rowActivities,
630                     const double * columnActivities);
631  /** Adds multiple of a column into an array */
632  void add(double * array,
633                   int column, double multiplier) const;
634  /**
635     Unpacks one column of the matrix into indexed array
636     Uses sequenceIn_
637     Also applies scaling if needed
638  */
639  void unpack(CoinIndexedVector * rowArray) const ;
640  /**
641     Unpacks one column of the matrix into indexed array
642     Slack if sequence>= numberColumns
643     Also applies scaling if needed
644  */
645  void unpack(CoinIndexedVector * rowArray,int sequence) const;
646  /**
647     Unpacks one column of the matrix into indexed array
648     ** as packed vector
649     Uses sequenceIn_
650     Also applies scaling if needed
651  */
652  void unpackPacked(CoinIndexedVector * rowArray) ;
653  /**
654     Unpacks one column of the matrix into indexed array
655     ** as packed vector
656     Slack if sequence>= numberColumns
657     Also applies scaling if needed
658  */
659  void unpackPacked(CoinIndexedVector * rowArray,int sequence);
660protected: 
661  /**
662      This does basis housekeeping and does values for in/out variables.
663      Can also decide to re-factorize
664  */
665  int housekeeping(double objectiveChange);
666  /** This sets largest infeasibility and most infeasible and sum
667      and number of infeasibilities (Primal) */
668  void checkPrimalSolution(const double * rowActivities=NULL,
669                           const double * columnActivies=NULL);
670  /** This sets largest infeasibility and most infeasible and sum
671      and number of infeasibilities (Dual) */
672  void checkDualSolution();
673  /** This sets sum and number of infeasibilities (Dual and Primal) */
674  void checkBothSolutions();
675public:
676  /** For advanced use.  When doing iterative solves things can get
677      nasty so on values pass if incoming solution has largest
678      infeasibility < incomingInfeasibility throw out variables
679      from basis until largest infeasibility < allowedInfeasibility
680      or incoming largest infeasibility.
681      If allowedInfeasibility>= incomingInfeasibility this is
682      always possible altough you may end up with an all slack basis.
683
684      Defaults are 1.0,10.0
685  */
686  void setValuesPassAction(double incomingInfeasibility,
687                           double allowedInfeasibility);
688  //@}
689  /**@name most useful gets and sets */
690  //@{
691public: 
692  /// Initial value for alpha accuracy calculation (-1.0 off)
693  inline double alphaAccuracy() const
694          { return alphaAccuracy_;} 
695  inline void setAlphaAccuracy(double value)
696          { alphaAccuracy_ = value;} 
697public:
698  /// Disaster handler
699  inline void setDisasterHandler(ClpDisasterHandler * handler)
700  { disasterArea_= handler;}
701  /// Large bound value (for complementarity etc)
702  inline double largeValue() const 
703          { return largeValue_;} 
704  void setLargeValue( double value) ;
705  /// Largest error on Ax-b
706  inline double largestPrimalError() const
707          { return largestPrimalError_;} 
708  /// Largest error on basic duals
709  inline double largestDualError() const
710          { return largestDualError_;} 
711  /// Largest error on Ax-b
712  inline void setLargestPrimalError(double value)
713          { largestPrimalError_=value;} 
714  /// Largest error on basic duals
715  inline void setLargestDualError(double value)
716          { largestDualError_=value;} 
717  /// Basic variables pivoting on which rows
718  inline int * pivotVariable() const
719          { return pivotVariable_;}
720  /// If automatic scaling on
721  inline bool automaticScaling() const
722  { return automaticScale_!=0;}
723  inline void setAutomaticScaling(bool onOff)
724  { automaticScale_ = onOff ? 1: 0;} 
725  /// Current dual tolerance
726  inline double currentDualTolerance() const 
727          { return dualTolerance_;} 
728  inline void setCurrentDualTolerance(double value)
729          { dualTolerance_ = value;} 
730  /// Current primal tolerance
731  inline double currentPrimalTolerance() const 
732          { return primalTolerance_;} 
733  inline void setCurrentPrimalTolerance(double value)
734          { primalTolerance_ = value;} 
735  /// How many iterative refinements to do
736  inline int numberRefinements() const 
737          { return numberRefinements_;} 
738  void setNumberRefinements( int value) ;
739  /// Alpha (pivot element) for use by classes e.g. steepestedge
740  inline double alpha() const { return alpha_;}
741  inline void setAlpha(double value) { alpha_ = value;}
742  /// Reduced cost of last incoming for use by classes e.g. steepestedge
743  inline double dualIn() const { return dualIn_;}
744  /// Pivot Row for use by classes e.g. steepestedge
745  inline int pivotRow() const{ return pivotRow_;}
746  inline void setPivotRow(int value) { pivotRow_=value;}
747  /// value of incoming variable (in Dual)
748  double valueIncomingDual() const;
749  //@}
750
751  protected:
752  /**@name protected methods */
753  //@{
754  /** May change basis and then returns number changed.
755      Computation of solutions may be overriden by given pi and solution
756  */
757  int gutsOfSolution ( double * givenDuals,
758                       const double * givenPrimals,
759                       bool valuesPass=false);
760  /// Does most of deletion (0 = all, 1 = most, 2 most + factorization)
761  void gutsOfDelete(int type);
762  /// Does most of copying
763  void gutsOfCopy(const ClpSimplex & rhs);
764  /** puts in format I like (rowLower,rowUpper) also see StandardMatrix
765      1 bit does rows (now and columns), (2 bit does column bounds), 4 bit does objective(s).
766      8 bit does solution scaling in
767      16 bit does rowArray and columnArray indexed vectors
768      and makes row copy if wanted, also sets columnStart_ etc
769      Also creates scaling arrays if needed.  It does scaling if needed.
770      16 also moves solutions etc in to work arrays
771      On 16 returns false if problem "bad" i.e. matrix or bounds bad
772      If startFinishOptions is -1 then called by user in getSolution
773      so do arrays but keep pivotVariable_
774  */
775  bool createRim(int what,bool makeRowCopy=false,int startFinishOptions=0);
776  /// Does rows and columns
777  void createRim1(bool initial);
778  /// Does objective
779  void createRim4(bool initial);
780  /// Does rows and columns and objective
781  void createRim5(bool initial);
782  /** releases above arrays and does solution scaling out.  May also
783      get rid of factorization data -
784      0 get rid of nothing, 1 get rid of arrays, 2 also factorization
785  */
786  void deleteRim(int getRidOfFactorizationData=2);
787  /// Sanity check on input rim data (after scaling) - returns true if okay
788  bool sanityCheck();
789  //@}
790  public:
791  /**@name public methods */
792  //@{
793  /** Return row or column sections - not as much needed as it
794      once was.  These just map into single arrays */
795  inline double * solutionRegion(int section) const
796  { if (!section) return rowActivityWork_; else return columnActivityWork_;}
797  inline double * djRegion(int section) const
798  { if (!section) return rowReducedCost_; else return reducedCostWork_;}
799  inline double * lowerRegion(int section) const
800  { if (!section) return rowLowerWork_; else return columnLowerWork_;}
801  inline double * upperRegion(int section) const
802  { if (!section) return rowUpperWork_; else return columnUpperWork_;}
803  inline double * costRegion(int section) const
804  { if (!section) return rowObjectiveWork_; else return objectiveWork_;}
805  /// Return region as single array
806  inline double * solutionRegion() const
807  { return solution_;}
808  inline double * djRegion() const
809  { return dj_;}
810  inline double * lowerRegion() const
811  { return lower_;}
812  inline double * upperRegion() const
813  { return upper_;}
814  inline double * costRegion() const
815  { return cost_;}
816  inline Status getStatus(int sequence) const
817  {return static_cast<Status> (status_[sequence]&7);}
818  inline void setStatus(int sequence, Status status)
819  {
820    unsigned char & st_byte = status_[sequence];
821    st_byte &= ~7;
822    st_byte |= status;
823  }
824  /// Start or reset using maximumRows_ and Columns_ - true if change
825  bool startPermanentArrays();
826  /** Normally the first factorization does sparse coding because
827      the factorization could be singular.  This allows initial dense
828      factorization when it is known to be safe
829  */
830  void setInitialDenseFactorization(bool onOff);
831  bool  initialDenseFactorization() const;
832  /** Return sequence In or Out */
833  inline int sequenceIn() const
834  {return sequenceIn_;}
835  inline int sequenceOut() const
836  {return sequenceOut_;}
837  /** Set sequenceIn or Out */
838  inline void  setSequenceIn(int sequence)
839  { sequenceIn_=sequence;}
840  inline void  setSequenceOut(int sequence)
841  { sequenceOut_=sequence;}
842  /** Return direction In or Out */
843  inline int directionIn() const
844  {return directionIn_;}
845  inline int directionOut() const
846  {return directionOut_;}
847  /** Set directionIn or Out */
848  inline void  setDirectionIn(int direction)
849  { directionIn_=direction;}
850  inline void  setDirectionOut(int direction)
851  { directionOut_=direction;}
852  /// Value of Out variable
853  inline double valueOut() const
854  { return valueOut_;}
855  /// Returns 1 if sequence indicates column
856  inline int isColumn(int sequence) const
857  { return sequence<numberColumns_ ? 1 : 0;}
858  /// Returns sequence number within section
859  inline int sequenceWithin(int sequence) const
860  { return sequence<numberColumns_ ? sequence : sequence-numberColumns_;}
861  /// Return row or column values
862  inline double solution(int sequence)
863  { return solution_[sequence];}
864  /// Return address of row or column values
865  inline double & solutionAddress(int sequence)
866  { return solution_[sequence];}
867  inline double reducedCost(int sequence)
868   { return dj_[sequence];}
869  inline double & reducedCostAddress(int sequence)
870   { return dj_[sequence];}
871  inline double lower(int sequence)
872  { return lower_[sequence];}
873  /// Return address of row or column lower bound
874  inline double & lowerAddress(int sequence)
875  { return lower_[sequence];}
876  inline double upper(int sequence)
877  { return upper_[sequence];}
878  /// Return address of row or column upper bound
879  inline double & upperAddress(int sequence)
880  { return upper_[sequence];}
881  inline double cost(int sequence)
882  { return cost_[sequence];}
883  /// Return address of row or column cost
884  inline double & costAddress(int sequence)
885  { return cost_[sequence];}
886  /// Return original lower bound
887  inline double originalLower(int iSequence) const
888  { if (iSequence<numberColumns_) return columnLower_[iSequence]; else
889    return rowLower_[iSequence-numberColumns_];}
890  /// Return original lower bound
891  inline double originalUpper(int iSequence) const
892  { if (iSequence<numberColumns_) return columnUpper_[iSequence]; else
893    return rowUpper_[iSequence-numberColumns_];}
894  /// Theta (pivot change)
895  inline double theta() const
896  { return theta_;}
897  /// Return pointer to details of costs
898  inline ClpNonLinearCost * nonLinearCost() const
899  { return nonLinearCost_;}
900  /** Return more special options
901      1 bit - if presolve says infeasible in ClpSolve return
902      2 bit - if presolved problem infeasible return
903      4 bit - keep arrays like upper_ around
904  */
905  inline int moreSpecialOptions() const
906  { return moreSpecialOptions_;}
907  /** Set more special options
908      1 bit - if presolve says infeasible in ClpSolve return
909      2 bit - if presolved problem infeasible return
910      4 bit - keep arrays like upper_ around
911  */
912  inline void setMoreSpecialOptions(int value)
913  { moreSpecialOptions_ = value;}
914  //@}
915  /**@name status methods */
916  //@{
917  inline void setFakeBound(int sequence, FakeBound fakeBound)
918  {
919    unsigned char & st_byte = status_[sequence];
920    st_byte &= ~24;
921    st_byte |= fakeBound<<3;
922  }
923  inline FakeBound getFakeBound(int sequence) const
924  {return static_cast<FakeBound> ((status_[sequence]>>3)&3);}
925  inline void setRowStatus(int sequence, Status status)
926  {
927    unsigned char & st_byte = status_[sequence+numberColumns_];
928    st_byte &= ~7;
929    st_byte |= status;
930  }
931  inline Status getRowStatus(int sequence) const
932  {return static_cast<Status> (status_[sequence+numberColumns_]&7);}
933  inline void setColumnStatus(int sequence, Status status)
934  {
935    unsigned char & st_byte = status_[sequence];
936    st_byte &= ~7;
937    st_byte |= status;
938  }
939  inline Status getColumnStatus(int sequence) const
940  {return static_cast<Status> (status_[sequence]&7);}
941  inline void setPivoted( int sequence)
942  { status_[sequence] |= 32;}
943  inline void clearPivoted( int sequence)
944  { status_[sequence] &= ~32; }
945  inline bool pivoted(int sequence) const
946  {return (((status_[sequence]>>5)&1)!=0);}
947  /// To flag a variable (not inline to allow for column generation)
948  void setFlagged( int sequence);
949  inline void clearFlagged( int sequence)
950  {
951    status_[sequence] &= ~64;
952  }
953  inline bool flagged(int sequence) const
954  {return ((status_[sequence]&64)!=0);}
955  /// To say row active in primal pivot row choice
956  inline void setActive( int iRow)
957  {
958    status_[iRow] |= 128;
959  }
960  inline void clearActive( int iRow)
961  {
962    status_[iRow] &= ~128;
963  }
964  inline bool active(int iRow) const
965  {return ((status_[iRow]&128)!=0);}
966  /** Set up status array (can be used by OsiClp).
967      Also can be used to set up all slack basis */
968  void createStatus() ;
969  /** Sets up all slack basis and resets solution to
970      as it was after initial load or readMps */
971  void allSlackBasis(bool resetSolution=false);
972   
973  /// So we know when to be cautious
974  inline int lastBadIteration() const
975  {return lastBadIteration_;}
976  /// Progress flag - at present 0 bit says artificials out
977  inline int progressFlag() const
978  {return progressFlag_;}
979  /// Force re-factorization early
980  inline void forceFactorization(int value)
981  { forceFactorization_ = value;}
982  /// Raw objective value (so always minimize in primal)
983  inline double rawObjectiveValue() const
984  { return objectiveValue_;}
985   /// Compute objective value from solution and put in objectiveValue_
986  void computeObjectiveValue(bool useWorkingSolution=false);
987  /// Compute minimization objective value from internal solution without perturbation
988  double computeInternalObjectiveValue();
989  /** Number of extra rows.  These are ones which will be dynamically created
990      each iteration.  This is for GUB but may have other uses.
991  */
992  inline int numberExtraRows() const
993  { return numberExtraRows_;}
994  /** Maximum number of basic variables - can be more than number of rows if GUB
995  */
996  inline int maximumBasic() const
997  { return maximumBasic_;}
998  /// Iteration when we entered dual or primal
999  inline int baseIteration() const
1000  { return baseIteration_;}
1001  /// Create C++ lines to get to current state
1002  void generateCpp( FILE * fp,bool defaultFactor=false);
1003  /// Gets clean and emptyish factorization
1004  ClpFactorization * getEmptyFactorization();
1005  /// May delete or may make clean and emptyish factorization
1006  void setEmptyFactorization();
1007  /// Move status and solution across
1008  void moveInfo(const ClpSimplex & rhs, bool justStatus=false);
1009  //@}
1010
1011  ///@name Basis handling
1012  // These are only to be used using startFinishOptions (ClpSimplexDual, ClpSimplexPrimal)
1013  // *** At present only without scaling
1014  // *** Slacks havve -1.0 element (so == row activity) - take care
1015  ///Get a row of the tableau (slack part in slack if not NULL)
1016  void getBInvARow(int row, double* z, double * slack=NULL);
1017 
1018  ///Get a row of the basis inverse
1019  void getBInvRow(int row, double* z);
1020 
1021  ///Get a column of the tableau
1022  void getBInvACol(int col, double* vec);
1023 
1024  ///Get a column of the basis inverse
1025  void getBInvCol(int col, double* vec);
1026 
1027  /** Get basic indices (order of indices corresponds to the
1028      order of elements in a vector retured by getBInvACol() and
1029      getBInvCol()).
1030  */
1031  void getBasics(int* index);
1032 
1033  //@}
1034    //-------------------------------------------------------------------------
1035    /**@name Changing bounds on variables and constraints */
1036    //@{
1037       /** Set an objective function coefficient */
1038       void setObjectiveCoefficient( int elementIndex, double elementValue );
1039       /** Set an objective function coefficient */
1040       inline void setObjCoeff( int elementIndex, double elementValue )
1041       { setObjectiveCoefficient( elementIndex, elementValue);}
1042
1043      /** Set a single column lower bound<br>
1044          Use -DBL_MAX for -infinity. */
1045       void setColumnLower( int elementIndex, double elementValue );
1046     
1047      /** Set a single column upper bound<br>
1048          Use DBL_MAX for infinity. */
1049       void setColumnUpper( int elementIndex, double elementValue );
1050
1051      /** Set a single column lower and upper bound */
1052      void setColumnBounds( int elementIndex,
1053        double lower, double upper );
1054
1055      /** Set the bounds on a number of columns simultaneously<br>
1056          The default implementation just invokes setColLower() and
1057          setColUpper() over and over again.
1058          @param indexFirst,indexLast pointers to the beginning and after the
1059                 end of the array of the indices of the variables whose
1060                 <em>either</em> bound changes
1061          @param boundList the new lower/upper bound pairs for the variables
1062      */
1063      void setColumnSetBounds(const int* indexFirst,
1064                                   const int* indexLast,
1065                                   const double* boundList);
1066     
1067      /** Set a single column lower bound<br>
1068          Use -DBL_MAX for -infinity. */
1069       inline void setColLower( int elementIndex, double elementValue )
1070       { setColumnLower(elementIndex, elementValue);}
1071      /** Set a single column upper bound<br>
1072          Use DBL_MAX for infinity. */
1073       inline void setColUpper( int elementIndex, double elementValue )
1074       { setColumnUpper(elementIndex, elementValue);}
1075
1076      /** Set a single column lower and upper bound */
1077      inline void setColBounds( int elementIndex,
1078        double lower, double upper )
1079       { setColumnBounds(elementIndex, lower, upper);}
1080
1081      /** Set the bounds on a number of columns simultaneously<br>
1082          @param indexFirst,indexLast pointers to the beginning and after the
1083                 end of the array of the indices of the variables whose
1084                 <em>either</em> bound changes
1085          @param boundList the new lower/upper bound pairs for the variables
1086      */
1087      inline void setColSetBounds(const int* indexFirst,
1088                                   const int* indexLast,
1089                                   const double* boundList)
1090      { setColumnSetBounds(indexFirst, indexLast, boundList);}
1091     
1092      /** Set a single row lower bound<br>
1093          Use -DBL_MAX for -infinity. */
1094      void setRowLower( int elementIndex, double elementValue );
1095     
1096      /** Set a single row upper bound<br>
1097          Use DBL_MAX for infinity. */
1098      void setRowUpper( int elementIndex, double elementValue ) ;
1099   
1100      /** Set a single row lower and upper bound */
1101      void setRowBounds( int elementIndex,
1102                                 double lower, double upper ) ;
1103   
1104      /** Set the bounds on a number of rows simultaneously<br>
1105          @param indexFirst,indexLast pointers to the beginning and after the
1106                 end of the array of the indices of the constraints whose
1107                 <em>either</em> bound changes
1108          @param boundList the new lower/upper bound pairs for the constraints
1109      */
1110      void setRowSetBounds(const int* indexFirst,
1111                                   const int* indexLast,
1112                                   const double* boundList);
1113   
1114    //@}
1115
1116////////////////// data //////////////////
1117protected:
1118
1119  /**@name data.  Many arrays have a row part and a column part.
1120   There is a single array with both - columns then rows and
1121   then normally two arrays pointing to rows and columns.  The
1122   single array is the owner of memory
1123  */
1124  //@{
1125  /// Worst column primal infeasibility
1126  double columnPrimalInfeasibility_;
1127  /// Worst row primal infeasibility
1128  double rowPrimalInfeasibility_;
1129  /// Sequence of worst (-1 if feasible)
1130  int columnPrimalSequence_;
1131  /// Sequence of worst (-1 if feasible)
1132  int rowPrimalSequence_;
1133  /// Worst column dual infeasibility
1134  double columnDualInfeasibility_;
1135  /// Worst row dual infeasibility
1136  double rowDualInfeasibility_;
1137  /// More special options - see set for details
1138  int moreSpecialOptions_;
1139  /// Iteration when we entered dual or primal
1140  int baseIteration_;
1141  /// Primal tolerance needed to make dual feasible (<largeTolerance)
1142  double primalToleranceToGetOptimal_;
1143  /// Remaining largest dual infeasibility
1144  double remainingDualInfeasibility_;
1145  /// Large bound value (for complementarity etc)
1146  double largeValue_;
1147  /// Largest error on Ax-b
1148  double largestPrimalError_;
1149  /// Largest error on basic duals
1150  double largestDualError_;
1151  /// For computing whether to re-factorize
1152  double alphaAccuracy_;
1153  /// Dual bound
1154  double dualBound_;
1155  /// Alpha (pivot element)
1156  double alpha_;
1157  /// Theta (pivot change)
1158  double theta_;
1159  /// Lower Bound on In variable
1160  double lowerIn_;
1161  /// Value of In variable
1162  double valueIn_;
1163  /// Upper Bound on In variable
1164  double upperIn_;
1165  /// Reduced cost of In variable
1166  double dualIn_;
1167  /// Lower Bound on Out variable
1168  double lowerOut_;
1169  /// Value of Out variable
1170  double valueOut_;
1171  /// Upper Bound on Out variable
1172  double upperOut_;
1173  /// Infeasibility (dual) or ? (primal) of Out variable
1174  double dualOut_;
1175  /// Current dual tolerance for algorithm
1176  double dualTolerance_;
1177  /// Current primal tolerance for algorithm
1178  double primalTolerance_;
1179  /// Sum of dual infeasibilities
1180  double sumDualInfeasibilities_;
1181  /// Sum of primal infeasibilities
1182  double sumPrimalInfeasibilities_;
1183  /// Weight assigned to being infeasible in primal
1184  double infeasibilityCost_;
1185  /// Sum of Dual infeasibilities using tolerance based on error in duals
1186  double sumOfRelaxedDualInfeasibilities_;
1187  /// Sum of Primal infeasibilities using tolerance based on error in primals
1188  double sumOfRelaxedPrimalInfeasibilities_;
1189  /// Acceptable pivot value just after factorization
1190  double acceptablePivot_;
1191  /// Working copy of lower bounds (Owner of arrays below)
1192  double * lower_;
1193  /// Row lower bounds - working copy
1194  double * rowLowerWork_;
1195  /// Column lower bounds - working copy
1196  double * columnLowerWork_;
1197  /// Working copy of upper bounds (Owner of arrays below)
1198  double * upper_;
1199  /// Row upper bounds - working copy
1200  double * rowUpperWork_;
1201  /// Column upper bounds - working copy
1202  double * columnUpperWork_;
1203  /// Working copy of objective (Owner of arrays below)
1204  double * cost_;
1205  /// Row objective - working copy
1206  double * rowObjectiveWork_;
1207  /// Column objective - working copy
1208  double * objectiveWork_;
1209  /// Useful row length arrays
1210  CoinIndexedVector * rowArray_[6];
1211  /// Useful column length arrays
1212  CoinIndexedVector * columnArray_[6];
1213  /// Sequence of In variable
1214  int sequenceIn_;
1215  /// Direction of In, 1 going up, -1 going down, 0 not a clude
1216  int directionIn_;
1217  /// Sequence of Out variable
1218  int sequenceOut_;
1219  /// Direction of Out, 1 to upper bound, -1 to lower bound, 0 - superbasic
1220  int directionOut_;
1221  /// Pivot Row
1222  int pivotRow_;
1223  /// Last good iteration (immediately after a re-factorization)
1224  int lastGoodIteration_;
1225  /// Working copy of reduced costs (Owner of arrays below)
1226  double * dj_;
1227  /// Reduced costs of slacks not same as duals (or - duals)
1228  double * rowReducedCost_;
1229  /// Possible scaled reduced costs
1230  double * reducedCostWork_;
1231  /// Working copy of primal solution (Owner of arrays below)
1232  double * solution_;
1233  /// Row activities - working copy
1234  double * rowActivityWork_;
1235  /// Column activities - working copy
1236  double * columnActivityWork_;
1237  /// Auxiliary model
1238  ClpSimplex * auxiliaryModel_;
1239  /// Number of dual infeasibilities
1240  int numberDualInfeasibilities_;
1241  /// Number of dual infeasibilities (without free)
1242  int numberDualInfeasibilitiesWithoutFree_;
1243  /// Number of primal infeasibilities
1244  int numberPrimalInfeasibilities_;
1245  /// How many iterative refinements to do
1246  int numberRefinements_;
1247  /// dual row pivot choice
1248  ClpDualRowPivot * dualRowPivot_;
1249  /// primal column pivot choice
1250  ClpPrimalColumnPivot * primalColumnPivot_;
1251  /// Basic variables pivoting on which rows
1252  int * pivotVariable_;
1253  /// factorization
1254  ClpFactorization * factorization_;
1255  /// Saved version of solution
1256  double * savedSolution_;
1257  /// Number of times code has tentatively thought optimal
1258  int numberTimesOptimal_;
1259  /// Disaster handler
1260  ClpDisasterHandler * disasterArea_;
1261  /// If change has been made (first attempt at stopping looping)
1262  int changeMade_;
1263  /// Algorithm >0 == Primal, <0 == Dual
1264  int algorithm_;
1265  /** Now for some reliability aids
1266      This forces re-factorization early */
1267  int forceFactorization_;
1268  /** Perturbation:
1269      -50 to +50 - perturb by this power of ten (-6 sounds good)
1270      100 - auto perturb if takes too long (1.0e-6 largest nonzero)
1271      101 - we are perturbed
1272      102 - don't try perturbing again
1273      default is 100
1274  */
1275  int perturbation_;
1276  /// Saved status regions
1277  unsigned char * saveStatus_;
1278  /** Very wasteful way of dealing with infeasibilities in primal.
1279      However it will allow non-linearities and use of dual
1280      analysis.  If it doesn't work it can easily be replaced.
1281  */
1282  ClpNonLinearCost * nonLinearCost_;
1283  /// So we know when to be cautious
1284  int lastBadIteration_;
1285  /// So we know when to open up again
1286  int lastFlaggedIteration_;
1287  /// Can be used for count of fake bounds (dual) or fake costs (primal)
1288  int numberFake_;
1289  /// Can be used for count of changed costs (dual) or changed bounds (primal)
1290  int numberChanged_;
1291  /// Progress flag - at present 0 bit says artificials out, 1 free in
1292  int progressFlag_;
1293  /// First free/super-basic variable (-1 if none)
1294  int firstFree_;
1295  /** Number of extra rows.  These are ones which will be dynamically created
1296      each iteration.  This is for GUB but may have other uses.
1297  */
1298  int numberExtraRows_;
1299  /** Maximum number of basic variables - can be more than number of rows if GUB
1300  */
1301  int maximumBasic_;
1302  /** For advanced use.  When doing iterative solves things can get
1303      nasty so on values pass if incoming solution has largest
1304      infeasibility < incomingInfeasibility throw out variables
1305      from basis until largest infeasibility < allowedInfeasibility.
1306      if allowedInfeasibility>= incomingInfeasibility this is
1307      always possible altough you may end up with an all slack basis.
1308
1309      Defaults are 1.0,10.0
1310  */
1311  double incomingInfeasibility_;
1312  double allowedInfeasibility_;
1313  /// Automatic scaling of objective and rhs and bounds
1314  int automaticScale_;
1315  /// A copy of model with certain state - normally without cuts
1316  ClpSimplex * baseModel_;
1317  /// For dealing with all issues of cycling etc
1318  ClpSimplexProgress progress_;
1319public:
1320  /// Spare int array for passing information [0]!=0 switches on
1321  mutable int spareIntArray_[4];
1322  /// Spare double array for passing information [0]!=0 switches on
1323  mutable double spareDoubleArray_[4];
1324protected:
1325  /// Allow OsiClp certain perks
1326  friend class OsiClpSolverInterface;
1327  //@}
1328};
1329//#############################################################################
1330/** A function that tests the methods in the ClpSimplex class. The
1331    only reason for it not to be a member method is that this way it doesn't
1332    have to be compiled into the library. And that's a gain, because the
1333    library should be compiled with optimization on, but this method should be
1334    compiled with debugging.
1335
1336    It also does some testing of ClpFactorization class
1337 */
1338void
1339ClpSimplexUnitTest(const std::string & mpsDir);
1340
1341// For Devex stuff
1342#define DEVEX_TRY_NORM 1.0e-4
1343#define DEVEX_ADD_ONE 1.0
1344#endif
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