1.1 --- a/lemon/network_simplex.h Sun Jan 29 22:33:14 2012 +0100
1.2 +++ b/lemon/network_simplex.h Mon Jan 30 20:21:45 2012 +0100
1.3 @@ -122,14 +122,17 @@
1.4 /// Enum type containing constants for selecting the pivot rule for
1.5 /// the \ref run() function.
1.6 ///
1.7 - /// \ref NetworkSimplex provides five different pivot rule
1.8 - /// implementations that significantly affect the running time
1.9 + /// \ref NetworkSimplex provides five different implementations for
1.10 + /// the pivot strategy that significantly affects the running time
1.11 /// of the algorithm.
1.12 - /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
1.13 - /// turend out to be the most efficient and the most robust on various
1.14 - /// test inputs.
1.15 - /// However, another pivot rule can be selected using the \ref run()
1.16 - /// function with the proper parameter.
1.17 + /// According to experimental tests conducted on various problem
1.18 + /// instances, \ref BLOCK_SEARCH "Block Search" and
1.19 + /// \ref ALTERING_LIST "Altering Candidate List" rules turned out
1.20 + /// to be the most efficient.
1.21 + /// Since \ref BLOCK_SEARCH "Block Search" is a simpler strategy that
1.22 + /// seemed to be slightly more robust, it is used by default.
1.23 + /// However, another pivot rule can easily be selected using the
1.24 + /// \ref run() function with the proper parameter.
1.25 enum PivotRule {
1.26
1.27 /// The \e First \e Eligible pivot rule.
1.28 @@ -155,7 +158,7 @@
1.29
1.30 /// The \e Altering \e Candidate \e List pivot rule.
1.31 /// It is a modified version of the Candidate List method.
1.32 - /// It keeps only the several best eligible arcs from the former
1.33 + /// It keeps only a few of the best eligible arcs from the former
1.34 /// candidate list and extends this list in every iteration.
1.35 ALTERING_LIST
1.36 };
1.37 @@ -538,7 +541,7 @@
1.38 public:
1.39 SortFunc(const CostVector &map) : _map(map) {}
1.40 bool operator()(int left, int right) {
1.41 - return _map[left] > _map[right];
1.42 + return _map[left] < _map[right];
1.43 }
1.44 };
1.45
1.46 @@ -556,7 +559,7 @@
1.47 // The main parameters of the pivot rule
1.48 const double BLOCK_SIZE_FACTOR = 1.0;
1.49 const int MIN_BLOCK_SIZE = 10;
1.50 - const double HEAD_LENGTH_FACTOR = 0.1;
1.51 + const double HEAD_LENGTH_FACTOR = 0.01;
1.52 const int MIN_HEAD_LENGTH = 3;
1.53
1.54 _block_size = std::max( int(BLOCK_SIZE_FACTOR *
1.55 @@ -600,9 +603,9 @@
1.56 }
1.57 }
1.58 for (e = 0; e != _next_arc; ++e) {
1.59 - _cand_cost[e] = _state[e] *
1.60 - (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
1.61 - if (_cand_cost[e] < 0) {
1.62 + c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
1.63 + if (c < 0) {
1.64 + _cand_cost[e] = c;
1.65 _candidates[_curr_length++] = e;
1.66 }
1.67 if (--cnt == 0) {
1.68 @@ -615,16 +618,16 @@
1.69
1.70 search_end:
1.71
1.72 - // Make heap of the candidate list (approximating a partial sort)
1.73 - make_heap( _candidates.begin(), _candidates.begin() + _curr_length,
1.74 - _sort_func );
1.75 + // Perform partial sort operation on the candidate list
1.76 + int new_length = std::min(_head_length + 1, _curr_length);
1.77 + std::partial_sort(_candidates.begin(), _candidates.begin() + new_length,
1.78 + _candidates.begin() + _curr_length, _sort_func);
1.79
1.80 - // Pop the first element of the heap
1.81 + // Select the entering arc and remove it from the list
1.82 _in_arc = _candidates[0];
1.83 _next_arc = e;
1.84 - pop_heap( _candidates.begin(), _candidates.begin() + _curr_length,
1.85 - _sort_func );
1.86 - _curr_length = std::min(_head_length, _curr_length - 1);
1.87 + _candidates[0] = _candidates[new_length - 1];
1.88 + _curr_length = new_length - 1;
1.89 return true;
1.90 }
1.91