Structure updated and intqpipopt files added

1 files changed, 372 insertions, 0 deletions

diff --git a/newstructure/thirdparty/linux/include/coin/CbcTreeLocal.hpp b/newstructure/thirdparty/linux/include/coin/CbcTreeLocal.hpp
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+/* $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
+