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kpeter (Peter Kovacs)
kpeter@inf.elte.hu
More options for run() in scaling MCF algorithms (#180) - Three methods can be selected and the scaling factor can be given for CostScaling. - The scaling factor can be given for CapacityScaling.
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2 files changed with 71 insertions and 42 deletions:
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Show white space 6 line context
... ...
@@ -173,7 +173,7 @@
173 173
    IntVector _deficit_nodes;
174 174

	
175 175
    Value _delta;
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    int _phase_num;
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    int _factor;
177 177
    IntVector _pred;
178 178

	
179 179
  public:
... ...
@@ -513,12 +513,11 @@
513 513
    /// \ref reset() is called, thus only the modified parameters
514 514
    /// have to be set again. See \ref reset() for examples.
515 515
    /// However the underlying digraph must not be modified after this
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    /// class have been constructed, since it copies the digraph.
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    /// class have been constructed, since it copies and extends the graph.
517 517
    ///
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    /// \param scaling Enable or disable capacity scaling.
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    /// If the maximum upper bound and/or the amount of total supply
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    /// is rather small, the algorithm could be slightly faster without
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    /// scaling.
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    /// \param factor The capacity scaling factor. It must be larger than
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    /// one to use scaling. If it is less or equal to one, then scaling
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    /// will be disabled.
522 521
    ///
523 522
    /// \return \c INFEASIBLE if no feasible flow exists,
524 523
    /// \n \c OPTIMAL if the problem has optimal solution
... ...
@@ -531,8 +530,9 @@
531 530
    /// these cases.
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    ///
533 532
    /// \see ProblemType
534
    ProblemType run(bool scaling = true) {
535
      ProblemType pt = init(scaling);
533
    ProblemType run(int factor = 4) {
534
      _factor = factor;
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      ProblemType pt = init();
536 536
      if (pt != OPTIMAL) return pt;
537 537
      return start();
538 538
    }
... ...
@@ -546,7 +546,7 @@
546 546
    /// It is useful for multiple run() calls. If this function is not
547 547
    /// used, all the parameters given before are kept for the next
548 548
    /// \ref run() call.
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    /// However the underlying digraph must not be modified after this
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    /// However, the underlying digraph must not be modified after this
550 550
    /// class have been constructed, since it copies and extends the graph.
551 551
    ///
552 552
    /// For example,
... ...
@@ -677,7 +677,7 @@
677 677
  private:
678 678

	
679 679
    // Initialize the algorithm
680
    ProblemType init(bool scaling) {
680
    ProblemType init() {
681 681
      if (_node_num == 0) return INFEASIBLE;
682 682

	
683 683
      // Check the sum of supply values
... ...
@@ -758,7 +758,7 @@
758 758
      }
759 759

	
760 760
      // Initialize delta value
761
      if (scaling) {
761
      if (_factor > 1) {
762 762
        // With scaling
763 763
        Value max_sup = 0, max_dem = 0;
764 764
        for (int i = 0; i != _node_num; ++i) {
... ...
@@ -770,9 +770,7 @@
770 770
          if (_res_cap[j] > max_cap) max_cap = _res_cap[j];
771 771
        }
772 772
        max_sup = std::min(std::min(max_sup, max_dem), max_cap);
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        _phase_num = 0;
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        for (_delta = 1; 2 * _delta <= max_sup; _delta *= 2)
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          ++_phase_num;
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        for (_delta = 1; 2 * _delta <= max_sup; _delta *= 2) ;
776 774
      } else {
777 775
        // Without scaling
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        _delta = 1;
... ...
@@ -811,8 +809,6 @@
811 809
    ProblemType startWithScaling() {
812 810
      // Perform capacity scaling phases
813 811
      int s, t;
814
      int phase_cnt = 0;
815
      int factor = 4;
816 812
      ResidualDijkstra _dijkstra(*this);
817 813
      while (true) {
818 814
        // Saturate all arcs not satisfying the optimality condition
... ...
@@ -887,8 +883,7 @@
887 883
        }
888 884

	
889 885
        if (_delta == 1) break;
890
        if (++phase_cnt == _phase_num / 4) factor = 2;
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        _delta = _delta <= factor ? 1 : _delta / factor;
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        _delta = _delta <= _factor ? 1 : _delta / _factor;
892 887
      }
893 888

	
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      return OPTIMAL;
Show white space 6 line context
... ...
@@ -110,6 +110,10 @@
110 110
  /// be integer.
111 111
  /// \warning This algorithm does not support negative costs for such
112 112
  /// arcs that have infinite upper bound.
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  ///
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  /// \note %CostScaling provides three different internal methods,
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  /// from which the most efficient one is used by default.
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  /// For more information, see \ref Method.
113 117
#ifdef DOXYGEN
114 118
  template <typename GR, typename V, typename C, typename TR>
115 119
#else
... ...
@@ -159,6 +163,33 @@
159 163
      UNBOUNDED
160 164
    };
161 165

	
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    /// \brief Constants for selecting the internal method.
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    ///
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    /// Enum type containing constants for selecting the internal method
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    /// for the \ref run() function.
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    ///
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    /// \ref CostScaling provides three internal methods that differ mainly
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    /// in their base operations, which are used in conjunction with the
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    /// relabel operation.
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    /// By default, the so called \ref PARTIAL_AUGMENT
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    /// "Partial Augment-Relabel" method is used, which proved to be
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    /// the most efficient and the most robust on various test inputs.
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    /// However, the other methods can be selected using the \ref run()
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    /// function with the proper parameter.
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    enum Method {
180
      /// Local push operations are used, i.e. flow is moved only on one
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      /// admissible arc at once.
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      PUSH,
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      /// Augment operations are used, i.e. flow is moved on admissible
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      /// paths from a node with excess to a node with deficit.
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      AUGMENT,
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      /// Partial augment operations are used, i.e. flow is moved on 
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      /// admissible paths started from a node with excess, but the
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      /// lengths of these paths are limited. This method can be viewed
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      /// as a combined version of the previous two operations.
190
      PARTIAL_AUGMENT
191
    };
192

	
162 193
  private:
163 194

	
164 195
    TEMPLATE_DIGRAPH_TYPEDEFS(GR);
... ...
@@ -505,13 +536,12 @@
505 536
    /// that have been given are kept for the next call, unless
506 537
    /// \ref reset() is called, thus only the modified parameters
507 538
    /// have to be set again. See \ref reset() for examples.
508
    /// However the underlying digraph must not be modified after this
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    /// class have been constructed, since it copies the digraph.
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    /// However, the underlying digraph must not be modified after this
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    /// class have been constructed, since it copies and extends the graph.
510 541
    ///
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    /// \param partial_augment By default the algorithm performs
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    /// partial augment and relabel operations in the cost scaling
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    /// phases. Set this parameter to \c false for using local push and
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    /// relabel operations instead.
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    /// \param method The internal method that will be used in the
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    /// algorithm. For more information, see \ref Method.
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    /// \param factor The cost scaling factor. It must be larger than one.
515 545
    ///
516 546
    /// \return \c INFEASIBLE if no feasible flow exists,
517 547
    /// \n \c OPTIMAL if the problem has optimal solution
... ...
@@ -523,11 +553,12 @@
523 553
    /// bounded over the feasible flows, but this algroithm cannot handle
524 554
    /// these cases.
525 555
    ///
526
    /// \see ProblemType
527
    ProblemType run(bool partial_augment = true) {
556
    /// \see ProblemType, Method
557
    ProblemType run(Method method = PARTIAL_AUGMENT, int factor = 8) {
558
      _alpha = factor;
528 559
      ProblemType pt = init();
529 560
      if (pt != OPTIMAL) return pt;
530
      start(partial_augment);
561
      start(method);
531 562
      return OPTIMAL;
532 563
    }
533 564

	
... ...
@@ -681,9 +712,6 @@
681 712
    ProblemType init() {
682 713
      if (_res_node_num == 0) return INFEASIBLE;
683 714

	
684
      // Scaling factor
685
      _alpha = 8;
686

	
687 715
      // Check the sum of supply values
688 716
      _sum_supply = 0;
689 717
      for (int i = 0; i != _root; ++i) {
... ...
@@ -817,12 +845,21 @@
817 845
    }
818 846

	
819 847
    // Execute the algorithm and transform the results
820
    void start(bool partial_augment) {
848
    void start(Method method) {
849
      // Maximum path length for partial augment
850
      const int MAX_PATH_LENGTH = 4;
851
      
821 852
      // Execute the algorithm
822
      if (partial_augment) {
823
        startPartialAugment();
824
      } else {
825
        startPushRelabel();
853
      switch (method) {
854
        case PUSH:
855
          startPush();
856
          break;
857
        case AUGMENT:
858
          startAugment();
859
          break;
860
        case PARTIAL_AUGMENT:
861
          startAugment(MAX_PATH_LENGTH);
862
          break;
826 863
      }
827 864

	
828 865
      // Compute node potentials for the original costs
... ...
@@ -851,14 +888,11 @@
851 888
      }
852 889
    }
853 890

	
854
    /// Execute the algorithm performing partial augmentation and
855
    /// relabel operations
856
    void startPartialAugment() {
891
    /// Execute the algorithm performing augment and relabel operations
892
    void startAugment(int max_length = std::numeric_limits<int>::max()) {
857 893
      // Paramters for heuristics
858 894
      const int BF_HEURISTIC_EPSILON_BOUND = 1000;
859 895
      const int BF_HEURISTIC_BOUND_FACTOR  = 3;
860
      // Maximum augment path length
861
      const int MAX_PATH_LENGTH = 4;
862 896

	
863 897
      // Perform cost scaling phases
864 898
      IntVector pred_arc(_res_node_num);
... ...
@@ -925,7 +959,7 @@
925 959
          // Find an augmenting path from the start node
926 960
          int tip = start;
927 961
          while (_excess[tip] >= 0 &&
928
                 int(path_nodes.size()) <= MAX_PATH_LENGTH) {
962
                 int(path_nodes.size()) <= max_length) {
929 963
            int u;
930 964
            LargeCost min_red_cost, rc;
931 965
            int last_out = _sum_supply < 0 ?
... ...
@@ -984,7 +1018,7 @@
984 1018
    }
985 1019

	
986 1020
    /// Execute the algorithm performing push and relabel operations
987
    void startPushRelabel() {
1021
    void startPush() {
988 1022
      // Paramters for heuristics
989 1023
      const int BF_HEURISTIC_EPSILON_BOUND = 1000;
990 1024
      const int BF_HEURISTIC_BOUND_FACTOR  = 3;
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