MaxCPlanarSub.h

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#pragma once
 
#include <ogdf/basic/ArrayBuffer.h>
#include <ogdf/cluster/internal/MaxCPlanarMaster.h>
#include <ogdf/planarity/BoyerMyrvold.h>
 
#include <ogdf/lib/abacus/standardpool.h>
#include <ogdf/lib/abacus/sub.h>
 
namespace ogdf {
namespace cluster_planarity {
 
class MaxCPlanarSub : public abacus::Sub {
public:
    explicit MaxCPlanarSub(abacus::Master* master);
    MaxCPlanarSub(abacus::Master* master, abacus::Sub* father, abacus::BranchRule* branchRule,
            List<abacus::Constraint*>& criticalConstraints);
 
    virtual ~MaxCPlanarSub();
 
    // Creation of a child-node in the Branch&Bound tree according to the
    // branching rule #rule
    virtual abacus::Sub* generateSon(abacus::BranchRule* rule) override;
 
 
protected:
    // Checks if the current solution of the LP relaxation is also a feasible solution to the ILP,
    // i.e. Integer-feasible + satisfying all Kuratowski- and Cut-constraints.
    virtual bool feasible() override;
 
    // to trick Sub::solveLp...
    virtual int makeFeasible() override { return 0; }
#if 0
    // to trick Sub::solveLp into returning 2
    virtual int removeNonLiftableCons() { return 0; }
#endif
    int repair();
 
    virtual int optimize() override {
        Logger::slout() << "OPTIMIZE BEGIN\tNode=" << this->id() << "\n";
        int ret = abacus::Sub::optimize();
        Logger::slout() << "OPTIMIZE END\tNode=" << this->id() << " db=" << dualBound()
                        << "\tReturn=" << (ret ? "(error)" : "(ok)") << "\n";
        return ret;
    }
 
    //functions for testing properties of the clustered graph
 
    bool checkCConnectivity(const GraphCopy& support);
    bool checkCConnectivityOld(const GraphCopy& support);
 
    inline node getRepresentative(node v, NodeArray<node>& parent) {
        while (v != parent[v]) {
            v = parent[v];
        }
        return v;
    }
 
    // Todo: Think about putting this into extended_graph_alg.h to make it
    // publically available
    int clusterBags(ClusterGraph& CG, cluster c);
 
#if 0
    // using the below two functions iunstead (and instead of solveLp) we would get a more traditional situation
    virtual int separate() {
        return separateRealO(0.001);
    }
    virtual int pricing()  {
        return pricingRealO(0.001);
    }
#endif
 
    int separateReal(double minViolate);
#if 0
    int pricingReal(double minViolate);
#endif
 
    inline int separateRealO(double minViolate) {
        Logger::slout() << "\tSeparate (minViolate=" << minViolate << ")..";
        int r = separateReal(minViolate);
        Logger::slout() << "..done: " << r << "\n";
        return r;
    }
#if 0
    inline int pricingRealO(double minViolate) {
        Logger::slout() << "\tPricing (minViolate=" << minViolate << ")..";
        int r = pricingReal(minViolate);
        master()->m_varsPrice += r;
        Logger::slout() << "..done: " << r << "\n";
        return r;
    }
#endif
 
    virtual int separate() override {
        Logger::slout()
                << "\tReporting Separation: " << ((m_reportCreation > 0) ? m_reportCreation : 0)
                << "\n";
        return (m_reportCreation > 0) ? m_reportCreation : 0;
    }
 
    virtual int pricing() override {
        if (inOrigSolveLp) {
            return 1;
        }
        Logger::slout()
                << "\tReporting Prizing: " << ((m_reportCreation < 0) ? -m_reportCreation : 0)
                << "\n";
        return (m_reportCreation < 0) ? -m_reportCreation : 0;
    }
 
    virtual int solveLp() override;
 
    // Implementation of virtual function improve() inherited from ABA::SUB.
    // Invokes the function heuristicImprovePrimalBound().
    // Tthis function belongs to the ABACUS framework. It is invoked after each separation-step.
    // Since the heuristic is rather time consuming and because it is not very advantageous
    // to run the heuristic even if additional constraints have been found, the heuristic
    // is run "by hand" each time no further constraints coud be found, i.e. after having solved
    // the LP-relaxation optimally.
    virtual int improve(double& primalValue) override;
 
    // Two functions inherited from ABACUS and overritten to manipulate the branching behaviour.
    virtual int selectBranchingVariableCandidates(ArrayBuffer<int>& candidates) override;
    virtual int selectBranchingVariable(int& variable) override;
 
    inline int addPoolCons(ArrayBuffer<abacus::Constraint*>& cons,
            abacus::StandardPool<abacus::Constraint, abacus::Variable>* pool) {
        return (master()->useDefaultCutPool()) ? addCons(cons) : addCons(cons, pool);
    }
 
    inline int separateCutPool(abacus::StandardPool<abacus::Constraint, abacus::Variable>* pool,
            double minViolation) {
        return (master()->useDefaultCutPool()) ? 0 : constraintPoolSeparation(0, pool, minViolation);
    }
 
private:
    MaxCPlanarMaster* master() const { return static_cast<MaxCPlanarMaster*>(master_); }
 
    // A flag indicating if constraints have been found in the current separation step.
    // Is used to check, if the primal heuristic should be run or not.
    bool m_constraintsFound;
    bool detectedInfeasibility;
    bool inOrigSolveLp;
    double realDualBound;
 
    // used for the steering in solveLp
    int m_reportCreation;
    bool m_sepFirst;
    List<abacus::Constraint*> criticalSinceBranching;
    ArrayBuffer<abacus::Constraint*> bufferedForCreation;
    int createVariablesForBufferedConstraints();
 
    void myAddVars(ArrayBuffer<abacus::Variable*>& b) {
        int num = b.size();
        ArrayBuffer<bool> keep(num, false);
        for (int i = num; i-- > 0;) {
            keep.push(true);
        }
#ifdef OGDF_DEBUG
        int r =
#endif
                addVars(b, nullptr, &keep);
        OGDF_ASSERT(r == num);
    }
 
    // Computes the support graph for Kuratowski- constraints according to the current LP-solution.
    // Parameter \p low defines a lower threshold, i.e all edges having value at most \p low
    // are not added to the support graph.
    // Parameter \p high defines an upper threshold, i.e all edges having value at least \p high,
    // are added to the support graph.
    // Edges having LP-value k that lies between \p low and \p high are added to the support graph
    // randomly with a probability of k.
    void kuratowskiSupportGraph(GraphCopy& support, double low, double high);
 
    // Computes the support graph for Connectivity- constraints according to the current LP-solution.
    void connectivitySupportGraph(GraphCopy& support, EdgeArray<double>& weight);
 
    // Computes the integer solution induced Graph.
    void intSolutionInducedGraph(GraphCopy& support);
 
    // Computes and returns the value of the lefthand side of the Kuratowski constraint
    // induced by \p it and given support graph \p gc.
    double subdivisionLefthandSide(SListConstIterator<KuratowskiWrapper> it, GraphCopy* gc);
 
    // Updates the best known solution according to integer solution of this subproblem.
    void updateSolution();
 
 
    // The following four functions are used by the primal heuristic.
 
    int getArrayIndex(double lpValue);
 
    void childClusterSpanningTree(GraphCopy& GC, List<edgeValue>& clusterEdges,
            List<NodePair>& MSTEdges);
 
    void clusterSpanningTree(ClusterGraph& C, cluster c, ClusterArray<List<NodePair>>& treeEdges,
            ClusterArray<List<edgeValue>>& clusterEdges);
 
    // Tries to improve the best primal solution by means of the current fractional LP-solution.
    double heuristicImprovePrimalBound(List<NodePair>& originalEdges,
            List<NodePair>& connectionEdges, List<edge>& deletedEdges);
 
    inline int addCutCons(ArrayBuffer<abacus::Constraint*> cons) {
        return addPoolCons(cons, static_cast<MaxCPlanarMaster*>(master_)->getCutConnPool());
    }
 
    inline int addKuraCons(ArrayBuffer<abacus::Constraint*> cons) {
        return addPoolCons(cons, static_cast<MaxCPlanarMaster*>(master_)->getCutKuraPool());
    }
 
    //tries to regenerate connectivity cuts
    inline int separateConnPool(double minViolation) {
        return separateCutPool(static_cast<MaxCPlanarMaster*>(master_)->getCutConnPool(),
                minViolation);
    }
 
    //tries to regenerate kuratowski cuts
    inline int separateKuraPool(double minViolation) {
        return separateCutPool(static_cast<MaxCPlanarMaster*>(master_)->getCutKuraPool(),
                minViolation);
    }
#if 0
    void minimumSpanningTree(
        GraphCopy &GC,
        List<NodePair> &clusterEdges,
        List<NodePair> &MSTEdges);
 
    void recursiveMinimumSpanningTree(
        ClusterGraph &C,
        cluster c,
        ClusterArray<List<NodePair> > &treeEdges,
        List<NodePair> &edgesByIncLPValue,
        List<node> &clusterNodes);
 
    double heuristicImprovePrimalBoundDet(
        List<NodePair> &origEdges,
        List<NodePair> &conEdges,
        List<NodePair> &delEdges);
#endif
};
 
}
}