source: trunk/Clp/src/ClpSimplexPrimal.hpp @ 2385

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1/* $Id: ClpSimplexPrimal.hpp 2385 2019-01-06 19:43:06Z unxusr $ */
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  /**@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        for use of exotic parameter startFinishoptions see Clpsimplex.hpp
112     */
113
114  int primal(int ifValuesPass = 0, int startFinishOptions = 0);
115  //@}
116
117  /**@name For advanced users */
118  //@{
119  /// Do not change infeasibility cost and always say optimal
120  void alwaysOptimal(bool onOff);
121  bool alwaysOptimal() const;
122  /** Normally outgoing variables can go out to slightly negative
123         values (but within tolerance) - this is to help stability and
124         and degeneracy.  This can be switched off
125     */
126  void exactOutgoing(bool onOff);
127  bool exactOutgoing() const;
128  //@}
129
130  /**@name Functions used in primal */
131  //@{
132  /** This has the flow between re-factorizations
133
134         Returns a code to say where decision to exit was made
135         Problem status set to:
136
137         -2 re-factorize
138         -4 Looks optimal/infeasible
139         -5 Looks unbounded
140         +3 max iterations
141
142         valuesOption has original value of valuesPass
143      */
144  int whileIterating(int valuesOption);
145
146  /** Do last half of an iteration.  This is split out so people can
147         force incoming variable.  If solveType_ is 2 then this may
148         re-factorize while normally it would exit to re-factorize.
149         Return codes
150         Reasons to come out (normal mode/user mode):
151         -1 normal
152         -2 factorize now - good iteration/ NA
153         -3 slight inaccuracy - refactorize - iteration done/ same but factor done
154         -4 inaccuracy - refactorize - no iteration/ NA
155         -5 something flagged - go round again/ pivot not possible
156         +2 looks unbounded
157         +3 max iterations (iteration done)
158
159         With solveType_ ==2 this should
160         Pivot in a variable and choose an outgoing one.  Assumes primal
161         feasible - will not go through a bound.  Returns step length in theta
162         Returns ray in ray_
163     */
164  int pivotResult(int ifValuesPass = 0);
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    int valuesPass);
174  /**
175         Row array has pivot column
176         This chooses pivot row.
177         Rhs array is used for distance to next bound (for speed)
178         For speed, we may need to go to a bucket approach when many
179         variables go through bounds
180         If valuesPass non-zero then compute dj for direction
181     */
182  void primalRow(CoinIndexedVector *rowArray,
183    CoinIndexedVector *rhsArray,
184    CoinIndexedVector *spareArray,
185    int valuesPass);
186  /**
187         Chooses primal pivot column
188         updateArray has cost updates (also use pivotRow_ from last iteration)
189         Would be faster with separate region to scan
190         and will have this (with square of infeasibility) when steepest
191         For easy problems we can just choose one of the first columns we look at
192     */
193  void primalColumn(CoinIndexedVector *updateArray,
194    CoinIndexedVector *spareRow1,
195    CoinIndexedVector *spareRow2,
196    CoinIndexedVector *spareColumn1,
197    CoinIndexedVector *spareColumn2);
198
199  /** Checks if tentative optimal actually means unbounded in primal
200         Returns -3 if not, 2 if is unbounded */
201  int checkUnbounded(CoinIndexedVector *ray, CoinIndexedVector *spare,
202    double changeCost);
203  /**  Refactorizes if necessary
204          Checks if finished.  Updates status.
205          lastCleaned refers to iteration at which some objective/feasibility
206          cleaning too place.
207
208          type - 0 initial so set up save arrays etc
209               - 1 normal -if good update save
210           - 2 restoring from saved
211          saveModel is normally NULL but may not be if doing Sprint
212     */
213  void statusOfProblemInPrimal(int &lastCleaned, int type,
214    ClpSimplexProgress *progress,
215    bool doFactorization,
216    int ifValuesPass,
217    ClpSimplex *saveModel = NULL);
218  /// Perturbs problem (method depends on perturbation())
219  void perturb(int type);
220  /// Take off effect of perturbation and say whether to try dual
221  bool unPerturb();
222  /// Unflag all variables and return number unflagged
223  int unflag();
224  /** Get next superbasic -1 if none,
225         Normal type is 1
226         If type is 3 then initializes sorted list
227         if 2 uses list.
228     */
229  int nextSuperBasic(int superBasicType, CoinIndexedVector *columnArray);
230
231  /// Create primal ray
232  void primalRay(CoinIndexedVector *rowArray);
233  /// Clears all bits and clears rowArray[1] etc
234  void clearAll();
235
236  /// Sort of lexicographic resolve
237  int lexSolve();
238
239  //@}
240};
241#endif
242
243/* vi: softtabstop=2 shiftwidth=2 expandtab tabstop=2
244*/
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