limp-cbc-0.3.2.0: cbits/coin/CbcTreeLocal.hpp
/* $Id: CbcTreeLocal.hpp 1573 2011-01-05 01:12:36Z lou $ */
// Copyright (C) 2004, International Business Machines
// Corporation and others. All Rights Reserved.
// This code is licensed under the terms of the Eclipse Public License (EPL).
#ifndef CbcTreeLocal_H
#define CbcTreeLocal_H
//#############################################################################
/* This implements (approximately) local branching as in the 2002 paper by
Matteo Fischetti and Andrea Lodi.
The very simple version of the algorithm for problems with
0-1 variables and continuous is as follows:
Obtain a feasible solution (one can be passed in).
Add a cut which limits search to a k neighborhood of this solution.
(At most k 0-1 variables may change value)
Do branch and bound on this problem.
If finished search and proven optimal then we can reverse cut so
any solutions must be at least k+1 away from solution and we can
add a new cut limiting search to a k neighborhood of new solution
repeat.
If finished search and no new solution then the simplest version
would reverse last cut and complete search. The version implemented
here can use time and node limits and can widen search (increase effective k)
.... and more
*/
#include "CbcTree.hpp"
#include "CbcNode.hpp"
#include "OsiRowCut.hpp"
class CbcModel;
class CbcTreeLocal : public CbcTree {
public:
// Default Constructor
CbcTreeLocal ();
/* Constructor with solution.
If solution NULL no solution, otherwise must be integer
range is initial upper bound (k) on difference from given solution.
typeCuts -
0 means just 0-1 cuts and will need to refine 0-1 solution
1 uses weaker cuts on all integer variables
maxDiversification is maximum number of range widenings to try
timeLimit is seconds in subTree
nodeLimit is nodes in subTree
refine is whether to see if we can prove current solution is optimal
when we fix all 0-1 (in case typeCuts==0 and there are general integer variables)
if false then no refinement but reverse cuts weaker
*/
CbcTreeLocal (CbcModel * model, const double * solution , int range = 10,
int typeCuts = 0, int maxDiversification = 0,
int timeLimit = 1000000, int nodeLimit = 1000000, bool refine = true);
// Copy constructor
CbcTreeLocal ( const CbcTreeLocal & rhs);
// = operator
CbcTreeLocal & operator=(const CbcTreeLocal & rhs);
virtual ~CbcTreeLocal();
/// Clone
virtual CbcTree * clone() const;
/// Create C++ lines to get to current state
virtual void generateCpp( FILE * fp) ;
/*! \name Heap access and maintenance methods */
//@{
/// Return the top node of the heap
virtual CbcNode * top() const;
/// Add a node to the heap
virtual void push(CbcNode * x);
/// Remove the top node from the heap
virtual void pop() ;
//@}
/*! \name Other stuff */
//@{
/// Create cut - return -1 if bad, 0 if okay and 1 if cut is everything
int createCut(const double * solution, OsiRowCut & cut);
/// Test if empty *** note may be overridden
virtual bool empty() ;
/// We may have got an intelligent tree so give it one more chance
virtual void endSearch() ;
/// Other side of last cut branch (if bias==rhs_ will be weakest possible)
void reverseCut(int state, double bias = 0.0);
/// Delete last cut branch
void deleteCut(OsiRowCut & cut);
/// Pass in solution (so can be used after heuristic)
void passInSolution(const double * solution, double solutionValue);
// range i.e. k
inline int range() const {
return range_;
}
// setrange i.e. k
inline void setRange(int value) {
range_ = value;
}
// Type of cuts - 0=just 0-1, 1=all
inline int typeCuts() const {
return typeCuts_;
}
// Type of cuts - 0=just 0-1, 1=all
inline void setTypeCuts(int value) {
typeCuts_ = value;
}
// maximum number of diversifications
inline int maxDiversification() const {
return maxDiversification_;
}
// maximum number of diversifications
inline void setMaxDiversification(int value) {
maxDiversification_ = value;
}
// time limit per subtree
inline int timeLimit() const {
return timeLimit_;
}
// time limit per subtree
inline void setTimeLimit(int value) {
timeLimit_ = value;
}
// node limit for subtree
inline int nodeLimit() const {
return nodeLimit_;
}
// node limit for subtree
inline void setNodeLimit(int value) {
nodeLimit_ = value;
}
// Whether to do refinement step
inline bool refine() const {
return refine_;
}
// Whether to do refinement step
inline void setRefine(bool yesNo) {
refine_ = yesNo;
}
//@}
private:
// Node for local cuts
CbcNode * localNode_;
// best solution
double * bestSolution_;
// saved solution
double * savedSolution_;
// solution number at start of pass
int saveNumberSolutions_;
/* Cut. If zero size then no solution yet. Otherwise is left hand branch */
OsiRowCut cut_;
// This cut fixes all 0-1 variables
OsiRowCut fixedCut_;
// Model
CbcModel * model_;
// Original lower bounds
double * originalLower_;
// Original upper bounds
double * originalUpper_;
// range i.e. k
int range_;
// Type of cuts - 0=just 0-1, 1=all
int typeCuts_;
// maximum number of diversifications
int maxDiversification_;
// current diversification
int diversification_;
// Whether next will be strong diversification
bool nextStrong_;
// Current rhs
double rhs_;
// Save allowable gap
double savedGap_;
// Best solution
double bestCutoff_;
// time limit per subtree
int timeLimit_;
// time when subtree started
int startTime_;
// node limit for subtree
int nodeLimit_;
// node count when subtree started
int startNode_;
// -1 not started, 0 == stop on first solution, 1 don't stop on first, 2 refinement step
int searchType_;
// Whether to do refinement step
bool refine_;
};
class CbcTreeVariable : public CbcTree {
public:
// Default Constructor
CbcTreeVariable ();
/* Constructor with solution.
If solution NULL no solution, otherwise must be integer
range is initial upper bound (k) on difference from given solution.
typeCuts -
0 means just 0-1 cuts and will need to refine 0-1 solution
1 uses weaker cuts on all integer variables
maxDiversification is maximum number of range widenings to try
timeLimit is seconds in subTree
nodeLimit is nodes in subTree
refine is whether to see if we can prove current solution is optimal
when we fix all 0-1 (in case typeCuts==0 and there are general integer variables)
if false then no refinement but reverse cuts weaker
*/
CbcTreeVariable (CbcModel * model, const double * solution , int range = 10,
int typeCuts = 0, int maxDiversification = 0,
int timeLimit = 1000000, int nodeLimit = 1000000, bool refine = true);
// Copy constructor
CbcTreeVariable ( const CbcTreeVariable & rhs);
// = operator
CbcTreeVariable & operator=(const CbcTreeVariable & rhs);
virtual ~CbcTreeVariable();
/// Clone
virtual CbcTree * clone() const;
/// Create C++ lines to get to current state
virtual void generateCpp( FILE * fp) ;
/*! \name Heap access and maintenance methods */
//@{
/// Return the top node of the heap
virtual CbcNode * top() const;
/// Add a node to the heap
virtual void push(CbcNode * x);
/// Remove the top node from the heap
virtual void pop() ;
//@}
/*! \name Other stuff */
//@{
/// Create cut - return -1 if bad, 0 if okay and 1 if cut is everything
int createCut(const double * solution, OsiRowCut & cut);
/// Test if empty *** note may be overridden
virtual bool empty() ;
/// We may have got an intelligent tree so give it one more chance
virtual void endSearch() ;
/// Other side of last cut branch (if bias==rhs_ will be weakest possible)
void reverseCut(int state, double bias = 0.0);
/// Delete last cut branch
void deleteCut(OsiRowCut & cut);
/// Pass in solution (so can be used after heuristic)
void passInSolution(const double * solution, double solutionValue);
// range i.e. k
inline int range() const {
return range_;
}
// setrange i.e. k
inline void setRange(int value) {
range_ = value;
}
// Type of cuts - 0=just 0-1, 1=all
inline int typeCuts() const {
return typeCuts_;
}
// Type of cuts - 0=just 0-1, 1=all
inline void setTypeCuts(int value) {
typeCuts_ = value;
}
// maximum number of diversifications
inline int maxDiversification() const {
return maxDiversification_;
}
// maximum number of diversifications
inline void setMaxDiversification(int value) {
maxDiversification_ = value;
}
// time limit per subtree
inline int timeLimit() const {
return timeLimit_;
}
// time limit per subtree
inline void setTimeLimit(int value) {
timeLimit_ = value;
}
// node limit for subtree
inline int nodeLimit() const {
return nodeLimit_;
}
// node limit for subtree
inline void setNodeLimit(int value) {
nodeLimit_ = value;
}
// Whether to do refinement step
inline bool refine() const {
return refine_;
}
// Whether to do refinement step
inline void setRefine(bool yesNo) {
refine_ = yesNo;
}
//@}
private:
// Node for local cuts
CbcNode * localNode_;
// best solution
double * bestSolution_;
// saved solution
double * savedSolution_;
// solution number at start of pass
int saveNumberSolutions_;
/* Cut. If zero size then no solution yet. Otherwise is left hand branch */
OsiRowCut cut_;
// This cut fixes all 0-1 variables
OsiRowCut fixedCut_;
// Model
CbcModel * model_;
// Original lower bounds
double * originalLower_;
// Original upper bounds
double * originalUpper_;
// range i.e. k
int range_;
// Type of cuts - 0=just 0-1, 1=all
int typeCuts_;
// maximum number of diversifications
int maxDiversification_;
// current diversification
int diversification_;
// Whether next will be strong diversification
bool nextStrong_;
// Current rhs
double rhs_;
// Save allowable gap
double savedGap_;
// Best solution
double bestCutoff_;
// time limit per subtree
int timeLimit_;
// time when subtree started
int startTime_;
// node limit for subtree
int nodeLimit_;
// node count when subtree started
int startNode_;
// -1 not started, 0 == stop on first solution, 1 don't stop on first, 2 refinement step
int searchType_;
// Whether to do refinement step
bool refine_;
};
#endif