source: stable/1.15/Clp/src/ClpSimplexPrimal.hpp @ 1949

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1/* $Id: ClpSimplexPrimal.hpp 1665 2011-01-04 17:55:54Z forrest $ */
2// Copyright (C) 2002, International Business Machines
3// Corporation and others.  All Rights Reserved.
4// This code is licensed under the terms of the Eclipse Public License (EPL).
5/*
6   Authors
7
8   John Forrest
9
10 */
11#ifndef ClpSimplexPrimal_H
12#define ClpSimplexPrimal_H
13
14#include "ClpSimplex.hpp"
15
16/** This solves LPs using the primal simplex method
17
18    It inherits from ClpSimplex.  It has no data of its own and
19    is never created - only cast from a ClpSimplex object at algorithm time.
20
21*/
22
23class ClpSimplexPrimal : public ClpSimplex {
24
25public:
26
27     /**@name Description of algorithm */
28     //@{
29     /** Primal algorithm
30
31         Method
32
33        It tries to be a single phase approach with a weight of 1.0 being
34        given to getting optimal and a weight of infeasibilityCost_ being
35        given to getting primal feasible.  In this version I have tried to
36        be clever in a stupid way.  The idea of fake bounds in dual
37        seems to work so the primal analogue would be that of getting
38        bounds on reduced costs (by a presolve approach) and using
39        these for being above or below feasible region.  I decided to waste
40        memory and keep these explicitly.  This allows for non-linear
41        costs!  I have not tested non-linear costs but will be glad
42        to do something if a reasonable example is provided.
43
44        The code is designed to take advantage of sparsity so arrays are
45        seldom zeroed out from scratch or gone over in their entirety.
46        The only exception is a full scan to find incoming variable for
47        Dantzig row choice.  For steepest edge we keep an updated list
48        of dual infeasibilities (actually squares).
49        On easy problems we don't need full scan - just
50        pick first reasonable.  This method has not been coded.
51
52        One problem is how to tackle degeneracy and accuracy.  At present
53        I am using the modification of costs which I put in OSL and which was
54        extended by Gill et al.  I am still not sure whether we will also
55        need explicit perturbation.
56
57        The flow of primal is three while loops as follows:
58
59        while (not finished) {
60
61          while (not clean solution) {
62
63             Factorize and/or clean up solution by changing bounds so
64         primal feasible.  If looks finished check fake primal bounds.
65         Repeat until status is iterating (-1) or finished (0,1,2)
66
67          }
68
69          while (status==-1) {
70
71            Iterate until no pivot in or out or time to re-factorize.
72
73            Flow is:
74
75            choose pivot column (incoming variable).  if none then
76        we are primal feasible so looks as if done but we need to
77        break and check bounds etc.
78
79        Get pivot column in tableau
80
81            Choose outgoing row.  If we don't find one then we look
82        primal unbounded so break and check bounds etc.  (Also the
83        pivot tolerance is larger after any iterations so that may be
84        reason)
85
86            If we do find outgoing row, we may have to adjust costs to
87        keep going forwards (anti-degeneracy).  Check pivot will be stable
88        and if unstable throw away iteration and break to re-factorize.
89        If minor error re-factorize after iteration.
90
91        Update everything (this may involve changing bounds on
92        variables to stay primal feasible.
93
94          }
95
96        }
97
98        TODO's (or maybe not)
99
100        At present we never check we are going forwards.  I overdid that in
101        OSL so will try and make a last resort.
102
103        Needs partial scan pivot in option.
104
105        May need other anti-degeneracy measures, especially if we try and use
106        loose tolerances as a way to solve in fewer iterations.
107
108        I like idea of dynamic scaling.  This gives opportunity to decouple
109        different implications of scaling for accuracy, iteration count and
110        feasibility tolerance.
111
112        for use of exotic parameter startFinishoptions see Clpsimplex.hpp
113     */
114
115     int primal(int ifValuesPass = 0, int startFinishOptions = 0);
116     //@}
117
118     /**@name For advanced users */
119     //@{
120     /// Do not change infeasibility cost and always say optimal
121     void alwaysOptimal(bool onOff);
122     bool alwaysOptimal() const;
123     /** Normally outgoing variables can go out to slightly negative
124         values (but within tolerance) - this is to help stability and
125         and degeneracy.  This can be switched off
126     */
127     void exactOutgoing(bool onOff);
128     bool exactOutgoing() const;
129     //@}
130
131     /**@name Functions used in primal */
132     //@{
133     /** This has the flow between re-factorizations
134
135         Returns a code to say where decision to exit was made
136         Problem status set to:
137
138         -2 re-factorize
139         -4 Looks optimal/infeasible
140         -5 Looks unbounded
141         +3 max iterations
142
143         valuesOption has original value of valuesPass
144      */
145     int whileIterating(int valuesOption);
146
147     /** Do last half of an iteration.  This is split out so people can
148         force incoming variable.  If solveType_ is 2 then this may
149         re-factorize while normally it would exit to re-factorize.
150         Return codes
151         Reasons to come out (normal mode/user mode):
152         -1 normal
153         -2 factorize now - good iteration/ NA
154         -3 slight inaccuracy - refactorize - iteration done/ same but factor done
155         -4 inaccuracy - refactorize - no iteration/ NA
156         -5 something flagged - go round again/ pivot not possible
157         +2 looks unbounded
158         +3 max iterations (iteration done)
159
160         With solveType_ ==2 this should
161         Pivot in a variable and choose an outgoing one.  Assumes primal
162         feasible - will not go through a bound.  Returns step length in theta
163         Returns ray in ray_
164     */
165     int pivotResult(int ifValuesPass = 0);
166
167
168     /** The primals are updated by the given array.
169         Returns number of infeasibilities.
170         After rowArray will have cost changes for use next iteration
171     */
172     int updatePrimalsInPrimal(CoinIndexedVector * rowArray,
173                               double theta,
174                               double & objectiveChange,
175                               int valuesPass);
176     /**
177         Row array has pivot column
178         This chooses pivot row.
179         Rhs array is used for distance to next bound (for speed)
180         For speed, we may need to go to a bucket approach when many
181         variables go through bounds
182         If valuesPass non-zero then compute dj for direction
183     */
184     void primalRow(CoinIndexedVector * rowArray,
185                    CoinIndexedVector * rhsArray,
186                    CoinIndexedVector * spareArray,
187                    int valuesPass);
188     /**
189         Chooses primal pivot column
190         updateArray has cost updates (also use pivotRow_ from last iteration)
191         Would be faster with separate region to scan
192         and will have this (with square of infeasibility) when steepest
193         For easy problems we can just choose one of the first columns we look at
194     */
195     void primalColumn(CoinIndexedVector * updateArray,
196                       CoinIndexedVector * spareRow1,
197                       CoinIndexedVector * spareRow2,
198                       CoinIndexedVector * spareColumn1,
199                       CoinIndexedVector * spareColumn2);
200
201     /** Checks if tentative optimal actually means unbounded in primal
202         Returns -3 if not, 2 if is unbounded */
203     int checkUnbounded(CoinIndexedVector * ray, CoinIndexedVector * spare,
204                        double changeCost);
205     /**  Refactorizes if necessary
206          Checks if finished.  Updates status.
207          lastCleaned refers to iteration at which some objective/feasibility
208          cleaning too place.
209
210          type - 0 initial so set up save arrays etc
211               - 1 normal -if good update save
212           - 2 restoring from saved
213          saveModel is normally NULL but may not be if doing Sprint
214     */
215     void statusOfProblemInPrimal(int & lastCleaned, int type,
216                                  ClpSimplexProgress * progress,
217                                  bool doFactorization,
218                                  int ifValuesPass,
219                                  ClpSimplex * saveModel = NULL);
220     /// Perturbs problem (method depends on perturbation())
221     void perturb(int type);
222     /// Take off effect of perturbation and say whether to try dual
223     bool unPerturb();
224     /// Unflag all variables and return number unflagged
225     int unflag();
226     /** Get next superbasic -1 if none,
227         Normal type is 1
228         If type is 3 then initializes sorted list
229         if 2 uses list.
230     */
231     int nextSuperBasic(int superBasicType, CoinIndexedVector * columnArray);
232
233     /// Create primal ray
234     void primalRay(CoinIndexedVector * rowArray);
235     /// Clears all bits and clears rowArray[1] etc
236     void clearAll();
237
238     /// Sort of lexicographic resolve
239     int lexSolve();
240
241     //@}
242};
243#endif
244
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