diff -r 70b199792735 -r ad40f7d32846 lemon/network_simplex.h
--- a/lemon/network_simplex.h	Fri Aug 09 11:07:27 2013 +0200
+++ b/lemon/network_simplex.h	Sun Aug 11 15:28:12 2013 +0200
@@ -2,7 +2,7 @@
  *
  * This file is a part of LEMON, a generic C++ optimization library.
  *
- * Copyright (C) 2003-2009
+ * Copyright (C) 2003-2010
  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
  * (Egervary Research Group on Combinatorial Optimization, EGRES).
  *
@@ -40,15 +40,17 @@
   /// for finding a \ref min_cost_flow "minimum cost flow".
   ///
   /// \ref NetworkSimplex implements the primal Network Simplex algorithm
-  /// for finding a \ref min_cost_flow "minimum cost flow".
-  /// This algorithm is a specialized version of the linear programming
-  /// simplex method directly for the minimum cost flow problem.
-  /// It is one of the most efficient solution methods.
+  /// for finding a \ref min_cost_flow "minimum cost flow"
+  /// \ref amo93networkflows, \ref dantzig63linearprog,
+  /// \ref kellyoneill91netsimplex.
+  /// This algorithm is a highly efficient specialized version of the
+  /// linear programming simplex method directly for the minimum cost
+  /// flow problem.
   ///
-  /// In general this class is the fastest implementation available
-  /// in LEMON for the minimum cost flow problem.
-  /// Moreover it supports both directions of the supply/demand inequality
-  /// constraints. For more information see \ref SupplyType.
+  /// 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.
   ///
   /// Most of the parameters of the problem (except for the digraph)
   /// can be given using separate functions, and the algorithm can be
@@ -56,17 +58,17 @@
   /// specified, then default values will be used.
   ///
   /// \tparam GR The digraph type the algorithm runs on.
-  /// \tparam V The value type used for flow amounts, capacity bounds
-  /// and supply values in the algorithm. By default it is \c int.
-  /// \tparam C The value type used for costs and potentials in the
-  /// algorithm. By default it is the same as \c V.
+  /// \tparam V The number type used for flow amounts, capacity bounds
+  /// and supply values in the algorithm. By default, it is \c int.
+  /// \tparam C The number type used for costs and potentials in the
+  /// algorithm. By default, it is the same as \c V.
   ///
-  /// \warning Both value types must be signed and all input data must
+  /// \warning Both number types must be signed and all input data must
   /// be integer.
   ///
   /// \note %NetworkSimplex provides five different pivot rule
   /// implementations, from which the most efficient one is used
-  /// by default. For more information see \ref PivotRule.
+  /// by default. For more information, see \ref PivotRule.
   template <typename GR, typename V = int, typename C = V>
   class NetworkSimplex
   {
@@ -95,7 +97,7 @@
       /// infinite upper bound.
       UNBOUNDED
     };
-    
+
     /// \brief Constants for selecting the type of the supply constraints.
     ///
     /// Enum type containing constants for selecting the supply type,
@@ -113,7 +115,7 @@
       /// supply/demand constraints in the definition of the problem.
       LEQ
     };
-    
+
     /// \brief Constants for selecting the pivot rule.
     ///
     /// Enum type containing constants for selecting the pivot rule for
@@ -122,59 +124,62 @@
     /// \ref NetworkSimplex provides five different pivot rule
     /// implementations that significantly affect the running time
     /// of the algorithm.
-    /// By default \ref BLOCK_SEARCH "Block Search" is used, which
+    /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
     /// proved to be the most efficient and the most robust on various
-    /// test inputs according to our benchmark tests.
-    /// However another pivot rule can be selected using the \ref run()
+    /// test inputs.
+    /// However, another pivot rule can be selected using the \ref run()
     /// function with the proper parameter.
     enum PivotRule {
 
-      /// The First Eligible pivot rule.
+      /// The \e First \e Eligible pivot rule.
       /// The next eligible arc is selected in a wraparound fashion
       /// in every iteration.
       FIRST_ELIGIBLE,
 
-      /// The Best Eligible pivot rule.
+      /// The \e Best \e Eligible pivot rule.
       /// The best eligible arc is selected in every iteration.
       BEST_ELIGIBLE,
 
-      /// The Block Search pivot rule.
+      /// The \e Block \e Search pivot rule.
       /// A specified number of arcs are examined in every iteration
       /// in a wraparound fashion and the best eligible arc is selected
       /// from this block.
       BLOCK_SEARCH,
 
-      /// The Candidate List pivot rule.
+      /// The \e Candidate \e List pivot rule.
       /// In a major iteration a candidate list is built from eligible arcs
       /// in a wraparound fashion and in the following minor iterations
       /// the best eligible arc is selected from this list.
       CANDIDATE_LIST,
 
-      /// The Altering Candidate List pivot rule.
+      /// 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
       /// candidate list and extends this list in every iteration.
       ALTERING_LIST
     };
-    
+
   private:
 
     TEMPLATE_DIGRAPH_TYPEDEFS(GR);
 
-    typedef std::vector<Arc> ArcVector;
-    typedef std::vector<Node> NodeVector;
     typedef std::vector<int> IntVector;
-    typedef std::vector<bool> BoolVector;
     typedef std::vector<Value> ValueVector;
     typedef std::vector<Cost> CostVector;
+    typedef std::vector<char> BoolVector;
+    // Note: vector<char> is used instead of vector<bool> for efficiency reasons
 
     // State constants for arcs
-    enum ArcStateEnum {
+    enum ArcState {
       STATE_UPPER = -1,
       STATE_TREE  =  0,
       STATE_LOWER =  1
     };
 
+    typedef std::vector<signed char> StateVector;
+    // Note: vector<signed char> is used instead of vector<ArcState> for
+    // efficiency reasons
+
   private:
 
     // Data related to the underlying digraph
@@ -194,6 +199,7 @@
     IntArcMap _arc_id;
     IntVector _source;
     IntVector _target;
+    bool _arc_mixing;
 
     // Node and arc data
     ValueVector _lower;
@@ -213,7 +219,7 @@
     IntVector _last_succ;
     IntVector _dirty_revs;
     BoolVector _forward;
-    IntVector _state;
+    StateVector _state;
     int _root;
 
     // Temporary data used in the current pivot iteration
@@ -222,8 +228,10 @@
     int stem, par_stem, new_stem;
     Value delta;
 
+    const Value MAX;
+
   public:
-  
+
     /// \brief Constant for infinite upper bounds (capacities).
     ///
     /// Constant for infinite upper bounds (capacities).
@@ -242,7 +250,7 @@
       const IntVector  &_source;
       const IntVector  &_target;
       const CostVector &_cost;
-      const IntVector  &_state;
+      const StateVector &_state;
       const CostVector &_pi;
       int &_in_arc;
       int _search_arc_num;
@@ -263,7 +271,7 @@
       // Find next entering arc
       bool findEnteringArc() {
         Cost c;
-        for (int e = _next_arc; e < _search_arc_num; ++e) {
+        for (int e = _next_arc; e != _search_arc_num; ++e) {
           c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
           if (c < 0) {
             _in_arc = e;
@@ -271,7 +279,7 @@
             return true;
           }
         }
-        for (int e = 0; e < _next_arc; ++e) {
+        for (int e = 0; e != _next_arc; ++e) {
           c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
           if (c < 0) {
             _in_arc = e;
@@ -294,7 +302,7 @@
       const IntVector  &_source;
       const IntVector  &_target;
       const CostVector &_cost;
-      const IntVector  &_state;
+      const StateVector &_state;
       const CostVector &_pi;
       int &_in_arc;
       int _search_arc_num;
@@ -311,7 +319,7 @@
       // Find next entering arc
       bool findEnteringArc() {
         Cost c, min = 0;
-        for (int e = 0; e < _search_arc_num; ++e) {
+        for (int e = 0; e != _search_arc_num; ++e) {
           c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
           if (c < min) {
             min = c;
@@ -333,7 +341,7 @@
       const IntVector  &_source;
       const IntVector  &_target;
       const CostVector &_cost;
-      const IntVector  &_state;
+      const StateVector &_state;
       const CostVector &_pi;
       int &_in_arc;
       int _search_arc_num;
@@ -352,7 +360,7 @@
         _next_arc(0)
       {
         // The main parameters of the pivot rule
-        const double BLOCK_SIZE_FACTOR = 0.5;
+        const double BLOCK_SIZE_FACTOR = 1.0;
         const int MIN_BLOCK_SIZE = 10;
 
         _block_size = std::max( int(BLOCK_SIZE_FACTOR *
@@ -364,33 +372,32 @@
       bool findEnteringArc() {
         Cost c, min = 0;
         int cnt = _block_size;
-        int e, min_arc = _next_arc;
-        for (e = _next_arc; e < _search_arc_num; ++e) {
+        int e;
+        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;
-            min_arc = e;
+            _in_arc = e;
           }
           if (--cnt == 0) {
-            if (min < 0) break;
+            if (min < 0) goto search_end;
             cnt = _block_size;
           }
         }
-        if (min == 0 || cnt > 0) {
-          for (e = 0; e < _next_arc; ++e) {
-            c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
-            if (c < min) {
-              min = c;
-              min_arc = e;
-            }
-            if (--cnt == 0) {
-              if (min < 0) break;
-              cnt = _block_size;
-            }
+        for (e = 0; e != _next_arc; ++e) {
+          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
+          if (c < min) {
+            min = c;
+            _in_arc = e;
+          }
+          if (--cnt == 0) {
+            if (min < 0) goto search_end;
+            cnt = _block_size;
           }
         }
         if (min >= 0) return false;
-        _in_arc = min_arc;
+
+      search_end:
         _next_arc = e;
         return true;
       }
@@ -407,7 +414,7 @@
       const IntVector  &_source;
       const IntVector  &_target;
       const CostVector &_cost;
-      const IntVector  &_state;
+      const StateVector &_state;
       const CostVector &_pi;
       int &_in_arc;
       int _search_arc_num;
@@ -428,7 +435,7 @@
         _next_arc(0)
       {
         // The main parameters of the pivot rule
-        const double LIST_LENGTH_FACTOR = 1.0;
+        const double LIST_LENGTH_FACTOR = 0.25;
         const int MIN_LIST_LENGTH = 10;
         const double MINOR_LIMIT_FACTOR = 0.1;
         const int MIN_MINOR_LIMIT = 3;
@@ -445,7 +452,7 @@
       /// Find next entering arc
       bool findEnteringArc() {
         Cost min, c;
-        int e, min_arc = _next_arc;
+        int e;
         if (_curr_length > 0 && _minor_count < _minor_limit) {
           // Minor iteration: select the best eligible arc from the
           // current candidate list
@@ -456,48 +463,44 @@
             c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
             if (c < min) {
               min = c;
-              min_arc = e;
+              _in_arc = e;
             }
-            if (c >= 0) {
+            else if (c >= 0) {
               _candidates[i--] = _candidates[--_curr_length];
             }
           }
-          if (min < 0) {
-            _in_arc = min_arc;
-            return true;
-          }
+          if (min < 0) return true;
         }
 
         // Major iteration: build a new candidate list
         min = 0;
         _curr_length = 0;
-        for (e = _next_arc; e < _search_arc_num; ++e) {
+        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;
-              min_arc = e;
+              _in_arc = e;
             }
-            if (_curr_length == _list_length) break;
+            if (_curr_length == _list_length) goto search_end;
           }
         }
-        if (_curr_length < _list_length) {
-          for (e = 0; e < _next_arc; ++e) {
-            c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
-            if (c < 0) {
-              _candidates[_curr_length++] = e;
-              if (c < min) {
-                min = c;
-                min_arc = e;
-              }
-              if (_curr_length == _list_length) break;
+        for (e = 0; e != _next_arc; ++e) {
+          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
+          if (c < 0) {
+            _candidates[_curr_length++] = e;
+            if (c < min) {
+              min = c;
+              _in_arc = e;
             }
+            if (_curr_length == _list_length) goto search_end;
           }
         }
         if (_curr_length == 0) return false;
+
+      search_end:
         _minor_count = 1;
-        _in_arc = min_arc;
         _next_arc = e;
         return true;
       }
@@ -514,7 +517,7 @@
       const IntVector  &_source;
       const IntVector  &_target;
       const CostVector &_cost;
-      const IntVector  &_state;
+      const StateVector &_state;
       const CostVector &_pi;
       int &_in_arc;
       int _search_arc_num;
@@ -549,7 +552,7 @@
         _next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost)
       {
         // The main parameters of the pivot rule
-        const double BLOCK_SIZE_FACTOR = 1.5;
+        const double BLOCK_SIZE_FACTOR = 1.0;
         const int MIN_BLOCK_SIZE = 10;
         const double HEAD_LENGTH_FACTOR = 0.1;
         const int MIN_HEAD_LENGTH = 3;
@@ -567,7 +570,7 @@
       bool findEnteringArc() {
         // Check the current candidate list
         int e;
-        for (int i = 0; i < _curr_length; ++i) {
+        for (int i = 0; i != _curr_length; ++i) {
           e = _candidates[i];
           _cand_cost[e] = _state[e] *
             (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
@@ -578,39 +581,35 @@
 
         // Extend the list
         int cnt = _block_size;
-        int last_arc = 0;
         int limit = _head_length;
 
-        for (int e = _next_arc; e < _search_arc_num; ++e) {
+        for (e = _next_arc; e != _search_arc_num; ++e) {
           _cand_cost[e] = _state[e] *
             (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
           if (_cand_cost[e] < 0) {
             _candidates[_curr_length++] = e;
-            last_arc = e;
           }
           if (--cnt == 0) {
-            if (_curr_length > limit) break;
+            if (_curr_length > limit) goto search_end;
             limit = 0;
             cnt = _block_size;
           }
         }
-        if (_curr_length <= limit) {
-          for (int e = 0; e < _next_arc; ++e) {
-            _cand_cost[e] = _state[e] *
-              (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
-            if (_cand_cost[e] < 0) {
-              _candidates[_curr_length++] = e;
-              last_arc = e;
-            }
-            if (--cnt == 0) {
-              if (_curr_length > limit) break;
-              limit = 0;
-              cnt = _block_size;
-            }
+        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) {
+            _candidates[_curr_length++] = e;
+          }
+          if (--cnt == 0) {
+            if (_curr_length > limit) goto search_end;
+            limit = 0;
+            cnt = _block_size;
           }
         }
         if (_curr_length == 0) return false;
-        _next_arc = last_arc + 1;
+
+      search_end:
 
         // Make heap of the candidate list (approximating a partial sort)
         make_heap( _candidates.begin(), _candidates.begin() + _curr_length,
@@ -618,6 +617,7 @@
 
         // Pop the first element of the heap
         _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);
@@ -633,69 +633,25 @@
     /// The constructor of the class.
     ///
     /// \param graph The digraph the algorithm runs on.
-    NetworkSimplex(const GR& graph) :
+    /// \param arc_mixing Indicate if the arcs have to be stored in a
+    /// mixed order in the internal data structure.
+    /// In special cases, it could lead to better overall performance,
+    /// but it is usually slower. Therefore it is disabled by default.
+    NetworkSimplex(const GR& graph, bool arc_mixing = false) :
       _graph(graph), _node_id(graph), _arc_id(graph),
+      _arc_mixing(arc_mixing),
+      MAX(std::numeric_limits<Value>::max()),
       INF(std::numeric_limits<Value>::has_infinity ?
-          std::numeric_limits<Value>::infinity() :
-          std::numeric_limits<Value>::max())
+          std::numeric_limits<Value>::infinity() : MAX)
     {
-      // Check the value types
+      // Check the number types
       LEMON_ASSERT(std::numeric_limits<Value>::is_signed,
         "The flow type of NetworkSimplex must be signed");
       LEMON_ASSERT(std::numeric_limits<Cost>::is_signed,
         "The cost type of NetworkSimplex must be signed");
-        
-      // Resize vectors
-      _node_num = countNodes(_graph);
-      _arc_num = countArcs(_graph);
-      int all_node_num = _node_num + 1;
-      int max_arc_num = _arc_num + 2 * _node_num;
 
-      _source.resize(max_arc_num);
-      _target.resize(max_arc_num);
-
-      _lower.resize(_arc_num);
-      _upper.resize(_arc_num);
-      _cap.resize(max_arc_num);
-      _cost.resize(max_arc_num);
-      _supply.resize(all_node_num);
-      _flow.resize(max_arc_num);
-      _pi.resize(all_node_num);
-
-      _parent.resize(all_node_num);
-      _pred.resize(all_node_num);
-      _forward.resize(all_node_num);
-      _thread.resize(all_node_num);
-      _rev_thread.resize(all_node_num);
-      _succ_num.resize(all_node_num);
-      _last_succ.resize(all_node_num);
-      _state.resize(max_arc_num);
-
-      // Copy the graph (store the arcs in a mixed order)
-      int i = 0;
-      for (NodeIt n(_graph); n != INVALID; ++n, ++i) {
-        _node_id[n] = i;
-      }
-      int k = std::max(int(std::sqrt(double(_arc_num))), 10);
-      i = 0;
-      for (ArcIt a(_graph); a != INVALID; ++a) {
-        _arc_id[a] = i;
-        _source[i] = _node_id[_graph.source(a)];
-        _target[i] = _node_id[_graph.target(a)];
-        if ((i += k) >= _arc_num) i = (i % k) + 1;
-      }
-      
-      // Initialize maps
-      for (int i = 0; i != _node_num; ++i) {
-        _supply[i] = 0;
-      }
-      for (int i = 0; i != _arc_num; ++i) {
-        _lower[i] = 0;
-        _upper[i] = INF;
-        _cost[i] = 1;
-      }
-      _have_lower = false;
-      _stype = GEQ;
+      // Reset data structures
+      reset();
     }
 
     /// \name Parameters
@@ -729,7 +685,7 @@
     /// This function sets the upper bounds (capacities) on the arcs.
     /// If it is not used before calling \ref run(), the upper bounds
     /// will be set to \ref INF on all arcs (i.e. the flow value will be
-    /// unbounded from above on each arc).
+    /// unbounded from above).
     ///
     /// \param map An arc map storing the upper bounds.
     /// Its \c Value type must be convertible to the \c Value type
@@ -768,7 +724,6 @@
     /// This function sets the supply values of the nodes.
     /// If neither this function nor \ref stSupply() is used before
     /// calling \ref run(), the supply of each node will be set to zero.
-    /// (It makes sense only if non-zero lower bounds are given.)
     ///
     /// \param map A node map storing the supply values.
     /// Its \c Value type must be convertible to the \c Value type
@@ -789,7 +744,6 @@
     /// and the required flow value.
     /// If neither this function nor \ref supplyMap() is used before
     /// calling \ref run(), the supply of each node will be set to zero.
-    /// (It makes sense only if non-zero lower bounds are given.)
     ///
     /// 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
@@ -809,14 +763,14 @@
       _supply[_node_id[t]] = -k;
       return *this;
     }
-    
+
     /// \brief Set the type of the supply constraints.
     ///
     /// This function sets the type of the supply/demand constraints.
     /// If it is not used before calling \ref run(), the \ref GEQ supply
     /// type will be used.
     ///
-    /// For more information see \ref SupplyType.
+    /// For more information, see \ref SupplyType.
     ///
     /// \return <tt>(*this)</tt>
     NetworkSimplex& supplyType(SupplyType supply_type) {
@@ -835,7 +789,7 @@
     ///
     /// This function runs the algorithm.
     /// The paramters can be specified using functions \ref lowerMap(),
-    /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), 
+    /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(),
     /// \ref supplyType().
     /// For example,
     /// \code
@@ -844,15 +798,15 @@
     ///     .supplyMap(sup).run();
     /// \endcode
     ///
-    /// This function can be called more than once. All the parameters
-    /// that have been given are kept for the next call, unless
-    /// \ref reset() is called, thus only the modified parameters
-    /// have to be set again. See \ref reset() for examples.
-    /// However the underlying digraph must not be modified after this
-    /// class have been constructed, since it copies and extends the graph.
+    /// This function can be called more than once. All the given parameters
+    /// are kept for the next call, unless \ref resetParams() or \ref reset()
+    /// is used, thus only the modified parameters have to be set again.
+    /// If the underlying digraph was also modified after the construction
+    /// of the class (or the last \ref reset() call), then the \ref reset()
+    /// function must be called.
     ///
     /// \param pivot_rule The pivot rule that will be used during the
-    /// algorithm. For more information see \ref PivotRule.
+    /// algorithm. For more information, see \ref PivotRule.
     ///
     /// \return \c INFEASIBLE if no feasible flow exists,
     /// \n \c OPTIMAL if the problem has optimal solution
@@ -863,6 +817,7 @@
     /// cost and infinite upper bound.
     ///
     /// \see ProblemType, PivotRule
+    /// \see resetParams(), reset()
     ProblemType run(PivotRule pivot_rule = BLOCK_SEARCH) {
       if (!init()) return INFEASIBLE;
       return start(pivot_rule);
@@ -874,11 +829,12 @@
     /// before using functions \ref lowerMap(), \ref upperMap(),
     /// \ref costMap(), \ref supplyMap(), \ref stSupply(), \ref supplyType().
     ///
-    /// It is useful for multiple run() calls. If this function is not
-    /// used, all the parameters given before are kept for the next
-    /// \ref run() call.
-    /// However the underlying digraph must not be modified after this
-    /// class have been constructed, since it copies and extends the graph.
+    /// It is useful for multiple \ref run() calls. Basically, all the given
+    /// parameters are kept for the next \ref run() call, unless
+    /// \ref resetParams() or \ref reset() is used.
+    /// If the underlying digraph was also modified after the construction
+    /// of the class or the last \ref reset() call, then the \ref reset()
+    /// function must be used, otherwise \ref resetParams() is sufficient.
     ///
     /// For example,
     /// \code
@@ -888,20 +844,22 @@
     ///   ns.lowerMap(lower).upperMap(upper).costMap(cost)
     ///     .supplyMap(sup).run();
     ///
-    ///   // Run again with modified cost map (reset() is not called,
+    ///   // Run again with modified cost map (resetParams() is not called,
     ///   // so only the cost map have to be set again)
     ///   cost[e] += 100;
     ///   ns.costMap(cost).run();
     ///
-    ///   // Run again from scratch using reset()
+    ///   // Run again from scratch using resetParams()
     ///   // (the lower bounds will be set to zero on all arcs)
-    ///   ns.reset();
+    ///   ns.resetParams();
     ///   ns.upperMap(capacity).costMap(cost)
     ///     .supplyMap(sup).run();
     /// \endcode
     ///
     /// \return <tt>(*this)</tt>
-    NetworkSimplex& reset() {
+    ///
+    /// \see reset(), run()
+    NetworkSimplex& resetParams() {
       for (int i = 0; i != _node_num; ++i) {
         _supply[i] = 0;
       }
@@ -915,6 +873,83 @@
       return *this;
     }
 
+    /// \brief Reset the internal data structures and all the parameters
+    /// that have been given before.
+    ///
+    /// This function resets the internal data structures and all the
+    /// paramaters that have been given before using functions \ref lowerMap(),
+    /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(),
+    /// \ref supplyType().
+    ///
+    /// It is useful for multiple \ref run() calls. Basically, all the given
+    /// parameters are kept for the next \ref run() call, unless
+    /// \ref resetParams() or \ref reset() is used.
+    /// If the underlying digraph was also modified after the construction
+    /// of the class or the last \ref reset() call, then the \ref reset()
+    /// function must be used, otherwise \ref resetParams() is sufficient.
+    ///
+    /// See \ref resetParams() for examples.
+    ///
+    /// \return <tt>(*this)</tt>
+    ///
+    /// \see resetParams(), run()
+    NetworkSimplex& reset() {
+      // Resize vectors
+      _node_num = countNodes(_graph);
+      _arc_num = countArcs(_graph);
+      int all_node_num = _node_num + 1;
+      int max_arc_num = _arc_num + 2 * _node_num;
+
+      _source.resize(max_arc_num);
+      _target.resize(max_arc_num);
+
+      _lower.resize(_arc_num);
+      _upper.resize(_arc_num);
+      _cap.resize(max_arc_num);
+      _cost.resize(max_arc_num);
+      _supply.resize(all_node_num);
+      _flow.resize(max_arc_num);
+      _pi.resize(all_node_num);
+
+      _parent.resize(all_node_num);
+      _pred.resize(all_node_num);
+      _forward.resize(all_node_num);
+      _thread.resize(all_node_num);
+      _rev_thread.resize(all_node_num);
+      _succ_num.resize(all_node_num);
+      _last_succ.resize(all_node_num);
+      _state.resize(max_arc_num);
+
+      // Copy the graph
+      int i = 0;
+      for (NodeIt n(_graph); n != INVALID; ++n, ++i) {
+        _node_id[n] = i;
+      }
+      if (_arc_mixing) {
+        // Store the arcs in a mixed order
+        int k = std::max(int(std::sqrt(double(_arc_num))), 10);
+        int i = 0, j = 0;
+        for (ArcIt a(_graph); a != INVALID; ++a) {
+          _arc_id[a] = i;
+          _source[i] = _node_id[_graph.source(a)];
+          _target[i] = _node_id[_graph.target(a)];
+          if ((i += k) >= _arc_num) i = ++j;
+        }
+      } else {
+        // Store the arcs in the original order
+        int i = 0;
+        for (ArcIt a(_graph); a != INVALID; ++a, ++i) {
+          _arc_id[a] = i;
+          _source[i] = _node_id[_graph.source(a)];
+          _target[i] = _node_id[_graph.target(a)];
+        }
+      }
+
+      // Reset parameters
+      resetParams();
+      return *this;
+    }
+
     /// @}
 
     /// \name Query Functions
@@ -1024,9 +1059,9 @@
         for (int i = 0; i != _arc_num; ++i) {
           Value c = _lower[i];
           if (c >= 0) {
-            _cap[i] = _upper[i] < INF ? _upper[i] - c : INF;
+            _cap[i] = _upper[i] < MAX ? _upper[i] - c : INF;
           } else {
-            _cap[i] = _upper[i] < INF + c ? _upper[i] - c : INF;
+            _cap[i] = _upper[i] < MAX + c ? _upper[i] - c : INF;
           }
           _supply[_source[i]] -= c;
           _supply[_target[i]] += c;
@@ -1054,7 +1089,7 @@
         _flow[i] = 0;
         _state[i] = STATE_LOWER;
       }
-      
+
       // Set data for the artificial root node
       _root = _node_num;
       _parent[_root] = -1;
@@ -1218,7 +1253,7 @@
       for (int u = first; u != join; u = _parent[u]) {
         e = _pred[u];
         d = _forward[u] ?
-          _flow[e] : (_cap[e] == INF ? INF : _cap[e] - _flow[e]);
+          _flow[e] : (_cap[e] >= MAX ? INF : _cap[e] - _flow[e]);
         if (d < delta) {
           delta = d;
           u_out = u;
@@ -1228,8 +1263,8 @@
       // Search the cycle along the path form the second node to the root
       for (int u = second; u != join; u = _parent[u]) {
         e = _pred[u];
-        d = _forward[u] ? 
-          (_cap[e] == INF ? INF : _cap[e] - _flow[e]) : _flow[e];
+        d = _forward[u] ?
+          (_cap[e] >= MAX ? INF : _cap[e] - _flow[e]) : _flow[e];
         if (d <= delta) {
           delta = d;
           u_out = u;
@@ -1330,7 +1365,7 @@
       }
 
       // Update _rev_thread using the new _thread values
-      for (int i = 0; i < int(_dirty_revs.size()); ++i) {
+      for (int i = 0; i != int(_dirty_revs.size()); ++i) {
         u = _dirty_revs[i];
         _rev_thread[_thread[u]] = u;
       }
@@ -1402,6 +1437,100 @@
       }
     }
 
+    // Heuristic initial pivots
+    bool initialPivots() {
+      Value curr, total = 0;
+      std::vector<Node> supply_nodes, demand_nodes;
+      for (NodeIt u(_graph); u != INVALID; ++u) {
+        curr = _supply[_node_id[u]];
+        if (curr > 0) {
+          total += curr;
+          supply_nodes.push_back(u);
+        }
+        else if (curr < 0) {
+          demand_nodes.push_back(u);
+        }
+      }
+      if (_sum_supply > 0) total -= _sum_supply;
+      if (total <= 0) return true;
+
+      IntVector arc_vector;
+      if (_sum_supply >= 0) {
+        if (supply_nodes.size() == 1 && demand_nodes.size() == 1) {
+          // Perform a reverse graph search from the sink to the source
+          typename GR::template NodeMap<bool> reached(_graph, false);
+          Node s = supply_nodes[0], t = demand_nodes[0];
+          std::vector<Node> stack;
+          reached[t] = true;
+          stack.push_back(t);
+          while (!stack.empty()) {
+            Node u, v = stack.back();
+            stack.pop_back();
+            if (v == s) break;
+            for (InArcIt a(_graph, v); a != INVALID; ++a) {
+              if (reached[u = _graph.source(a)]) continue;
+              int j = _arc_id[a];
+              if (_cap[j] >= total) {
+                arc_vector.push_back(j);
+                reached[u] = true;
+                stack.push_back(u);
+              }
+            }
+          }
+        } else {
+          // Find the min. cost incomming arc for each demand node
+          for (int i = 0; i != int(demand_nodes.size()); ++i) {
+            Node v = demand_nodes[i];
+            Cost c, min_cost = std::numeric_limits<Cost>::max();
+            Arc min_arc = INVALID;
+            for (InArcIt a(_graph, v); a != INVALID; ++a) {
+              c = _cost[_arc_id[a]];
+              if (c < min_cost) {
+                min_cost = c;
+                min_arc = a;
+              }
+            }
+            if (min_arc != INVALID) {
+              arc_vector.push_back(_arc_id[min_arc]);
+            }
+          }
+        }
+      } else {
+        // Find the min. cost outgoing arc for each supply node
+        for (int i = 0; i != int(supply_nodes.size()); ++i) {
+          Node u = supply_nodes[i];
+          Cost c, min_cost = std::numeric_limits<Cost>::max();
+          Arc min_arc = INVALID;
+          for (OutArcIt a(_graph, u); a != INVALID; ++a) {
+            c = _cost[_arc_id[a]];
+            if (c < min_cost) {
+              min_cost = c;
+              min_arc = a;
+            }
+          }
+          if (min_arc != INVALID) {
+            arc_vector.push_back(_arc_id[min_arc]);
+          }
+        }
+      }
+
+      // Perform heuristic initial pivots
+      for (int i = 0; i != int(arc_vector.size()); ++i) {
+        in_arc = arc_vector[i];
+        if (_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -
+            _pi[_target[in_arc]]) >= 0) continue;
+        findJoinNode();
+        bool change = findLeavingArc();
+        if (delta >= MAX) return false;
+        changeFlow(change);
+        if (change) {
+          updateTreeStructure();
+          updatePotential();
+        }
+      }
+      return true;
+    }
+
     // Execute the algorithm
     ProblemType start(PivotRule pivot_rule) {
       // Select the pivot rule implementation
@@ -1424,18 +1553,21 @@
     ProblemType start() {
       PivotRuleImpl pivot(*this);
 
+      // Perform heuristic initial pivots
+      if (!initialPivots()) return UNBOUNDED;
+
       // Execute the Network Simplex algorithm
       while (pivot.findEnteringArc()) {
         findJoinNode();
         bool change = findLeavingArc();
-        if (delta >= INF) return UNBOUNDED;
+        if (delta >= MAX) return UNBOUNDED;
         changeFlow(change);
         if (change) {
           updateTreeStructure();
           updatePotential();
         }
       }
-      
+
       // Check feasibility
       for (int e = _search_arc_num; e != _all_arc_num; ++e) {
         if (_flow[e] != 0) return INFEASIBLE;
@@ -1452,7 +1584,7 @@
           }
         }
       }
-      
+
       // Shift potentials to meet the requirements of the GEQ/LEQ type
       // optimality conditions
       if (_sum_supply == 0) {