source: branches/pre/include/ClpSimplexPrimal.hpp @ 222

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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 ClpSimplexPrimal_H
11#define ClpSimplexPrimal_H
12
13#include "ClpSimplex.hpp"
14
15/** This solves LPs using the primal simplex method
16
17    It inherits from ClpSimplex.  It has no data of its own and
18    is never created - only cast from a ClpSimplex object at algorithm time.
19
20*/
21
22class ClpSimplexPrimal : public ClpSimplex {
23
24public:
25
26  /**@name Description of algorithm */
27  //@{
28  /** Primal algorithm
29
30      Method
31
32     It tries to be a single phase approach with a weight of 1.0 being
33     given to getting optimal and a weight of infeasibilityCost_ being
34     given to getting primal feasible.  In this version I have tried to
35     be clever in a stupid way.  The idea of fake bounds in dual
36     seems to work so the primal analogue would be that of getting
37     bounds on reduced costs (by a presolve approach) and using
38     these for being above or below feasible region.  I decided to waste
39     memory and keep these explicitly.  This allows for non-linear
40     costs!  I have not tested non-linear costs but will be glad
41     to do something if a reasonable example is provided.
42
43     The code is designed to take advantage of sparsity so arrays are
44     seldom zeroed out from scratch or gone over in their entirety.
45     The only exception is a full scan to find incoming variable for
46     Dantzig row choice.  For steepest edge we keep an updated list
47     of dual infeasibilities (actually squares). 
48     On easy problems we don't need full scan - just
49     pick first reasonable.  This method has not been coded.
50
51     One problem is how to tackle degeneracy and accuracy.  At present
52     I am using the modification of costs which I put in OSL and which was
53     extended by Gill et al.  I am still not sure whether we will also
54     need explicit perturbation.
55
56     The flow of primal is three while loops as follows:
57
58     while (not finished) {
59
60       while (not clean solution) {
61
62          Factorize and/or clean up solution by changing bounds so
63          primal feasible.  If looks finished check fake primal bounds.
64          Repeat until status is iterating (-1) or finished (0,1,2)
65
66       }
67
68       while (status==-1) {
69
70         Iterate until no pivot in or out or time to re-factorize.
71
72         Flow is:
73
74         choose pivot column (incoming variable).  if none then
75         we are primal feasible so looks as if done but we need to
76         break and check bounds etc.
77
78         Get pivot column in tableau
79
80         Choose outgoing row.  If we don't find one then we look
81         primal unbounded so break and check bounds etc.  (Also the
82         pivot tolerance is larger after any iterations so that may be
83         reason)
84
85         If we do find outgoing row, we may have to adjust costs to
86         keep going forwards (anti-degeneracy).  Check pivot will be stable
87         and if unstable throw away iteration and break to re-factorize.
88         If minor error re-factorize after iteration.
89
90         Update everything (this may involve changing bounds on
91         variables to stay primal feasible.
92
93       }
94
95     }
96
97     TODO's (or maybe not)
98
99     At present we never check we are going forwards.  I overdid that in
100     OSL so will try and make a last resort.
101
102     Needs partial scan pivot in option.
103
104     May need other anti-degeneracy measures, especially if we try and use
105     loose tolerances as a way to solve in fewer iterations.
106
107     I like idea of dynamic scaling.  This gives opportunity to decouple
108     different implications of scaling for accuracy, iteration count and
109     feasibility tolerance.
110
111  */
112
113  int primal(int ifValuesPass=0);
114  //@}
115
116  /**@name For advanced users */
117  //@{
118  /// Do not change infeasibility cost and always say optimal
119  void alwaysOptimal(bool onOff);
120  bool alwaysOptimal() const;
121  /** Normally outgoing variables can go out to slightly negative
122      values (but within tolerance) - this is to help stability and
123      and degeneracy.  This can be switched off
124  */
125  void exactOutgoing(bool onOff);
126  bool exactOutgoing() const;
127  //@}
128
129  /**@name Functions used in primal */
130  //@{
131  /** This has the flow between re-factorizations
132
133      Returns a code to say where decision to exit was made
134      Problem status set to:
135
136      -2 re-factorize
137      -4 Looks optimal/infeasible
138      -5 Looks unbounded
139      +3 max iterations
140     
141      valuesOption has original value of valuesPass
142   */
143  int whileIterating(int valuesOption); 
144
145  /** Do last half of an iteration.  This is split out so people can
146      force incoming variable.  If solveType_ is 2 then this may
147      re-factorize while normally it would exit to re-factorize.
148      Return codes
149      Reasons to come out (normal mode/user mode):
150      -1 normal
151      -2 factorize now - good iteration/ NA
152      -3 slight inaccuracy - refactorize - iteration done/ same but factor done
153      -4 inaccuracy - refactorize - no iteration/ NA
154      -5 something flagged - go round again/ pivot not possible
155      +2 looks unbounded
156      +3 max iterations (iteration done)
157
158      With solveType_ ==2 this should
159      Pivot in a variable and choose an outgoing one.  Assumes primal
160      feasible - will not go through a bound.  Returns step length in theta
161      Returns ray in ray_
162  */
163  int pivotResult(int ifValuesPass=0);
164
165
166  /** The primals are updated by the given array.
167      Returns number of infeasibilities.
168      After rowArray will have cost changes for use next iteration
169  */
170  int updatePrimalsInPrimal(CoinIndexedVector * rowArray,
171                  double theta,
172                  double & objectiveChange);
173  /**
174      Row array has pivot column
175      This chooses pivot row.
176      Rhs array is used for distance to next bound (for speed)
177      For speed, we may need to go to a bucket approach when many
178      variables go through bounds
179      On exit rhsArray will have changes in costs of basic variables
180      If valuesPass non-zero then compute dj for direction
181  */
182  void primalRow(CoinIndexedVector * rowArray,
183                 CoinIndexedVector * rhsArray,
184                 CoinIndexedVector * spareArray,
185                 CoinIndexedVector * spareArray2,
186                 int valuesPass);
187  /**
188      Chooses primal pivot column
189      updateArray has cost updates (also use pivotRow_ from last iteration)
190      Would be faster with separate region to scan
191      and will have this (with square of infeasibility) when steepest
192      For easy problems we can just choose one of the first columns we look at
193  */
194  void primalColumn(CoinIndexedVector * updateArray,
195                    CoinIndexedVector * spareRow1,
196                    CoinIndexedVector * spareRow2,
197                    CoinIndexedVector * spareColumn1,
198                    CoinIndexedVector * spareColumn2);
199
200  /** Checks if tentative optimal actually means unbounded in primal
201      Returns -3 if not, 2 if is unbounded */
202  int checkUnbounded(CoinIndexedVector * ray,CoinIndexedVector * spare,
203                     double changeCost);
204  /**  Refactorizes if necessary
205       Checks if finished.  Updates status.
206       lastCleaned refers to iteration at which some objective/feasibility
207       cleaning too place.
208
209       type - 0 initial so set up save arrays etc
210            - 1 normal -if good update save
211            - 2 restoring from saved
212       originalModel is normally NULL but may not be if doing Sprint
213  */
214  void statusOfProblemInPrimal(int & lastCleaned, int type,
215                             ClpSimplexProgress * progress,
216                               ClpSimplex * originalModel=NULL);
217  /// Perturbs problem (method depends on perturbation())
218  void perturb(int type);
219  /// Take off effect of perturbation and say whether to try dual
220  bool unPerturb();
221  /// Unflag all variables and return number unflagged
222  int unflag();
223  /** Get next superbasic -1 if none,
224      Normal type is 1
225      If type is 3 then initializes sorted list
226      if 2 uses list.
227  */
228  int nextSuperBasic(int superBasicType,CoinIndexedVector * columnArray);
229
230  /// Create primal ray
231  void primalRay(CoinIndexedVector * rowArray);
232 
233  //@}
234};
235#endif
236
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