source: trunk/Cbc/src/CbcTreeLocal.hpp @ 310

Last change on this file since 310 was 186, checked in by forrest, 14 years ago

heuristics

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1// Copyright (C) 2004, International Business Machines
2// Corporation and others.  All Rights Reserved.
3#ifndef CbcTreeLocal_H
4#define CbcTreeLocal_H
5
6//#############################################################################
7/*  This implements (approximately) local branching as in the 2002 paper by
8    Matteo Fischetti and Andrea Lodi.
9
10    The very simple version of the algorithm for problems with
11    0-1 variables and continuous is as follows:
12
13    Obtain a feasible solution (one can be passed in).
14
15    Add a cut which limits search to a k neighborhood of this solution.
16    (At most k 0-1 variables may change value)
17    Do branch and bound on this problem.
18
19    If finished search and proven optimal then we can reverse cut so
20    any solutions must be at least k+1 away from solution and we can
21    add a new cut limiting search to a k neighborhood of new solution
22    repeat.
23
24    If finished search and no new solution then the simplest version
25    would reverse last cut and complete search.  The version implemented
26    here can use time and node limits and can widen search (increase effective k)
27    .... and more
28
29*/
30
31#include "CbcTree.hpp"
32#include "CbcNode.hpp"
33#include "OsiRowCut.hpp"
34class CbcModel;
35
36
37class CbcTreeLocal : public CbcTree {
38
39public:
40
41  // Default Constructor
42  CbcTreeLocal ();
43
44  /* Constructor with solution.
45     If solution NULL no solution, otherwise must be integer
46     range is initial upper bound (k) on difference from given solution.
47     typeCuts -
48              0 means just 0-1 cuts and will need to refine 0-1 solution
49              1 uses weaker cuts on all integer variables
50     maxDiversification is maximum number of range widenings to try
51     timeLimit is seconds in subTree
52     nodeLimit is nodes in subTree
53     refine is whether to see if we can prove current solution is optimal
54     when we fix all 0-1 (in case typeCuts==0 and there are general integer variables)
55     if false then no refinement but reverse cuts weaker
56  */
57  CbcTreeLocal (CbcModel * model,const double * solution ,int range=10,
58                   int typeCuts=0,int maxDiversification=0,
59                   int timeLimit=1000000, int nodeLimit=1000000,bool refine=true);
60  // Copy constructor
61  CbcTreeLocal ( const CbcTreeLocal & rhs);
62
63  // = operator
64  CbcTreeLocal & operator=(const CbcTreeLocal & rhs);
65   
66  virtual ~CbcTreeLocal();
67
68  /// Clone
69  virtual CbcTree * clone() const;
70
71/*! \name Heap access and maintenance methods */
72//@{
73
74  /// Return the top node of the heap
75  virtual CbcNode * top();
76
77  /// Add a node to the heap
78  virtual void push(CbcNode * x);
79
80  /// Remove the top node from the heap
81  virtual void pop() ;
82
83//@}
84/*! \name Other stuff */
85//@{
86
87  /// Create cut - return -1 if bad, 0 if okay and 1 if cut is everything
88  int createCut(const double * solution, OsiRowCut & cut);
89
90  /// Test if empty *** note may be overridden
91  virtual bool empty() ;
92
93  /// We may have got an intelligent tree so give it one more chance
94  virtual void endSearch() ;
95  /// Other side of last cut branch (if bias==rhs_ will be weakest possible)
96  void reverseCut(int state, double bias=0.0);
97  /// Delete last cut branch
98  void deleteCut(OsiRowCut & cut);
99  /// Pass in solution (so can be used after heuristic)
100  void passInSolution(const double * solution, double solutionValue);
101
102//@}
103private:
104  // Node for local cuts
105  CbcNode * localNode_;
106  // best solution
107  double * bestSolution_;
108  // saved solution
109  double * savedSolution_;
110  // solution number at start of pass
111  int saveNumberSolutions_;
112  /* Cut.  If zero size then no solution yet.  Otherwise is left hand branch */
113  OsiRowCut cut_;
114  // This cut fixes all 0-1 variables
115  OsiRowCut fixedCut_;
116  // Model
117  CbcModel * model_;
118  // Original lower bounds
119  double * originalLower_;
120  // Original upper bounds
121  double * originalUpper_;
122  // range i.e. k
123  int range_;
124  // Type of cuts - 0=just 0-1, 1=all
125  int typeCuts_;
126  // maximum number of diversifications
127  int maxDiversification_;
128  // current diversification
129  int diversification_;
130  // Whether next will be strong diversification
131  bool nextStrong_;
132  // Current rhs
133  double rhs_;
134  // Save allowable gap
135  double savedGap_;
136  // Best solution
137  double bestCutoff_;
138  // time limit per subtree
139  int timeLimit_;
140  // time when subtree started
141  int startTime_;
142  // node limit for subtree
143  int nodeLimit_;
144  // node count when subtree started
145  int startNode_;
146  // -1 not started, 0 == stop on first solution, 1 don't stop on first, 2 refinement step
147  int searchType_;
148  // Whether to do refinement step
149  bool refine_;
150
151};
152#endif
153
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