Changeset 1118:ce1533650f7d in lemon-main

Timestamp:

07/07/14 15:40:12 (10 years ago)

Author:

Alpar Juttner <alpar@…>

Branch:

default

Parents:

1116:07cd9a2d20e0 (diff), 1117:b40c2bbb8da5 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Phase:

public

Message:

Merge bugfix #474

Files:

• lemon/network_simplex.h (modified) (1 diff)
• lemon/network_simplex.h (modified) (22 diffs)
• test/min_cost_flow_test.cc (modified) (1 diff)
• test/min_cost_flow_test.cc (modified) (1 diff)

lemon/network_simplex.h

@@ -937 +937 @@
         _node_id[n] = i;
       }
-      if (_arc_mixing) {
+      if (_arc_mixing && _node_num > 1) {
         // Store the arcs in a mixed order
         const int skip = std::max(_arc_num / _node_num, 3);

lemon/network_simplex.h

@@ -3 +3 @@
  * This file is a part of LEMON, a generic C++ optimization library.
  *
- * Copyright (C) 2003-2010
+ * Copyright (C) 2003-2013
  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
  * (Egervary Research Group on Combinatorial Optimization, EGRES).
@@ -42 +42 @@
   /// \ref NetworkSimplex implements the primal Network Simplex algorithm
   /// for finding a \ref min_cost_flow "minimum cost flow"
-  /// \ref amo93networkflows, \ref dantzig63linearprog,
-  /// \ref kellyoneill91netsimplex.
+  /// \cite amo93networkflows, \cite dantzig63linearprog,
+  /// \cite kellyoneill91netsimplex.
   /// This algorithm is a highly efficient specialized version of the
   /// linear programming simplex method directly for the minimum cost
   /// flow problem.
   ///
-  /// In general, %NetworkSimplex is the fastest implementation available
-  /// in LEMON for this problem.
-  /// Moreover, it supports both directions of the supply/demand inequality
-  /// constraints. For more information, see \ref SupplyType.
+  /// In general, \ref NetworkSimplex and \ref CostScaling are the fastest
+  /// implementations available in LEMON for solving this problem.
+  /// (For more information, see \ref min_cost_flow_algs "the module page".)
+  /// Furthermore, this class supports both directions of the supply/demand
+  /// inequality constraints. For more information, see \ref SupplyType.
   ///
   /// Most of the parameters of the problem (except for the digraph)
@@ -64 +65 @@
   /// algorithm. By default, it is the same as \c V.
   ///
-  /// \warning Both number types must be signed and all input data must
+  /// \warning Both \c V and \c C must be signed number types.
+  /// \warning All input data (capacities, supply values, and costs) must
   /// be integer.
   ///
@@ -122 +124 @@
     /// the \ref run() function.
     ///
-    /// \ref NetworkSimplex provides five different pivot rule
-    /// implementations that significantly affect the running time
+    /// \ref NetworkSimplex provides five different implementations for
+    /// the pivot strategy that significantly affects the running time
     /// of the algorithm.
-    /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
-    /// proved to be the most efficient and the most robust on various
-    /// test inputs.
-    /// However, another pivot rule can be selected using the \ref run()
-    /// function with the proper parameter.
+    /// According to experimental tests conducted on various problem
+    /// instances, \ref BLOCK_SEARCH "Block Search" and
+    /// \ref ALTERING_LIST "Altering Candidate List" rules turned out
+    /// to be the most efficient.
+    /// Since \ref BLOCK_SEARCH "Block Search" is a simpler strategy that
+    /// seemed to be slightly more robust, it is used by default.
+    /// However, another pivot rule can easily be selected using the
+    /// \ref run() function with the proper parameter.
     enum PivotRule {
 
@@ -155 +160 @@
       /// The \e Altering \e Candidate \e List pivot rule.
       /// It is a modified version of the Candidate List method.
-      /// It keeps only the several best eligible arcs from the former
+      /// It keeps only a few of the best eligible arcs from the former
       /// candidate list and extends this list in every iteration.
       ALTERING_LIST
@@ -168 +173 @@
     typedef std::vector<Cost> CostVector;
     typedef std::vector<signed char> CharVector;
-    // Note: vector<signed char> is used instead of vector<ArcState> and 
+    // Note: vector<signed char> is used instead of vector<ArcState> and
     // vector<ArcDirection> for efficiency reasons
 
@@ -194 +199 @@
 
     // Parameters of the problem
-    bool _have_lower;
+    bool _has_lower;
     SupplyType _stype;
     Value _sum_supply;
@@ -538 +543 @@
         SortFunc(const CostVector &map) : _map(map) {}
         bool operator()(int left, int right) {
-          return _map[left] > _map[right];
+          return _map[left] < _map[right];
         }
       };
@@ -556 +561 @@
         const double BLOCK_SIZE_FACTOR = 1.0;
         const int MIN_BLOCK_SIZE = 10;
-        const double HEAD_LENGTH_FACTOR = 0.1;
+        const double HEAD_LENGTH_FACTOR = 0.01;
         const int MIN_HEAD_LENGTH = 3;
 
@@ -600 +605 @@
         }
         for (e = 0; e != _next_arc; ++e) {
-          _cand_cost[e] = _state[e] *
-            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
-          if (_cand_cost[e] < 0) {
+          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
+          if (c < 0) {
+            _cand_cost[e] = c;
             _candidates[_curr_length++] = e;
           }
@@ -615 +620 @@
       search_end:
 
-        // 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
+        // Perform partial sort operation on the candidate list
+        int new_length = std::min(_head_length + 1, _curr_length);
+        std::partial_sort(_candidates.begin(), _candidates.begin() + new_length,
+                          _candidates.begin() + _curr_length, _sort_func);
+
+        // Select the entering arc and remove it from the list
         _in_arc = _candidates[0];
         _next_arc = e;
-        pop_heap( _candidates.begin(), _candidates.begin() + _curr_length,
-                  _sort_func );
-        _curr_length = std::min(_head_length, _curr_length - 1);
+        _candidates[0] = _candidates[new_length - 1];
+        _curr_length = new_length - 1;
         return true;
       }
@@ -678 +683 @@
     template <typename LowerMap>
     NetworkSimplex& lowerMap(const LowerMap& map) {
-      _have_lower = true;
+      _has_lower = true;
       for (ArcIt a(_graph); a != INVALID; ++a) {
         _lower[_arc_id[a]] = map[a];
@@ -735 +740 @@
     ///
     /// \return <tt>(*this)</tt>
+    ///
+    /// \sa supplyType()
     template<typename SupplyMap>
     NetworkSimplex& supplyMap(const SupplyMap& map) {
@@ -751 +758 @@
     ///
     /// Using this function has the same effect as using \ref supplyMap()
-    /// with such a map in which \c k is assigned to \c s, \c -k is
+    /// with a map in which \c k is assigned to \c s, \c -k is
     /// assigned to \c t and all other nodes have zero supply value.
     ///
@@ -873 +880 @@
         _cost[i] = 1;
       }
-      _have_lower = false;
+      _has_lower = false;
       _stype = GEQ;
       return *this;
@@ -967 +974 @@
     ///
     /// This function returns the total cost of the found flow.
-    /// Its complexity is O(e).
+    /// Its complexity is O(m).
     ///
     /// \note The return type of the function can be specified as a
@@ -1004 +1011 @@
     }
 
-    /// \brief Return the flow map (the primal solution).
+    /// \brief Copy the flow values (the primal solution) into the
+    /// given map.
     ///
     /// This function copies the flow value on each arc into the given
@@ -1028 +1036 @@
     }
 
-    /// \brief Return the potential map (the dual solution).
+    /// \brief Copy the potential values (the dual solution) into the
+    /// given map.
     ///
     /// This function copies the potential (dual value) of each node
@@ -1059 +1068 @@
              (_stype == LEQ && _sum_supply >= 0)) ) return false;
 
+      // Check lower and upper bounds
+      LEMON_DEBUG(checkBoundMaps(),
+          "Upper bounds must be greater or equal to the lower bounds");
+
       // Remove non-zero lower bounds
-      if (_have_lower) {
+      if (_has_lower) {
         for (int i = 0; i != _arc_num; ++i) {
           Value c = _lower[i];
@@ -1223 +1236 @@
     }
 
+    // Check if the upper bound is greater than or equal to the lower bound
+    // on each arc.
+    bool checkBoundMaps() {
+      for (int j = 0; j != _arc_num; ++j) {
+        if (_upper[j] < _lower[j]) return false;
+      }
+      return true;
+    }
+
     // Find the join node
     void findJoinNode() {
@@ -1495 +1517 @@
           }
         } else {
-          // Find the min. cost incomming arc for each demand node
+          // Find the min. cost incoming arc for each demand node
           for (int i = 0; i != int(demand_nodes.size()); ++i) {
             Node v = demand_nodes[i];
@@ -1591 +1613 @@
 
       // Transform the solution and the supply map to the original form
-      if (_have_lower) {
+      if (_has_lower) {
         for (int i = 0; i != _arc_num; ++i) {
           Value c = _lower[i];

test/min_cost_flow_test.cc

@@ -396 +396 @@
   checkMcf(mcf3, mcf3.run(param), neg2_gr, neg2_l, neg2_u, neg2_c, neg2_s,
            mcf3.OPTIMAL, true,     -300, test_str + "-18", GEQ);
+
+  // Tests for empty graph
+  Digraph gr0;
+  MCF mcf0(gr0);
+  mcf0.run(param);
+  check(mcf0.totalCost() == 0, "Wrong total cost");  
 }
 

test/min_cost_flow_test.cc

@@ -3 +3 @@
  * This file is a part of LEMON, a generic C++ optimization library.
  *
- * Copyright (C) 2003-2010
+ * Copyright (C) 2003-2013
  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
  * (Egervary Research Group on Combinatorial Optimization, EGRES).