/* $Id$ */ // Copyright (C) 2005, International Business Machines // Corporation and others. All Rights Reserved. // This code is licensed under the terms of the Eclipse Public License (EPL). #ifndef CbcHeuristicGreedy_H #define CbcHeuristicGreedy_H #include "CbcHeuristic.hpp" /** Greedy heuristic classes */ class CbcHeuristicGreedyCover : public CbcHeuristic { public: // Default Constructor CbcHeuristicGreedyCover(); /* Constructor with model - assumed before cuts Initial version does not do Lps */ CbcHeuristicGreedyCover(CbcModel &model); // Copy constructor CbcHeuristicGreedyCover(const CbcHeuristicGreedyCover &); // Destructor ~CbcHeuristicGreedyCover(); /// Clone virtual CbcHeuristic *clone() const; /// Assignment operator CbcHeuristicGreedyCover &operator=(const CbcHeuristicGreedyCover &rhs); /// Create C++ lines to get to current state virtual void generateCpp(FILE *fp); /// update model (This is needed if cliques update matrix etc) virtual void setModel(CbcModel *model); using CbcHeuristic::solution; /** returns 0 if no solution, 1 if valid solution. Sets solution values if good, sets objective value (only if good) We leave all variables which are at one at this node of the tree to that value and will initially set all others to zero. We then sort all variables in order of their cost divided by the number of entries in rows which are not yet covered. We randomize that value a bit so that ties will be broken in different ways on different runs of the heuristic. We then choose the best one and set it to one and repeat the exercise. */ virtual int solution(double &objectiveValue, double *newSolution); /// Validate model i.e. sets when_ to 0 if necessary (may be NULL) virtual void validate(); /// Resets stuff if model changes virtual void resetModel(CbcModel *model); /* Algorithm 0 - use current upper bounds 1 - use original upper bounds If 10 added perturb ratios more if 100 added round up all >=0.5 */ inline int algorithm() const { return algorithm_; } inline void setAlgorithm(int value) { algorithm_ = value; } // Only do this many times inline int numberTimes() const { return numberTimes_; } inline void setNumberTimes(int value) { numberTimes_ = value; } protected: /// Guts of constructor from a CbcModel void gutsOfConstructor(CbcModel *model); // Data // Original matrix by column CoinPackedMatrix matrix_; // original number of rows int originalNumberRows_; /* Algorithm 0 - use current upper bounds 1 - use original upper bounds If 10 added perturb ratios more */ int algorithm_; /// Do this many times int numberTimes_; }; class CbcHeuristicGreedyEquality : public CbcHeuristic { public: // Default Constructor CbcHeuristicGreedyEquality(); /* Constructor with model - assumed before cuts Initial version does not do Lps */ CbcHeuristicGreedyEquality(CbcModel &model); // Copy constructor CbcHeuristicGreedyEquality(const CbcHeuristicGreedyEquality &); // Destructor ~CbcHeuristicGreedyEquality(); /// Clone virtual CbcHeuristic *clone() const; /// Assignment operator CbcHeuristicGreedyEquality &operator=(const CbcHeuristicGreedyEquality &rhs); /// Create C++ lines to get to current state virtual void generateCpp(FILE *fp); /// update model (This is needed if cliques update matrix etc) virtual void setModel(CbcModel *model); using CbcHeuristic::solution; /** returns 0 if no solution, 1 if valid solution. Sets solution values if good, sets objective value (only if good) We leave all variables which are at one at this node of the tree to that value and will initially set all others to zero. We then sort all variables in order of their cost divided by the number of entries in rows which are not yet covered. We randomize that value a bit so that ties will be broken in different ways on different runs of the heuristic. We then choose the best one and set it to one and repeat the exercise. */ virtual int solution(double &objectiveValue, double *newSolution); /// Validate model i.e. sets when_ to 0 if necessary (may be NULL) virtual void validate(); /// Resets stuff if model changes virtual void resetModel(CbcModel *model); /* Algorithm 0 - use current upper bounds 1 - use original upper bounds If 10 added perturb ratios more if 100 added round up all >=0.5 */ inline int algorithm() const { return algorithm_; } inline void setAlgorithm(int value) { algorithm_ = value; } // Fraction of rhs to cover before branch and cut inline void setFraction(double value) { fraction_ = value; } inline double fraction() const { return fraction_; } // Only do this many times inline int numberTimes() const { return numberTimes_; } inline void setNumberTimes(int value) { numberTimes_ = value; } protected: /// Guts of constructor from a CbcModel void gutsOfConstructor(CbcModel *model); // Data // Original matrix by column CoinPackedMatrix matrix_; // Fraction of rhs to cover before branch and cut double fraction_; // original number of rows int originalNumberRows_; /* Algorithm 0 - use current upper bounds 1 - use original upper bounds If 10 added perturb ratios more */ int algorithm_; /// Do this many times int numberTimes_; }; /** Greedy heuristic for SOS and L rows (and positive elements) */ class CbcHeuristicGreedySOS : public CbcHeuristic { public: // Default Constructor CbcHeuristicGreedySOS(); /* Constructor with model - assumed before cuts Initial version does not do Lps */ CbcHeuristicGreedySOS(CbcModel &model); // Copy constructor CbcHeuristicGreedySOS(const CbcHeuristicGreedySOS &); // Destructor ~CbcHeuristicGreedySOS(); /// Clone virtual CbcHeuristic *clone() const; /// Assignment operator CbcHeuristicGreedySOS &operator=(const CbcHeuristicGreedySOS &rhs); /// Create C++ lines to get to current state virtual void generateCpp(FILE *fp); /// update model (This is needed if cliques update matrix etc) virtual void setModel(CbcModel *model); using CbcHeuristic::solution; /** returns 0 if no solution, 1 if valid solution. Sets solution values if good, sets objective value (only if good) We leave all variables which are at one at this node of the tree to that value and will initially set all others to zero. We then sort all variables in order of their cost divided by the number of entries in rows which are not yet covered. We randomize that value a bit so that ties will be broken in different ways on different runs of the heuristic. We then choose the best one and set it to one and repeat the exercise. */ virtual int solution(double &objectiveValue, double *newSolution); /// Validate model i.e. sets when_ to 0 if necessary (may be NULL) virtual void validate(); /// Resets stuff if model changes virtual void resetModel(CbcModel *model); /* Algorithm Bits 1 bit - use current model, otherwise original 2 - use current solution as starting point, otherwise pure greedy 4 - as 2 but use merit not merit/size 8 - use duals to modify greedy 16 - use duals on GUB/SOS in special way */ inline int algorithm() const { return algorithm_; } inline void setAlgorithm(int value) { algorithm_ = value; } // Only do this many times inline int numberTimes() const { return numberTimes_; } inline void setNumberTimes(int value) { numberTimes_ = value; } protected: /// Guts of constructor from a CbcModel void gutsOfConstructor(CbcModel *model); // Data // Original RHS - if -1.0 then SOS otherwise <= value double *originalRhs_; // Original matrix by column CoinPackedMatrix matrix_; // original number of rows int originalNumberRows_; /* Algorithm */ int algorithm_; /// Do this many times int numberTimes_; }; #endif /* vi: softtabstop=2 shiftwidth=2 expandtab tabstop=2 */