RhodeCode

      }

      // Find next entering arc

      bool findEnteringArc() {

        Cost c, min = 0;

        int cnt = _block_size;

        for (e = _next_arc; e < _search_arc_num; ++e) {

          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);

          if (c < min) {

            min = c;

          }

          if (--cnt == 0) {

            cnt = _block_size;

          }

        }

          }

        }

        if (min >= 0) return false;

        _next_arc = e;

        return true;

      }

    }; //class BlockSearchPivotRule

        _source(ns._source), _target(ns._target),

        _cost(ns._cost), _state(ns._state), _pi(ns._pi),

        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),

        _next_arc(0)

      {

        // The main parameters of the pivot rule

        const int MIN_LIST_LENGTH = 10;

        const double MINOR_LIMIT_FACTOR = 0.1;

        const int MIN_MINOR_LIMIT = 3;

        _list_length = std::max( int(LIST_LENGTH_FACTOR *

                                     std::sqrt(double(_search_arc_num))),

        _candidates.resize(_list_length);

      }

      /// Find next entering arc

      bool findEnteringArc() {

        Cost min, c;

        if (_curr_length > 0 && _minor_count < _minor_limit) {

          // Minor iteration: select the best eligible arc from the

          // current candidate list

          ++_minor_count;

          min = 0;

          for (int i = 0; i < _curr_length; ++i) {

            e = _candidates[i];

            c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);

            if (c < min) {

              min = c;

            }

              _candidates[i--] = _candidates[--_curr_length];

            }

          }

        }

        // Major iteration: build a new candidate list

        min = 0;

        _curr_length = 0;

        for (e = _next_arc; e < _search_arc_num; ++e) {

          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);

          if (c < 0) {

            _candidates[_curr_length++] = e;

            if (c < min) {

              min = c;

            }

          }

        }

            }

          }

        }

        if (_curr_length == 0) return false;

        _minor_count = 1;

        _next_arc = e;

        return true;

      }

    }; //class CandidateListPivotRule

        _source(ns._source), _target(ns._target),

        _cost(ns._cost), _state(ns._state), _pi(ns._pi),

        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),

        _next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost)

      {

        // The main parameters of the pivot rule

        const int MIN_BLOCK_SIZE = 10;

        const double HEAD_LENGTH_FACTOR = 0.1;

        const int MIN_HEAD_LENGTH = 3;

        _block_size = std::max( int(BLOCK_SIZE_FACTOR *

                                    std::sqrt(double(_search_arc_num))),

            _candidates[i--] = _candidates[--_curr_length];

          }

        }

        // Extend the list

        int cnt = _block_size;

        int limit = _head_length;

          _cand_cost[e] = _state[e] *

            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);

          if (_cand_cost[e] < 0) {

            _candidates[_curr_length++] = e;

          }

          if (--cnt == 0) {

            limit = 0;

            cnt = _block_size;

          }

        }

          }

        }

        if (_curr_length == 0) return false;

        // Make heap of the candidate list (approximating a partial sort)

        make_heap( _candidates.begin(), _candidates.begin() + _curr_length,

                   _sort_func );

        // Pop the first element of the heap

        _in_arc = _candidates[0];

        pop_heap( _candidates.begin(), _candidates.begin() + _curr_length,

                  _sort_func );

        _curr_length = std::min(_head_length, _curr_length - 1);

        return true;

      }