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alpar (Alpar Juttner)
alpar@cs.elte.hu
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Ignore white space 32 line context
... ...
@@ -74,34 +74,34 @@
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  /// can be given using separate functions, and the algorithm can be
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  /// executed using the \ref run() function. If some parameters are not
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  /// specified, then default values will be used.
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  ///
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  /// \tparam GR The digraph type the algorithm runs on.
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  /// \tparam V The number type used for flow amounts, capacity bounds
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  /// and supply values in the algorithm. By default, it is \c int.
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  /// \tparam C The number type used for costs and potentials in the
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  /// algorithm. By default, it is the same as \c V.
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  /// \tparam TR The traits class that defines various types used by the
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  /// algorithm. By default, it is \ref CapacityScalingDefaultTraits
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  /// "CapacityScalingDefaultTraits<GR, V, C>".
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  /// In most cases, this parameter should not be set directly,
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  /// consider to use the named template parameters instead.
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  ///
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  /// \warning Both \c V and \c C must be signed number types.
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  /// \warning All input data (capacities, supply values, and costs) must
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  /// be integer.
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  /// \warning Capacity bounds and supply values must be integer, but
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  /// arc costs can be arbitrary real numbers.
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  /// \warning This algorithm does not support negative costs for
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  /// arcs having infinite upper bound.
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#ifdef DOXYGEN
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  template <typename GR, typename V, typename C, typename TR>
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#else
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  template < typename GR, typename V = int, typename C = V,
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             typename TR = CapacityScalingDefaultTraits<GR, V, C> >
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#endif
100 100
  class CapacityScaling
101 101
  {
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  public:
103 103

	
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    /// The type of the digraph
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    typedef typename TR::Digraph Digraph;
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    /// The type of the flow amounts, capacity bounds and supply values
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    typedef typename TR::Value Value;
Ignore white space 32 line context
... ...
@@ -109,66 +109,69 @@
109 109
    /// selected using \ref supplyType().
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    /// The equality form is a special case of both supply types.
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    enum SupplyType {
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      /// This option means that there are <em>"greater or equal"</em>
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      /// supply/demand constraints in the definition of the problem.
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      GEQ,
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      /// This option means that there are <em>"less or equal"</em>
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      /// supply/demand constraints in the definition of the problem.
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      LEQ
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    };
119 119

	
120 120
    /// \brief Constants for selecting the pivot rule.
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    ///
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    /// Enum type containing constants for selecting the pivot rule for
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    /// the \ref run() function.
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    ///
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    /// \ref NetworkSimplex provides five different pivot rule
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    /// implementations that significantly affect the running time
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    /// \ref NetworkSimplex provides five different implementations for
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    /// the pivot strategy that significantly affects the running time
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    /// of the algorithm.
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    /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
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    /// turend out to be the most efficient and the most robust on various
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    /// test inputs.
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    /// However, another pivot rule can be selected using the \ref run()
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    /// function with the proper parameter.
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    /// According to experimental tests conducted on various problem
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    /// instances, \ref BLOCK_SEARCH "Block Search" and
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    /// \ref ALTERING_LIST "Altering Candidate List" rules turned out
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    /// to be the most efficient.
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    /// Since \ref BLOCK_SEARCH "Block Search" is a simpler strategy that
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    /// seemed to be slightly more robust, it is used by default.
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    /// However, another pivot rule can easily be selected using the
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    /// \ref run() function with the proper parameter.
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    enum PivotRule {
134 137

	
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      /// The \e First \e Eligible pivot rule.
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      /// The next eligible arc is selected in a wraparound fashion
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      /// in every iteration.
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      FIRST_ELIGIBLE,
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      /// The \e Best \e Eligible pivot rule.
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      /// The best eligible arc is selected in every iteration.
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      BEST_ELIGIBLE,
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      /// The \e Block \e Search pivot rule.
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      /// A specified number of arcs are examined in every iteration
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      /// in a wraparound fashion and the best eligible arc is selected
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      /// from this block.
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      BLOCK_SEARCH,
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      /// The \e Candidate \e List pivot rule.
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      /// In a major iteration a candidate list is built from eligible arcs
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      /// in a wraparound fashion and in the following minor iterations
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      /// the best eligible arc is selected from this list.
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      CANDIDATE_LIST,
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      /// The \e Altering \e Candidate \e List pivot rule.
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      /// It is a modified version of the Candidate List method.
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      /// It keeps only the several best eligible arcs from the former
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      /// It keeps only a few of the best eligible arcs from the former
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      /// candidate list and extends this list in every iteration.
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      ALTERING_LIST
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    };
162 165

	
163 166
  private:
164 167

	
165 168
    TEMPLATE_DIGRAPH_TYPEDEFS(GR);
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167 170
    typedef std::vector<int> IntVector;
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    typedef std::vector<Value> ValueVector;
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    typedef std::vector<Cost> CostVector;
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    typedef std::vector<signed char> CharVector;
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    // Note: vector<signed char> is used instead of vector<ArcState> and
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    // vector<ArcDirection> for efficiency reasons
173 176

	
174 177
    // State constants for arcs
... ...
@@ -525,51 +528,51 @@
525 528
      int _search_arc_num;
526 529

	
527 530
      // Pivot rule data
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      int _block_size, _head_length, _curr_length;
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      int _next_arc;
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      IntVector _candidates;
531 534
      CostVector _cand_cost;
532 535

	
533 536
      // Functor class to compare arcs during sort of the candidate list
534 537
      class SortFunc
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      {
536 539
      private:
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        const CostVector &_map;
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      public:
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        SortFunc(const CostVector &map) : _map(map) {}
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        bool operator()(int left, int right) {
541
          return _map[left] > _map[right];
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          return _map[left] < _map[right];
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        }
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      };
544 547

	
545 548
      SortFunc _sort_func;
546 549

	
547 550
    public:
548 551

	
549 552
      // Constructor
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      AlteringListPivotRule(NetworkSimplex &ns) :
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        _source(ns._source), _target(ns._target),
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        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
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        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
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        _next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost)
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      {
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        // The main parameters of the pivot rule
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        const double BLOCK_SIZE_FACTOR = 1.0;
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        const int MIN_BLOCK_SIZE = 10;
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        const double HEAD_LENGTH_FACTOR = 0.1;
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        const double HEAD_LENGTH_FACTOR = 0.01;
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        const int MIN_HEAD_LENGTH = 3;
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        _block_size = std::max( int(BLOCK_SIZE_FACTOR *
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                                    std::sqrt(double(_search_arc_num))),
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                                MIN_BLOCK_SIZE );
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        _head_length = std::max( int(HEAD_LENGTH_FACTOR * _block_size),
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                                 MIN_HEAD_LENGTH );
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        _candidates.resize(_head_length + _block_size);
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        _curr_length = 0;
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      }
570 573

	
571 574
      // Find next entering arc
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      bool findEnteringArc() {
573 576
        // Check the current candidate list
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        int e;
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        Cost c;
... ...
@@ -587,57 +590,57 @@
587 590
        int cnt = _block_size;
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        int limit = _head_length;
589 592

	
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        for (e = _next_arc; e != _search_arc_num; ++e) {
591 594
          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < 0) {
593 596
            _cand_cost[e] = c;
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            _candidates[_curr_length++] = e;
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          }
596 599
          if (--cnt == 0) {
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            if (_curr_length > limit) goto search_end;
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            limit = 0;
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            cnt = _block_size;
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          }
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        }
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        for (e = 0; e != _next_arc; ++e) {
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          _cand_cost[e] = _state[e] *
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            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (_cand_cost[e] < 0) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < 0) {
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            _cand_cost[e] = c;
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            _candidates[_curr_length++] = e;
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          }
608 611
          if (--cnt == 0) {
609 612
            if (_curr_length > limit) goto search_end;
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            limit = 0;
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            cnt = _block_size;
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          }
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        }
614 617
        if (_curr_length == 0) return false;
615 618

	
616 619
      search_end:
617 620

	
618
        // Make heap of the candidate list (approximating a partial sort)
619
        make_heap( _candidates.begin(), _candidates.begin() + _curr_length,
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                   _sort_func );
621
        // Perform partial sort operation on the candidate list
622
        int new_length = std::min(_head_length + 1, _curr_length);
623
        std::partial_sort(_candidates.begin(), _candidates.begin() + new_length,
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                          _candidates.begin() + _curr_length, _sort_func);
621 625

	
622
        // Pop the first element of the heap
626
        // Select the entering arc and remove it from the list
623 627
        _in_arc = _candidates[0];
624 628
        _next_arc = e;
625
        pop_heap( _candidates.begin(), _candidates.begin() + _curr_length,
626
                  _sort_func );
627
        _curr_length = std::min(_head_length, _curr_length - 1);
629
        _candidates[0] = _candidates[new_length - 1];
630
        _curr_length = new_length - 1;
628 631
        return true;
629 632
      }
630 633

	
631 634
    }; //class AlteringListPivotRule
632 635

	
633 636
  public:
634 637

	
635 638
    /// \brief Constructor.
636 639
    ///
637 640
    /// The constructor of the class.
638 641
    ///
639 642
    /// \param graph The digraph the algorithm runs on.
640 643
    /// \param arc_mixing Indicate if the arcs will be stored in a
641 644
    /// mixed order in the internal data structure.
642 645
    /// In general, it leads to similar performance as using the original
643 646
    /// arc order, but it makes the algorithm more robust and in special
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