lemon/preflow.h
changeset 2517 d9cfac072869
parent 2473 9ffff9051a4b
child 2518 4c0a23bd70b5
equal deleted inserted replaced
22:11c5b26a5ef0 23:bf8b77df6350
    17  */
    17  */
    18 
    18 
    19 #ifndef LEMON_PREFLOW_H
    19 #ifndef LEMON_PREFLOW_H
    20 #define LEMON_PREFLOW_H
    20 #define LEMON_PREFLOW_H
    21 
    21 
    22 #include <vector>
       
    23 #include <queue>
       
    24 
       
    25 #include <lemon/error.h>
    22 #include <lemon/error.h>
    26 #include <lemon/bits/invalid.h>
    23 #include <lemon/bits/invalid.h>
    27 #include <lemon/tolerance.h>
    24 #include <lemon/tolerance.h>
    28 #include <lemon/maps.h>
    25 #include <lemon/maps.h>
    29 #include <lemon/graph_utils.h>
    26 #include <lemon/graph_utils.h>
       
    27 #include <lemon/elevator.h>
    30 
    28 
    31 /// \file
    29 /// \file
    32 /// \ingroup max_flow
    30 /// \ingroup max_flow
    33 /// \brief Implementation of the preflow algorithm.
    31 /// \brief Implementation of the preflow algorithm.
    34 
    32 
    35 namespace lemon {
    33 namespace lemon { 
    36 
    34   
    37   ///\ingroup max_flow
    35   /// \brief Default traits class of Preflow class.
    38   ///\brief %Preflow algorithms class.
       
    39   ///
    36   ///
    40   ///This class provides an implementation of the \e preflow \e
    37   /// Default traits class of Preflow class.
    41   ///algorithm producing a flow of maximum value in a directed
    38   /// \param _Graph Graph type.
    42   ///graph. The preflow algorithms are the fastest known max flow algorithms. 
    39   /// \param _CapacityMap Type of capacity map.
    43   ///The \e source node, the \e target node, the \e
    40   template <typename _Graph, typename _CapacityMap>
    44   ///capacity of the edges and the \e starting \e flow value of the
    41   struct PreflowDefaultTraits {
    45   ///edges should be passed to the algorithm through the
    42 
    46   ///constructor. It is possible to change these quantities using the
    43     /// \brief The graph type the algorithm runs on. 
    47   ///functions \ref source, \ref target, \ref capacityMap and \ref
    44     typedef _Graph Graph;
    48   ///flowMap.
    45 
       
    46     /// \brief The type of the map that stores the edge capacities.
       
    47     ///
       
    48     /// The type of the map that stores the edge capacities.
       
    49     /// It must meet the \ref concepts::ReadMap "ReadMap" concept.
       
    50     typedef _CapacityMap CapacityMap;
       
    51 
       
    52     /// \brief The type of the length of the edges.
       
    53     typedef typename CapacityMap::Value Value;
       
    54 
       
    55     /// \brief The map type that stores the flow values.
       
    56     ///
       
    57     /// The map type that stores the flow values. 
       
    58     /// It must meet the \ref concepts::ReadWriteMap "ReadWriteMap" concept.
       
    59     typedef typename Graph::template EdgeMap<Value> FlowMap;
       
    60 
       
    61     /// \brief Instantiates a FlowMap.
       
    62     ///
       
    63     /// This function instantiates a \ref FlowMap. 
       
    64     /// \param graph The graph, to which we would like to define the flow map.
       
    65     static FlowMap* createFlowMap(const Graph& graph) {
       
    66       return new FlowMap(graph);
       
    67     }
       
    68 
       
    69     /// \brief The eleavator type used by Preflow algorithm.
       
    70     /// 
       
    71     /// The elevator type used by Preflow algorithm.
       
    72     ///
       
    73     /// \sa Elevator
       
    74     /// \sa LinkedElevator
       
    75     typedef LinkedElevator<Graph, typename Graph::Node> Elevator;
       
    76     
       
    77     /// \brief Instantiates an Elevator.
       
    78     ///
       
    79     /// This function instantiates a \ref Elevator. 
       
    80     /// \param graph The graph, to which we would like to define the elevator.
       
    81     /// \param max_level The maximum level of the elevator.
       
    82     static Elevator* createElevator(const Graph& graph, int max_level) {
       
    83       return new Elevator(graph, max_level);
       
    84     }
       
    85 
       
    86     /// \brief The tolerance used by the algorithm
       
    87     ///
       
    88     /// The tolerance used by the algorithm to handle inexact computation.
       
    89     typedef Tolerance<Value> Tolerance;
       
    90 
       
    91   };
       
    92   
       
    93 
       
    94   /// \ingroup max_flow
    49   ///
    95   ///
    50   ///After running \ref lemon::Preflow::phase1() "phase1()"
    96   /// \brief %Preflow algorithms class.
    51   ///or \ref lemon::Preflow::run() "run()", the maximal flow
       
    52   ///value can be obtained by calling \ref flowValue(). The minimum
       
    53   ///value cut can be written into a <tt>bool</tt> node map by
       
    54   ///calling \ref minCut(). (\ref minMinCut() and \ref maxMinCut() writes
       
    55   ///the inclusionwise minimum and maximum of the minimum value cuts,
       
    56   ///resp.)
       
    57   ///
    97   ///
    58   ///\param Graph The directed graph type the algorithm runs on.
    98   /// This class provides an implementation of the Goldberg's \e
    59   ///\param Num The number type of the capacities and the flow values.
    99   /// preflow \e algorithm producing a flow of maximum value in a
    60   ///\param CapacityMap The capacity map type.
   100   /// directed graph. The preflow algorithms are the fastest known max
    61   ///\param FlowMap The flow map type.
   101   /// flow algorithms. The current implementation use a mixture of the
    62   ///\param Tol The tolerance type. 
   102   /// \e "highest label" and the \e "bound decrease" heuristics.
       
   103   /// The worst case time complexity of the algorithm is \f$O(n^3)\f$.
    63   ///
   104   ///
    64   ///\author Jacint Szabo 
   105   /// The algorithm consists from two phases. After the first phase
    65   ///\todo Second template parameter is superfluous
   106   /// the maximal flow value and the minimum cut can be obtained. The
    66   template <typename Graph, typename Num,
   107   /// second phase constructs the feasible maximum flow on each edge.
    67 	    typename CapacityMap=typename Graph::template EdgeMap<Num>,
   108   ///
    68             typename FlowMap=typename Graph::template EdgeMap<Num>,
   109   /// \param _Graph The directed graph type the algorithm runs on.
    69 	    typename Tol=Tolerance<Num> >
   110   /// \param _CapacityMap The flow map type.
       
   111   /// \param _Traits Traits class to set various data types used by
       
   112   /// the algorithm.  The default traits class is \ref
       
   113   /// PreflowDefaultTraits.  See \ref PreflowDefaultTraits for the
       
   114   /// documentation of a %Preflow traits class. 
       
   115   ///
       
   116   ///\author Jacint Szabo and Balazs Dezso
       
   117 #ifdef DOXYGEN
       
   118   template <typename _Graph, typename _CapacityMap, typename _Traits>
       
   119 #else
       
   120   template <typename _Graph, 
       
   121 	    typename _CapacityMap = typename _Graph::template EdgeMap<int>,
       
   122 	    typename _Traits = PreflowDefaultTraits<_Graph, _CapacityMap> >
       
   123 #endif
    70   class Preflow {
   124   class Preflow {
    71   protected:
   125   public:
    72     typedef typename Graph::Node Node;
       
    73     typedef typename Graph::NodeIt NodeIt;
       
    74     typedef typename Graph::EdgeIt EdgeIt;
       
    75     typedef typename Graph::OutEdgeIt OutEdgeIt;
       
    76     typedef typename Graph::InEdgeIt InEdgeIt;
       
    77 
       
    78     typedef typename Graph::template NodeMap<Node> NNMap;
       
    79     typedef typename std::vector<Node> VecNode;
       
    80 
       
    81     const Graph* _g;
       
    82     Node _source;
       
    83     Node _target;
       
    84     const CapacityMap* _capacity;
       
    85     FlowMap* _flow;
       
    86 
       
    87     Tol _surely;
       
    88     
   126     
    89     int _node_num;      //the number of nodes of G
   127     typedef _Traits Traits;
    90     
   128     typedef typename Traits::Graph Graph;
    91     typename Graph::template NodeMap<int> level;  
   129     typedef typename Traits::CapacityMap CapacityMap;
    92     typename Graph::template NodeMap<Num> excess;
   130     typedef typename Traits::Value Value; 
    93 
   131 
    94     // constants used for heuristics
   132     typedef typename Traits::FlowMap FlowMap;
    95     static const int H0=20;
   133     typedef typename Traits::Elevator Elevator;
    96     static const int H1=1;
   134     typedef typename Traits::Tolerance Tolerance;
    97 
   135 
    98   public:
   136     /// \brief \ref Exception for uninitialized parameters.
    99 
   137     ///
   100     ///\ref Exception for the case when s=t.
   138     /// This error represents problems in the initialization
   101 
   139     /// of the parameters of the algorithms.
   102     ///\ref Exception for the case when the source equals the target.
   140     class UninitializedParameter : public lemon::UninitializedParameter {
   103     class InvalidArgument : public lemon::LogicError {
       
   104     public:
   141     public:
   105       virtual const char* what() const throw() {
   142       virtual const char* what() const throw() {
   106 	return "lemon::Preflow::InvalidArgument";
   143 	return "lemon::Preflow::UninitializedParameter";
   107       }
   144       }
   108     };
   145     };
       
   146 
       
   147   private:
       
   148 
       
   149     GRAPH_TYPEDEFS(typename Graph);
       
   150 
       
   151     const Graph& _graph;
       
   152     const CapacityMap* _capacity;
       
   153 
       
   154     int _node_num;
       
   155 
       
   156     Node _source, _target;
       
   157 
       
   158     FlowMap* _flow;
       
   159     bool _local_flow;
       
   160 
       
   161     Elevator* _level;
       
   162     bool _local_level;
       
   163 
       
   164     typedef typename Graph::template NodeMap<Value> ExcessMap;
       
   165     ExcessMap* _excess;
       
   166 
       
   167     Tolerance _tolerance;
       
   168 
       
   169     bool _phase;
       
   170 
       
   171     void createStructures() {
       
   172       _node_num = countNodes(_graph);
       
   173 
       
   174       if (!_flow) {
       
   175 	_flow = Traits::createFlowMap(_graph);
       
   176 	_local_flow = true;
       
   177       }
       
   178       if (!_level) {
       
   179 	_level = Traits::createElevator(_graph, _node_num);
       
   180 	_local_level = true;
       
   181       }
       
   182       if (!_excess) {
       
   183 	_excess = new ExcessMap(_graph);
       
   184       }
       
   185     }
       
   186 
       
   187     void destroyStructures() {
       
   188       if (_local_flow) {
       
   189 	delete _flow;
       
   190       }
       
   191       if (_local_level) {
       
   192 	delete _level;
       
   193       }
       
   194       if (_excess) {
       
   195 	delete _excess;
       
   196       }
       
   197     }
       
   198 
       
   199   public:
       
   200 
       
   201     typedef Preflow Create;
       
   202 
       
   203     ///\name Named template parameters
       
   204 
       
   205     ///@{
       
   206 
       
   207     template <typename _FlowMap>
       
   208     struct DefFlowMapTraits : public Traits {
       
   209       typedef _FlowMap FlowMap;
       
   210       static FlowMap *createFlowMap(const Graph&) {
       
   211 	throw UninitializedParameter();
       
   212       }
       
   213     };
       
   214 
       
   215     /// \brief \ref named-templ-param "Named parameter" for setting
       
   216     /// FlowMap type
       
   217     ///
       
   218     /// \ref named-templ-param "Named parameter" for setting FlowMap
       
   219     /// type
       
   220     template <typename _FlowMap>
       
   221     struct DefFlowMap 
       
   222       : public Preflow<Graph, CapacityMap, DefFlowMapTraits<_FlowMap> > {
       
   223       typedef Preflow<Graph, CapacityMap, DefFlowMapTraits<_FlowMap> > Create;
       
   224     };
       
   225 
       
   226     template <typename _Elevator>
       
   227     struct DefElevatorTraits : public Traits {
       
   228       typedef _Elevator Elevator;
       
   229       static Elevator *createElevator(const Graph&, int) {
       
   230 	throw UninitializedParameter();
       
   231       }
       
   232     };
       
   233 
       
   234     /// \brief \ref named-templ-param "Named parameter" for setting
       
   235     /// Elevator type
       
   236     ///
       
   237     /// \ref named-templ-param "Named parameter" for setting Elevator
       
   238     /// type
       
   239     template <typename _Elevator>
       
   240     struct DefElevator 
       
   241       : public Preflow<Graph, CapacityMap, DefElevatorTraits<_Elevator> > {
       
   242       typedef Preflow<Graph, CapacityMap, DefElevatorTraits<_Elevator> > Create;
       
   243     };
       
   244 
       
   245     template <typename _Elevator>
       
   246     struct DefStandardElevatorTraits : public Traits {
       
   247       typedef _Elevator Elevator;
       
   248       static Elevator *createElevator(const Graph& graph, int max_level) {
       
   249 	return new Elevator(graph, max_level);
       
   250       }
       
   251     };
       
   252 
       
   253     /// \brief \ref named-templ-param "Named parameter" for setting
       
   254     /// Elevator type
       
   255     ///
       
   256     /// \ref named-templ-param "Named parameter" for setting Elevator
       
   257     /// type. The Elevator should be standard constructor interface, ie.
       
   258     /// the graph and the maximum level should be passed to it.
       
   259     template <typename _Elevator>
       
   260     struct DefStandardElevator 
       
   261       : public Preflow<Graph, CapacityMap, 
       
   262 		       DefStandardElevatorTraits<_Elevator> > {
       
   263       typedef Preflow<Graph, CapacityMap, 
       
   264 		      DefStandardElevatorTraits<_Elevator> > Create;
       
   265     };    
       
   266 
       
   267     /// @}
       
   268 
       
   269     /// \brief The constructor of the class.
       
   270     ///
       
   271     /// The constructor of the class. 
       
   272     /// \param graph The directed graph the algorithm runs on. 
       
   273     /// \param capacity The capacity of the edges. 
       
   274     /// \param source The source node.
       
   275     /// \param target The target node.
       
   276     Preflow(const Graph& graph, const CapacityMap& capacity, 
       
   277 	       Node source, Node target) 
       
   278       : _graph(graph), _capacity(&capacity), 
       
   279 	_node_num(0), _source(source), _target(target), 
       
   280 	_flow(0), _local_flow(false),
       
   281 	_level(0), _local_level(false),
       
   282 	_excess(0), _tolerance(), _phase() {}
       
   283 
       
   284     /// \brief Destrcutor.
       
   285     ///
       
   286     /// Destructor.
       
   287     ~Preflow() {
       
   288       destroyStructures();
       
   289     }
       
   290 
       
   291     /// \brief Sets the capacity map.
       
   292     ///
       
   293     /// Sets the capacity map.
       
   294     /// \return \c (*this)
       
   295     Preflow& capacityMap(const CapacityMap& map) {
       
   296       _capacity = &map;
       
   297       return *this;
       
   298     }
       
   299 
       
   300     /// \brief Sets the flow map.
       
   301     ///
       
   302     /// Sets the flow map.
       
   303     /// \return \c (*this)
       
   304     Preflow& flowMap(FlowMap& map) {
       
   305       if (_local_flow) {
       
   306 	delete _flow;
       
   307 	_local_flow = false;
       
   308       }
       
   309       _flow = &map;
       
   310       return *this;
       
   311     }
       
   312 
       
   313     /// \brief Returns the flow map.
       
   314     ///
       
   315     /// \return The flow map.
       
   316     const FlowMap& flowMap() {
       
   317       return *_flow;
       
   318     }
       
   319 
       
   320     /// \brief Sets the elevator.
       
   321     ///
       
   322     /// Sets the elevator.
       
   323     /// \return \c (*this)
       
   324     Preflow& elevator(Elevator& elevator) {
       
   325       if (_local_level) {
       
   326 	delete _level;
       
   327 	_local_level = false;
       
   328       }
       
   329       _level = &elevator;
       
   330       return *this;
       
   331     }
       
   332 
       
   333     /// \brief Returns the elevator.
       
   334     ///
       
   335     /// \return The elevator.
       
   336     const Elevator& elevator() {
       
   337       return *_level;
       
   338     }
       
   339 
       
   340     /// \brief Sets the source node.
       
   341     ///
       
   342     /// Sets the source node.
       
   343     /// \return \c (*this)
       
   344     Preflow& source(const Node& node) {
       
   345       _source = node;
       
   346       return *this;
       
   347     }
       
   348 
       
   349     /// \brief Sets the target node.
       
   350     ///
       
   351     /// Sets the target node.
       
   352     /// \return \c (*this)
       
   353     Preflow& target(const Node& node) {
       
   354       _target = node;
       
   355       return *this;
       
   356     }
       
   357  
       
   358     /// \brief Sets the tolerance used by algorithm.
       
   359     ///
       
   360     /// Sets the tolerance used by algorithm.
       
   361     Preflow& tolerance(const Tolerance& tolerance) const {
       
   362       _tolerance = tolerance;
       
   363       return *this;
       
   364     } 
       
   365 
       
   366     /// \brief Returns the tolerance used by algorithm.
       
   367     ///
       
   368     /// Returns the tolerance used by algorithm.
       
   369     const Tolerance& tolerance() const {
       
   370       return tolerance;
       
   371     } 
       
   372 
       
   373     /// \name Execution control The simplest way to execute the
       
   374     /// algorithm is to use one of the member functions called \c
       
   375     /// run().  
       
   376     /// \n
       
   377     /// If you need more control on initial solution or
       
   378     /// execution then you have to call one \ref init() function and then
       
   379     /// the startFirstPhase() and if you need the startSecondPhase().  
   109     
   380     
       
   381     ///@{
       
   382 
       
   383     /// \brief Initializes the internal data structures.
       
   384     ///
       
   385     /// Initializes the internal data structures.
       
   386     ///
       
   387     void init() {
       
   388       createStructures();
       
   389 
       
   390       _phase = true;
       
   391       for (NodeIt n(_graph); n != INVALID; ++n) {
       
   392 	_excess->set(n, 0);
       
   393       }
       
   394 
       
   395       for (EdgeIt e(_graph); e != INVALID; ++e) {
       
   396 	_flow->set(e, 0);
       
   397       }
       
   398 
       
   399       typename Graph::template NodeMap<bool> reached(_graph, false);
       
   400 
       
   401       _level->initStart();
       
   402       _level->initAddItem(_target);
       
   403 
       
   404       std::vector<Node> queue;
       
   405       reached.set(_source, true);
       
   406 
       
   407       queue.push_back(_target);
       
   408       reached.set(_target, true);
       
   409       while (!queue.empty()) {
       
   410 	std::vector<Node> nqueue;
       
   411 	for (int i = 0; i < int(queue.size()); ++i) {
       
   412 	  Node n = queue[i];
       
   413 	  for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   414 	    Node u = _graph.source(e);
       
   415 	    if (!reached[u] && _tolerance.positive((*_capacity)[e])) {
       
   416 	      reached.set(u, true);
       
   417 	      _level->initAddItem(u);
       
   418 	      nqueue.push_back(u);
       
   419 	    }
       
   420 	  }
       
   421 	}
       
   422 	queue.swap(nqueue);
       
   423       }
       
   424       _level->initFinish();
       
   425 
       
   426       for (OutEdgeIt e(_graph, _source); e != INVALID; ++e) {
       
   427 	if (_tolerance.positive((*_capacity)[e])) {
       
   428 	  Node u = _graph.target(e);
       
   429 	  if ((*_level)[u] == _level->maxLevel()) continue;
       
   430 	  _flow->set(e, (*_capacity)[e]);
       
   431 	  _excess->set(u, (*_excess)[u] + (*_capacity)[e]);
       
   432 	  if (u != _target && !_level->active(u)) {
       
   433 	    _level->activate(u);
       
   434 	  }
       
   435 	}
       
   436       }
       
   437     }
       
   438 
       
   439     /// \brief Initializes the internal data structures.
       
   440     ///
       
   441     /// Initializes the internal data structures and sets the initial
       
   442     /// flow to the given \c flowMap. The \c flowMap should contain a
       
   443     /// flow or at least a preflow, ie. in each node excluding the
       
   444     /// target the incoming flow should greater or equal to the
       
   445     /// outgoing flow.
       
   446     /// \return %False when the given \c flowMap is not a preflow. 
       
   447     template <typename FlowMap>
       
   448     bool flowInit(const FlowMap& flowMap) {
       
   449       createStructures();
       
   450 
       
   451       for (EdgeIt e(_graph); e != INVALID; ++e) {
       
   452 	_flow->set(e, flowMap[e]);
       
   453       }
       
   454 
       
   455       for (NodeIt n(_graph); n != INVALID; ++n) {
       
   456 	Value excess = 0;
       
   457 	for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   458 	  excess += (*_flow)[e];
       
   459 	}
       
   460 	for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   461 	  excess -= (*_flow)[e];
       
   462 	}
       
   463 	if (excess < 0 && n != _source) return false;
       
   464 	_excess->set(n, excess);
       
   465       }
       
   466 
       
   467       typename Graph::template NodeMap<bool> reached(_graph, false);
       
   468 
       
   469       _level->initStart();
       
   470       _level->initAddItem(_target);
       
   471 
       
   472       std::vector<Node> queue;
       
   473       reached.set(_source, true);
       
   474 
       
   475       queue.push_back(_target);
       
   476       reached.set(_target, true);
       
   477       while (!queue.empty()) {
       
   478 	_level->initNewLevel();
       
   479 	std::vector<Node> nqueue;
       
   480 	for (int i = 0; i < int(queue.size()); ++i) {
       
   481 	  Node n = queue[i];
       
   482 	  for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   483 	    Node u = _graph.source(e);
       
   484 	    if (!reached[u] && 
       
   485 		_tolerance.positive((*_capacity)[e] - (*_flow)[e])) {
       
   486 	      reached.set(u, true);
       
   487 	      _level->initAddItem(u);
       
   488 	      nqueue.push_back(u);
       
   489 	    }
       
   490 	  }
       
   491 	  for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   492 	    Node v = _graph.target(e);
       
   493 	    if (!reached[v] && _tolerance.positive((*_flow)[e])) {
       
   494 	      reached.set(v, true);
       
   495 	      _level->initAddItem(v);
       
   496 	      nqueue.push_back(v);
       
   497 	    }
       
   498 	  }
       
   499 	}
       
   500 	queue.swap(nqueue);
       
   501       }
       
   502       _level->initFinish();
       
   503 
       
   504       for (OutEdgeIt e(_graph, _source); e != INVALID; ++e) {
       
   505 	Value rem = (*_capacity)[e] - (*_flow)[e]; 
       
   506 	if (_tolerance.positive(rem)) {
       
   507 	  Node u = _graph.target(e);
       
   508 	  if ((*_level)[u] == _level->maxLevel()) continue;
       
   509 	  _flow->set(e, (*_capacity)[e]);
       
   510 	  _excess->set(u, (*_excess)[u] + rem);
       
   511 	  if (u != _target && !_level->active(u)) {
       
   512 	    _level->activate(u);
       
   513 	  }
       
   514 	}
       
   515       }
       
   516       for (InEdgeIt e(_graph, _source); e != INVALID; ++e) {
       
   517 	Value rem = (*_flow)[e]; 
       
   518 	if (_tolerance.positive(rem)) {
       
   519 	  Node v = _graph.source(e);
       
   520 	  if ((*_level)[v] == _level->maxLevel()) continue;
       
   521 	  _flow->set(e, 0);
       
   522 	  _excess->set(v, (*_excess)[v] + rem);
       
   523 	  if (v != _target && !_level->active(v)) {
       
   524 	    _level->activate(v);
       
   525 	  }
       
   526 	}
       
   527       }
       
   528       return true;
       
   529     }
       
   530 
       
   531     /// \brief Starts the first phase of the preflow algorithm.
       
   532     ///
       
   533     /// The preflow algorithm consists of two phases, this method runs
       
   534     /// the first phase. After the first phase the maximum flow value
       
   535     /// and a minimum value cut can already be computed, although a
       
   536     /// maximum flow is not yet obtained. So after calling this method
       
   537     /// \ref flowValue() returns the value of a maximum flow and \ref
       
   538     /// minCut() returns a minimum cut.     
       
   539     /// \pre One of the \ref init() functions should be called. 
       
   540     void startFirstPhase() {
       
   541       _phase = true;
       
   542 
       
   543       Node n = _level->highestActive();
       
   544       int level = _level->highestActiveLevel();
       
   545       while (n != INVALID) {
       
   546 	int num = _node_num;
       
   547 
       
   548 	while (num > 0 && n != INVALID) {
       
   549 	  Value excess = (*_excess)[n];
       
   550 	  int new_level = _level->maxLevel();
       
   551 
       
   552 	  for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   553 	    Value rem = (*_capacity)[e] - (*_flow)[e];
       
   554 	    if (!_tolerance.positive(rem)) continue;
       
   555 	    Node v = _graph.target(e);
       
   556 	    if ((*_level)[v] < level) {
       
   557 	      if (!_level->active(v) && v != _target) {
       
   558 		_level->activate(v);
       
   559 	      }
       
   560 	      if (!_tolerance.less(rem, excess)) {
       
   561 		_flow->set(e, (*_flow)[e] + excess);
       
   562 		_excess->set(v, (*_excess)[v] + excess);
       
   563 		excess = 0;
       
   564 		goto no_more_push_1;
       
   565 	      } else {
       
   566 		excess -= rem;
       
   567 		_excess->set(v, (*_excess)[v] + rem);
       
   568 		_flow->set(e, (*_capacity)[e]);
       
   569 	      }
       
   570 	    } else if (new_level > (*_level)[v]) {
       
   571 	      new_level = (*_level)[v];
       
   572 	    }
       
   573 	  }
       
   574 
       
   575 	  for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   576 	    Value rem = (*_flow)[e];
       
   577 	    if (!_tolerance.positive(rem)) continue;
       
   578 	    Node v = _graph.source(e);
       
   579 	    if ((*_level)[v] < level) {
       
   580 	      if (!_level->active(v) && v != _target) {
       
   581 		_level->activate(v);
       
   582 	      }
       
   583 	      if (!_tolerance.less(rem, excess)) {
       
   584 		_flow->set(e, (*_flow)[e] - excess);
       
   585 		_excess->set(v, (*_excess)[v] + excess);
       
   586 		excess = 0;
       
   587 		goto no_more_push_1;
       
   588 	      } else {
       
   589 		excess -= rem;
       
   590 		_excess->set(v, (*_excess)[v] + rem);
       
   591 		_flow->set(e, 0);
       
   592 	      }
       
   593 	    } else if (new_level > (*_level)[v]) {
       
   594 	      new_level = (*_level)[v];
       
   595 	    }
       
   596 	  }
       
   597 
       
   598 	no_more_push_1:
       
   599 
       
   600 	  _excess->set(n, excess);
       
   601 
       
   602 	  if (excess != 0) {
       
   603 	    if (new_level + 1 < _level->maxLevel()) {
       
   604 	      _level->liftHighestActive(new_level + 1);
       
   605 	    } else {
       
   606 	      _level->liftHighestActiveToTop();
       
   607 	    }
       
   608 	    if (_level->emptyLevel(level)) {
       
   609 	      _level->liftToTop(level);
       
   610 	    }
       
   611 	  } else {
       
   612 	    _level->deactivate(n);
       
   613 	  }
       
   614 	  
       
   615 	  n = _level->highestActive();
       
   616 	  level = _level->highestActiveLevel();
       
   617 	  --num;
       
   618 	}
       
   619 	
       
   620 	num = _node_num * 20;
       
   621 	while (num > 0 && n != INVALID) {
       
   622 	  Value excess = (*_excess)[n];
       
   623 	  int new_level = _level->maxLevel();
       
   624 
       
   625 	  for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   626 	    Value rem = (*_capacity)[e] - (*_flow)[e];
       
   627 	    if (!_tolerance.positive(rem)) continue;
       
   628 	    Node v = _graph.target(e);
       
   629 	    if ((*_level)[v] < level) {
       
   630 	      if (!_level->active(v) && v != _target) {
       
   631 		_level->activate(v);
       
   632 	      }
       
   633 	      if (!_tolerance.less(rem, excess)) {
       
   634 		_flow->set(e, (*_flow)[e] + excess);
       
   635 		_excess->set(v, (*_excess)[v] + excess);
       
   636 		excess = 0;
       
   637 		goto no_more_push_2;
       
   638 	      } else {
       
   639 		excess -= rem;
       
   640 		_excess->set(v, (*_excess)[v] + rem);
       
   641 		_flow->set(e, (*_capacity)[e]);
       
   642 	      }
       
   643 	    } else if (new_level > (*_level)[v]) {
       
   644 	      new_level = (*_level)[v];
       
   645 	    }
       
   646 	  }
       
   647 
       
   648 	  for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   649 	    Value rem = (*_flow)[e];
       
   650 	    if (!_tolerance.positive(rem)) continue;
       
   651 	    Node v = _graph.source(e);
       
   652 	    if ((*_level)[v] < level) {
       
   653 	      if (!_level->active(v) && v != _target) {
       
   654 		_level->activate(v);
       
   655 	      }
       
   656 	      if (!_tolerance.less(rem, excess)) {
       
   657 		_flow->set(e, (*_flow)[e] - excess);
       
   658 		_excess->set(v, (*_excess)[v] + excess);
       
   659 		excess = 0;
       
   660 		goto no_more_push_2;
       
   661 	      } else {
       
   662 		excess -= rem;
       
   663 		_excess->set(v, (*_excess)[v] + rem);
       
   664 		_flow->set(e, 0);
       
   665 	      }
       
   666 	    } else if (new_level > (*_level)[v]) {
       
   667 	      new_level = (*_level)[v];
       
   668 	    }
       
   669 	  }
       
   670 
       
   671 	no_more_push_2:
       
   672 
       
   673 	  _excess->set(n, excess);
       
   674 
       
   675 	  if (excess != 0) {
       
   676 	    if (new_level + 1 < _level->maxLevel()) {
       
   677 	      _level->liftActiveOn(level, new_level + 1);
       
   678 	    } else {
       
   679 	      _level->liftActiveToTop(level);
       
   680 	    }
       
   681 	    if (_level->emptyLevel(level)) {
       
   682 	      _level->liftToTop(level);
       
   683 	    }
       
   684 	  } else {
       
   685 	    _level->deactivate(n);
       
   686 	  }
       
   687 
       
   688 	  while (level >= 0 && _level->activeFree(level)) {
       
   689 	    --level;
       
   690 	  }
       
   691 	  if (level == -1) {
       
   692 	    n = _level->highestActive();
       
   693 	    level = _level->highestActiveLevel();
       
   694 	  } else {
       
   695 	    n = _level->activeOn(level);
       
   696 	  }
       
   697 	  --num;
       
   698 	}
       
   699       }
       
   700     }
       
   701 
       
   702     /// \brief Starts the second phase of the preflow algorithm.
       
   703     ///
       
   704     /// The preflow algorithm consists of two phases, this method runs
       
   705     /// the second phase. After calling \ref init() and \ref
       
   706     /// startFirstPhase() and then \ref startSecondPhase(), \ref
       
   707     /// flowMap() return a maximum flow, \ref flowValue() returns the
       
   708     /// value of a maximum flow, \ref minCut() returns a minimum cut
       
   709     /// \pre The \ref init() and startFirstPhase() functions should be
       
   710     /// called before.
       
   711     void startSecondPhase() {
       
   712       _phase = false;
       
   713 
       
   714       typename Graph::template NodeMap<bool> reached(_graph, false);
       
   715       for (NodeIt n(_graph); n != INVALID; ++n) {
       
   716 	reached.set(n, (*_level)[n] < _level->maxLevel());
       
   717       }
       
   718 
       
   719       _level->initStart();
       
   720       _level->initAddItem(_source);
       
   721  
       
   722       std::vector<Node> queue;
       
   723       queue.push_back(_source);
       
   724       reached.set(_source, true);
       
   725 
       
   726       while (!queue.empty()) {
       
   727 	_level->initNewLevel();
       
   728 	std::vector<Node> nqueue;
       
   729 	for (int i = 0; i < int(queue.size()); ++i) {
       
   730 	  Node n = queue[i];
       
   731 	  for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   732 	    Node v = _graph.target(e);
       
   733 	    if (!reached[v] && _tolerance.positive((*_flow)[e])) {
       
   734 	      reached.set(v, true);
       
   735 	      _level->initAddItem(v);
       
   736 	      nqueue.push_back(v);
       
   737 	    }
       
   738 	  }
       
   739 	  for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   740 	    Node u = _graph.source(e);
       
   741 	    if (!reached[u] && 
       
   742 		_tolerance.positive((*_capacity)[e] - (*_flow)[e])) {
       
   743 	      reached.set(u, true);
       
   744 	      _level->initAddItem(u);
       
   745 	      nqueue.push_back(u);
       
   746 	    }
       
   747 	  }
       
   748 	}
       
   749 	queue.swap(nqueue);
       
   750       }
       
   751       _level->initFinish();
       
   752 
       
   753       for (NodeIt n(_graph); n != INVALID; ++n) {
       
   754 	if ((*_excess)[n] > 0 && _target != n) {
       
   755 	  _level->activate(n);
       
   756 	}
       
   757       }
       
   758 
       
   759       Node n;
       
   760       while ((n = _level->highestActive()) != INVALID) {
       
   761 	Value excess = (*_excess)[n];
       
   762 	int level = _level->highestActiveLevel();
       
   763 	int new_level = _level->maxLevel();
       
   764 
       
   765 	for (OutEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   766 	  Value rem = (*_capacity)[e] - (*_flow)[e];
       
   767 	  if (!_tolerance.positive(rem)) continue;
       
   768 	  Node v = _graph.target(e);
       
   769 	  if ((*_level)[v] < level) {
       
   770 	    if (!_level->active(v) && v != _source) {
       
   771 	      _level->activate(v);
       
   772 	    }
       
   773 	    if (!_tolerance.less(rem, excess)) {
       
   774 	      _flow->set(e, (*_flow)[e] + excess);
       
   775 	      _excess->set(v, (*_excess)[v] + excess);
       
   776 	      excess = 0;
       
   777 	      goto no_more_push;
       
   778 	    } else {
       
   779 	      excess -= rem;
       
   780 	      _excess->set(v, (*_excess)[v] + rem);
       
   781 	      _flow->set(e, (*_capacity)[e]);
       
   782 	    }
       
   783 	  } else if (new_level > (*_level)[v]) {
       
   784 	    new_level = (*_level)[v];
       
   785 	  }
       
   786 	}
       
   787 
       
   788 	for (InEdgeIt e(_graph, n); e != INVALID; ++e) {
       
   789 	  Value rem = (*_flow)[e];
       
   790 	  if (!_tolerance.positive(rem)) continue;
       
   791 	  Node v = _graph.source(e);
       
   792 	  if ((*_level)[v] < level) {
       
   793 	    if (!_level->active(v) && v != _source) {
       
   794 	      _level->activate(v);
       
   795 	    }
       
   796 	    if (!_tolerance.less(rem, excess)) {
       
   797 	      _flow->set(e, (*_flow)[e] - excess);
       
   798 	      _excess->set(v, (*_excess)[v] + excess);
       
   799 	      excess = 0;
       
   800 	      goto no_more_push;
       
   801 	    } else {
       
   802 	      excess -= rem;
       
   803 	      _excess->set(v, (*_excess)[v] + rem);
       
   804 	      _flow->set(e, 0);
       
   805 	    }
       
   806 	  } else if (new_level > (*_level)[v]) {
       
   807 	    new_level = (*_level)[v];
       
   808 	  }
       
   809 	}
       
   810 
       
   811       no_more_push:
       
   812 
       
   813 	_excess->set(n, excess);
       
   814       
       
   815 	if (excess != 0) {
       
   816 	  if (new_level + 1 < _level->maxLevel()) {
       
   817 	    _level->liftHighestActive(new_level + 1);
       
   818 	  } else {
       
   819 	    // Calculation error 
       
   820 	    _level->liftHighestActiveToTop();
       
   821 	  }
       
   822 	  if (_level->emptyLevel(level)) {
       
   823 	    // Calculation error 
       
   824 	    _level->liftToTop(level);
       
   825 	  }
       
   826 	} else {
       
   827 	  _level->deactivate(n);
       
   828 	}
       
   829 
       
   830       }
       
   831     }
       
   832 
       
   833     /// \brief Runs the preflow algorithm.  
       
   834     ///
       
   835     /// Runs the preflow algorithm.
       
   836     /// \note pf.run() is just a shortcut of the following code.
       
   837     /// \code
       
   838     ///   pf.init();
       
   839     ///   pf.startFirstPhase();
       
   840     ///   pf.startSecondPhase();
       
   841     /// \endcode
       
   842     void run() {
       
   843       init();
       
   844       startFirstPhase();
       
   845       startSecondPhase();
       
   846     }
       
   847 
       
   848     /// \brief Runs the preflow algorithm to compute the minimum cut.  
       
   849     ///
       
   850     /// Runs the preflow algorithm to compute the minimum cut.
       
   851     /// \note pf.runMinCut() is just a shortcut of the following code.
       
   852     /// \code
       
   853     ///   pf.init();
       
   854     ///   pf.startFirstPhase();
       
   855     /// \endcode
       
   856     void runMinCut() {
       
   857       init();
       
   858       startFirstPhase();
       
   859     }
       
   860 
       
   861     /// @}
       
   862 
       
   863     /// \name Query Functions
       
   864     /// The result of the %Dijkstra algorithm can be obtained using these
       
   865     /// functions.\n
       
   866     /// Before the use of these functions,
       
   867     /// either run() or start() must be called.
   110     
   868     
   111     ///Indicates the property of the starting flow map.
   869     ///@{
   112     
   870 
   113     ///Indicates the property of the starting flow map.
   871     /// \brief Returns the value of the maximum flow.
   114     ///
   872     ///
   115     enum FlowEnum{
       
   116       ///indicates an unspecified edge map. \c flow will be 
       
   117       ///set to the constant zero flow in the beginning of
       
   118       ///the algorithm in this case.
       
   119       NO_FLOW,
       
   120       ///constant zero flow
       
   121       ZERO_FLOW,
       
   122       ///any flow, i.e. the sum of the in-flows equals to
       
   123       ///the sum of the out-flows in every node except the \c source and
       
   124       ///the \c target.
       
   125       GEN_FLOW,
       
   126       ///any preflow, i.e. the sum of the in-flows is at 
       
   127       ///least the sum of the out-flows in every node except the \c source.
       
   128       PRE_FLOW
       
   129     };
       
   130 
       
   131     ///Indicates the state of the preflow algorithm.
       
   132 
       
   133     ///Indicates the state of the preflow algorithm.
       
   134     ///
       
   135     enum StatusEnum {
       
   136       ///before running the algorithm or
       
   137       ///at an unspecified state.
       
   138       AFTER_NOTHING,
       
   139       ///right after running \ref phase1()
       
   140       AFTER_PREFLOW_PHASE_1,      
       
   141       ///after running \ref phase2()
       
   142       AFTER_PREFLOW_PHASE_2
       
   143     };
       
   144     
       
   145   protected: 
       
   146     FlowEnum flow_prop;
       
   147     StatusEnum status; // Do not needle this flag only if necessary.
       
   148     
       
   149   public: 
       
   150     ///The constructor of the class.
       
   151 
       
   152     ///The constructor of the class. 
       
   153     ///\param _gr The directed graph the algorithm runs on. 
       
   154     ///\param _s The source node.
       
   155     ///\param _t The target node.
       
   156     ///\param _cap The capacity of the edges. 
       
   157     ///\param _f The flow of the edges. 
       
   158     ///\param _sr Tol class.
       
   159     ///Except the graph, all of these parameters can be reset by
       
   160     ///calling \ref source, \ref target, \ref capacityMap and \ref
       
   161     ///flowMap, resp.
       
   162     Preflow(const Graph& _gr, Node _s, Node _t, 
       
   163             const CapacityMap& _cap, FlowMap& _f,
       
   164             const Tol &_sr=Tol()) :
       
   165 	_g(&_gr), _source(_s), _target(_t), _capacity(&_cap),
       
   166 	_flow(&_f), _surely(_sr),
       
   167 	_node_num(countNodes(_gr)), level(_gr), excess(_gr,0), 
       
   168 	flow_prop(NO_FLOW), status(AFTER_NOTHING) { 
       
   169 	if ( _source==_target )
       
   170 	  throw InvalidArgument();
       
   171     }
       
   172     
       
   173     ///Give a reference to the tolerance handler class
       
   174 
       
   175     ///Give a reference to the tolerance handler class
       
   176     ///\sa Tolerance
       
   177     Tol &tolerance() { return _surely; }
       
   178 
       
   179     ///Runs the preflow algorithm.  
       
   180 
       
   181     ///Runs the preflow algorithm.
       
   182     ///
       
   183     void run() {
       
   184       phase1(flow_prop);
       
   185       phase2();
       
   186     }
       
   187     
       
   188     ///Runs the preflow algorithm.  
       
   189     
       
   190     ///Runs the preflow algorithm. 
       
   191     ///\pre The starting flow map must be
       
   192     /// - a constant zero flow if \c fp is \c ZERO_FLOW,
       
   193     /// - an arbitrary flow if \c fp is \c GEN_FLOW,
       
   194     /// - an arbitrary preflow if \c fp is \c PRE_FLOW,
       
   195     /// - any map if \c fp is NO_FLOW.
       
   196     ///If the starting flow map is a flow or a preflow then 
       
   197     ///the algorithm terminates faster.
       
   198     void run(FlowEnum fp) {
       
   199       flow_prop=fp;
       
   200       run();
       
   201     }
       
   202       
       
   203     ///Runs the first phase of the preflow algorithm.
       
   204 
       
   205     ///The preflow algorithm consists of two phases, this method runs
       
   206     ///the first phase. After the first phase the maximum flow value
       
   207     ///and a minimum value cut can already be computed, although a
       
   208     ///maximum flow is not yet obtained. So after calling this method
       
   209     ///\ref flowValue returns the value of a maximum flow and \ref
       
   210     ///minCut returns a minimum cut.     
       
   211     ///\warning \ref minMinCut and \ref maxMinCut do not give minimum
       
   212     ///value cuts unless calling \ref phase2.  
       
   213     ///\warning A real flow map (i.e. not \ref lemon::NullMap "NullMap")
       
   214     ///is needed for this phase.
       
   215     ///\pre The starting flow must be 
       
   216     ///- a constant zero flow if \c fp is \c ZERO_FLOW, 
       
   217     ///- an arbitary flow if \c fp is \c GEN_FLOW, 
       
   218     ///- an arbitary preflow if \c fp is \c PRE_FLOW, 
       
   219     ///- any map if \c fp is NO_FLOW.
       
   220     void phase1(FlowEnum fp)
       
   221     {
       
   222       flow_prop=fp;
       
   223       phase1();
       
   224     }
       
   225 
       
   226     
       
   227     ///Runs the first phase of the preflow algorithm.
       
   228 
       
   229     ///The preflow algorithm consists of two phases, this method runs
       
   230     ///the first phase. After the first phase the maximum flow value
       
   231     ///and a minimum value cut can already be computed, although a
       
   232     ///maximum flow is not yet obtained. So after calling this method
       
   233     ///\ref flowValue returns the value of a maximum flow and \ref
       
   234     ///minCut returns a minimum cut.
       
   235     ///\warning \ref minMinCut() and \ref maxMinCut() do not
       
   236     ///give minimum value cuts unless calling \ref phase2().
       
   237     ///\warning A real flow map (i.e. not \ref lemon::NullMap "NullMap")
       
   238     ///is needed for this phase.
       
   239     void phase1()
       
   240     {
       
   241       int heur0=int(H0*_node_num);  //time while running 'bound decrease'
       
   242       int heur1=int(H1*_node_num);  //time while running 'highest label'
       
   243       int heur=heur1;         //starting time interval (#of relabels)
       
   244       int numrelabel=0;
       
   245 
       
   246       bool what_heur=1;
       
   247       //It is 0 in case 'bound decrease' and 1 in case 'highest label'
       
   248 
       
   249       bool end=false;
       
   250       //Needed for 'bound decrease', true means no active 
       
   251       //nodes are above bound b.
       
   252 
       
   253       int k=_node_num-2;  //bound on the highest level under n containing a node
       
   254       int b=k;    //bound on the highest level under n containing an active node
       
   255 
       
   256       VecNode first(_node_num, INVALID);
       
   257       NNMap next(*_g, INVALID);
       
   258 
       
   259       NNMap left(*_g, INVALID);
       
   260       NNMap right(*_g, INVALID);
       
   261       VecNode level_list(_node_num,INVALID);
       
   262       //List of the nodes in level i<n, set to n.
       
   263 
       
   264       preflowPreproc(first, next, level_list, left, right);
       
   265 
       
   266       //Push/relabel on the highest level active nodes.
       
   267       while ( true ) {
       
   268 	if ( b == 0 ) {
       
   269 	  if ( !what_heur && !end && k > 0 ) {
       
   270 	    b=k;
       
   271 	    end=true;
       
   272 	  } else break;
       
   273 	}
       
   274 
       
   275 	if ( first[b]==INVALID ) --b;
       
   276 	else {
       
   277 	  end=false;
       
   278 	  Node w=first[b];
       
   279 	  first[b]=next[w];
       
   280 	  int newlevel=push(w, next, first);
       
   281 	  if ( excess[w] != 0 ) {
       
   282             relabel(w, newlevel, first, next, level_list, 
       
   283                     left, right, b, k, what_heur);
       
   284           }
       
   285 
       
   286 	  ++numrelabel;
       
   287 	  if ( numrelabel >= heur ) {
       
   288 	    numrelabel=0;
       
   289 	    if ( what_heur ) {
       
   290 	      what_heur=0;
       
   291 	      heur=heur0;
       
   292 	      end=false;
       
   293 	    } else {
       
   294 	      what_heur=1;
       
   295 	      heur=heur1;
       
   296 	      b=k;
       
   297 	    }
       
   298 	  }
       
   299 	}
       
   300       }
       
   301       flow_prop=PRE_FLOW;
       
   302       status=AFTER_PREFLOW_PHASE_1;
       
   303     }
       
   304     // Heuristics:
       
   305     //   2 phase
       
   306     //   gap
       
   307     //   list 'level_list' on the nodes on level i implemented by hand
       
   308     //   stack 'active' on the active nodes on level i      
       
   309     //   runs heuristic 'highest label' for H1*n relabels
       
   310     //   runs heuristic 'bound decrease' for H0*n relabels,
       
   311     //        starts with 'highest label'
       
   312     //   Parameters H0 and H1 are initialized to 20 and 1.
       
   313 
       
   314 
       
   315     ///Runs the second phase of the preflow algorithm.
       
   316 
       
   317     ///The preflow algorithm consists of two phases, this method runs
       
   318     ///the second phase. After calling \ref phase1() and then
       
   319     ///\ref phase2(),
       
   320     /// \ref flowMap() return a maximum flow, \ref flowValue
       
   321     ///returns the value of a maximum flow, \ref minCut returns a
       
   322     ///minimum cut, while the methods \ref minMinCut and \ref
       
   323     ///maxMinCut return the inclusionwise minimum and maximum cuts of
       
   324     ///minimum value, resp.  \pre \ref phase1 must be called before.
       
   325     ///
       
   326     /// \todo The inexact computation can cause positive excess on a set of 
       
   327     /// unpushable nodes. We may have to watch the empty level in this case 
       
   328     /// due to avoid the terrible long running time.
       
   329     void phase2()
       
   330     {
       
   331 
       
   332       int k=_node_num-2;  //bound on the highest level under n containing a node
       
   333       int b=k;    //bound on the highest level under n of an active node
       
   334 
       
   335     
       
   336       VecNode first(_node_num, INVALID);
       
   337       NNMap next(*_g, INVALID); 
       
   338       level.set(_source,0);
       
   339       std::queue<Node> bfs_queue;
       
   340       bfs_queue.push(_source);
       
   341 
       
   342       while ( !bfs_queue.empty() ) {
       
   343 
       
   344 	Node v=bfs_queue.front();
       
   345 	bfs_queue.pop();
       
   346 	int l=level[v]+1;
       
   347 
       
   348 	for(InEdgeIt e(*_g,v); e!=INVALID; ++e) {
       
   349 	  if ( !_surely.positive((*_capacity)[e] - (*_flow)[e])) continue;
       
   350 	  Node u=_g->source(e);
       
   351 	  if ( level[u] >= _node_num ) {
       
   352 	    bfs_queue.push(u);
       
   353 	    level.set(u, l);
       
   354 	    if ( excess[u] != 0 ) {
       
   355 	      next.set(u,first[l]);
       
   356 	      first[l]=u;
       
   357 	    }
       
   358 	  }
       
   359 	}
       
   360 
       
   361 	for(OutEdgeIt e(*_g,v); e!=INVALID; ++e) {
       
   362 	  if ( !_surely.positive((*_flow)[e]) ) continue;
       
   363 	  Node u=_g->target(e);
       
   364 	  if ( level[u] >= _node_num ) {
       
   365 	    bfs_queue.push(u);
       
   366 	    level.set(u, l);
       
   367 	    if ( excess[u] != 0 ) {
       
   368 	      next.set(u,first[l]);
       
   369 	      first[l]=u;
       
   370 	    }
       
   371 	  }
       
   372 	}
       
   373       }
       
   374       b=_node_num-2;
       
   375 
       
   376       while ( true ) {
       
   377 
       
   378 	if ( b == 0 ) break;
       
   379 	if ( first[b]==INVALID ) --b;
       
   380 	else {
       
   381 	  Node w=first[b];
       
   382 	  first[b]=next[w];
       
   383 	  int newlevel=push(w,next, first);
       
   384 	  
       
   385           if ( newlevel == _node_num) {
       
   386             excess.set(w, 0);
       
   387 	    level.set(w,_node_num);
       
   388           }
       
   389 	  //relabel
       
   390 	  if ( excess[w] != 0 ) {
       
   391 	    level.set(w,++newlevel);
       
   392 	    next.set(w,first[newlevel]);
       
   393 	    first[newlevel]=w;
       
   394 	    b=newlevel;
       
   395 	  }
       
   396 	} 
       
   397       } // while(true)
       
   398       flow_prop=GEN_FLOW;
       
   399       status=AFTER_PREFLOW_PHASE_2;
       
   400     }
       
   401 
       
   402     /// Returns the value of the maximum flow.
       
   403 
       
   404     /// Returns the value of the maximum flow by returning the excess
   873     /// Returns the value of the maximum flow by returning the excess
   405     /// of the target node \c t. This value equals to the value of
   874     /// of the target node \c t. This value equals to the value of
   406     /// the maximum flow already after running \ref phase1.
   875     /// the maximum flow already after the first phase.
   407     Num flowValue() const {
   876     Value flowValue() const {
   408       return excess[_target];
   877       return (*_excess)[_target];
   409     }
   878     }
   410 
   879 
   411 
   880     /// \brief Returns true when the node is on the source side of minimum cut.
   412     ///Returns a minimum value cut.
   881     ///
   413 
   882     /// Returns true when the node is on the source side of minimum
   414     ///Sets \c M to the characteristic vector of a minimum value
   883     /// cut. This method can be called both after running \ref
   415     ///cut. This method can be called both after running \ref
   884     /// startFirstPhase() and \ref startSecondPhase().
   416     ///phase1 and \ref phase2. It is much faster after
   885     bool minCut(const Node& node) const {
   417     ///\ref phase1.  \pre M should be a bool-valued node-map. \pre
   886       return ((*_level)[node] == _level->maxLevel()) == _phase;
   418     ///If \ref minCut() is called after \ref phase2() then M should
   887     }
   419     ///be initialized to false.
   888  
   420     template<typename _CutMap>
   889     /// \brief Returns a minimum value cut.
   421     void minCut(_CutMap& M) const {
   890     ///
   422       switch ( status ) {
   891     /// Sets the \c cutMap to the characteristic vector of a minimum value
   423 	case AFTER_PREFLOW_PHASE_1:
   892     /// cut. This method can be called both after running \ref
   424 	for(NodeIt v(*_g); v!=INVALID; ++v) {
   893     /// startFirstPhase() and \ref startSecondPhase(). The result after second
   425 	  if (level[v] < _node_num) {
   894     /// phase could be changed slightly if inexact computation is used.
   426 	    M.set(v, false);
   895     /// \pre The \c cutMap should be a bool-valued node-map.
   427 	  } else {
   896     template <typename CutMap>
   428 	    M.set(v, true);
   897     void minCutMap(CutMap& cutMap) const {
   429 	  }
   898       for (NodeIt n(_graph); n != INVALID; ++n) {
   430 	}
   899 	cutMap.set(n, minCut(n));
   431 	break;
   900       }
   432 	case AFTER_PREFLOW_PHASE_2:
   901     }
   433 	minMinCut(M);
   902 
   434 	break;
   903     /// \brief Returns the flow on the edge.
   435 	case AFTER_NOTHING:
   904     ///
   436 	break;
   905     /// Sets the \c flowMap to the flow on the edges. This method can
   437       }
   906     /// be called after the second phase of algorithm.
   438     }
   907     Value flow(const Edge& edge) const {
   439 
   908       return (*_flow)[edge];
   440     ///Returns the inclusionwise minimum of the minimum value cuts.
       
   441 
       
   442     ///Sets \c M to the characteristic vector of the minimum value cut
       
   443     ///which is inclusionwise minimum. It is computed by processing a
       
   444     ///bfs from the source node \c s in the residual graph.  \pre M
       
   445     ///should be a node map of bools initialized to false.  \pre \ref
       
   446     ///phase2 should already be run.
       
   447     template<typename _CutMap>
       
   448     void minMinCut(_CutMap& M) const {
       
   449 
       
   450       std::queue<Node> queue;
       
   451       M.set(_source,true);
       
   452       queue.push(_source);
       
   453       
       
   454       while (!queue.empty()) {
       
   455 	Node w=queue.front();
       
   456 	queue.pop();
       
   457 	
       
   458 	for(OutEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   459 	  Node v=_g->target(e);
       
   460 	  if (!M[v] && _surely.positive((*_capacity)[e] -(*_flow)[e]) ) {
       
   461 	    queue.push(v);
       
   462 	    M.set(v, true);
       
   463 	  }
       
   464 	}
       
   465 	
       
   466 	for(InEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   467 	  Node v=_g->source(e);
       
   468 	  if (!M[v] && _surely.positive((*_flow)[e]) ) {
       
   469 	    queue.push(v);
       
   470 	    M.set(v, true);
       
   471 	  }
       
   472 	}
       
   473       }
       
   474     }
   909     }
   475     
   910     
   476     ///Returns the inclusionwise maximum of the minimum value cuts.
   911     /// @}    
   477 
   912   };
   478     ///Sets \c M to the characteristic vector of the minimum value cut
   913 }
   479     ///which is inclusionwise maximum. It is computed by processing a
   914 
   480     ///backward bfs from the target node \c t in the residual graph.
   915 #endif
   481     ///\pre \ref phase2() or run() should already be run.
       
   482     template<typename _CutMap>
       
   483     void maxMinCut(_CutMap& M) const {
       
   484 
       
   485       for(NodeIt v(*_g) ; v!=INVALID; ++v) M.set(v, true);
       
   486 
       
   487       std::queue<Node> queue;
       
   488 
       
   489       M.set(_target,false);
       
   490       queue.push(_target);
       
   491 
       
   492       while (!queue.empty()) {
       
   493         Node w=queue.front();
       
   494 	queue.pop();
       
   495 
       
   496 	for(InEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   497 	  Node v=_g->source(e);
       
   498 	  if (M[v] && _surely.positive((*_capacity)[e] - (*_flow)[e]) ) {
       
   499 	    queue.push(v);
       
   500 	    M.set(v, false);
       
   501 	  }
       
   502 	}
       
   503 
       
   504 	for(OutEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   505 	  Node v=_g->target(e);
       
   506 	  if (M[v] && _surely.positive((*_flow)[e]) ) {
       
   507 	    queue.push(v);
       
   508 	    M.set(v, false);
       
   509 	  }
       
   510 	}
       
   511       }
       
   512     }
       
   513 
       
   514     ///Sets the source node to \c _s.
       
   515 
       
   516     ///Sets the source node to \c _s.
       
   517     /// 
       
   518     void source(Node _s) { 
       
   519       _source=_s; 
       
   520       if ( flow_prop != ZERO_FLOW ) flow_prop=NO_FLOW;
       
   521       status=AFTER_NOTHING; 
       
   522     }
       
   523 
       
   524     ///Returns the source node.
       
   525 
       
   526     ///Returns the source node.
       
   527     /// 
       
   528     Node source() const { 
       
   529       return _source;
       
   530     }
       
   531 
       
   532     ///Sets the target node to \c _t.
       
   533 
       
   534     ///Sets the target node to \c _t.
       
   535     ///
       
   536     void target(Node _t) { 
       
   537       _target=_t; 
       
   538       if ( flow_prop == GEN_FLOW ) flow_prop=PRE_FLOW;
       
   539       status=AFTER_NOTHING; 
       
   540     }
       
   541 
       
   542     ///Returns the target node.
       
   543 
       
   544     ///Returns the target node.
       
   545     /// 
       
   546     Node target() const { 
       
   547       return _target;
       
   548     }
       
   549 
       
   550     /// Sets the edge map of the capacities to _cap.
       
   551 
       
   552     /// Sets the edge map of the capacities to _cap.
       
   553     /// 
       
   554     void capacityMap(const CapacityMap& _cap) { 
       
   555       _capacity=&_cap; 
       
   556       status=AFTER_NOTHING; 
       
   557     }
       
   558     /// Returns a reference to capacity map.
       
   559 
       
   560     /// Returns a reference to capacity map.
       
   561     /// 
       
   562     const CapacityMap &capacityMap() const { 
       
   563       return *_capacity;
       
   564     }
       
   565 
       
   566     /// Sets the edge map of the flows to _flow.
       
   567 
       
   568     /// Sets the edge map of the flows to _flow.
       
   569     /// 
       
   570     void flowMap(FlowMap& _f) { 
       
   571       _flow=&_f; 
       
   572       flow_prop=NO_FLOW;
       
   573       status=AFTER_NOTHING; 
       
   574     }
       
   575      
       
   576     /// Returns a reference to flow map.
       
   577 
       
   578     /// Returns a reference to flow map.
       
   579     /// 
       
   580     const FlowMap &flowMap() const { 
       
   581       return *_flow;
       
   582     }
       
   583 
       
   584   private:
       
   585 
       
   586     int push(Node w, NNMap& next, VecNode& first) {
       
   587 
       
   588       int lev=level[w];
       
   589       Num exc=excess[w];
       
   590       int newlevel=_node_num;       //bound on the next level of w
       
   591 
       
   592       for(OutEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   593 	if ( !_surely.positive((*_capacity)[e] - (*_flow)[e])) continue;
       
   594 	Node v=_g->target(e);
       
   595 	
       
   596 	if( lev > level[v] ) { //Push is allowed now
       
   597 	  
       
   598 	  if ( excess[v] == 0 && v!=_target && v!=_source ) {
       
   599 	    next.set(v,first[level[v]]);
       
   600 	    first[level[v]]=v;
       
   601 	  }
       
   602 
       
   603 	  Num cap=(*_capacity)[e];
       
   604 	  Num flo=(*_flow)[e];
       
   605 	  Num remcap=cap-flo;
       
   606 	  
       
   607 	  if ( ! _surely.less(remcap, exc) ) { //A nonsaturating push.
       
   608 	    
       
   609 	    _flow->set(e, flo+exc);
       
   610 	    excess.set(v, excess[v]+exc);
       
   611 	    exc=0;
       
   612 	    break;
       
   613 
       
   614 	  } else { //A saturating push.
       
   615 	    _flow->set(e, cap);
       
   616 	    excess.set(v, excess[v]+remcap);
       
   617 	    exc-=remcap;
       
   618 	  }
       
   619 	} else if ( newlevel > level[v] ) newlevel = level[v];
       
   620       } //for out edges wv
       
   621 
       
   622       if ( exc != 0 ) {
       
   623 	for(InEdgeIt e(*_g,w) ; e!=INVALID; ++e) {
       
   624 	  
       
   625 	  if ( !_surely.positive((*_flow)[e]) ) continue;
       
   626 	  Node v=_g->source(e);
       
   627 	  
       
   628 	  if( lev > level[v] ) { //Push is allowed now
       
   629 
       
   630 	    if ( excess[v] == 0 && v!=_target && v!=_source ) {
       
   631 	      next.set(v,first[level[v]]);
       
   632 	      first[level[v]]=v;
       
   633 	    }
       
   634 
       
   635 	    Num flo=(*_flow)[e];
       
   636 
       
   637 	    if ( !_surely.less(flo, exc) ) { //A nonsaturating push.
       
   638 
       
   639 	      _flow->set(e, flo-exc);
       
   640 	      excess.set(v, excess[v]+exc);
       
   641 	      exc=0;
       
   642 	      break;
       
   643 	    } else {  //A saturating push.
       
   644 
       
   645 	      excess.set(v, excess[v]+flo);
       
   646 	      exc-=flo;
       
   647 	      _flow->set(e,0);
       
   648 	    }
       
   649 	  } else if ( newlevel > level[v] ) newlevel = level[v];
       
   650 	} //for in edges vw
       
   651 
       
   652       } // if w still has excess after the out edge for cycle
       
   653 
       
   654       excess.set(w, exc);
       
   655       
       
   656       return newlevel;
       
   657     }
       
   658     
       
   659     
       
   660     
       
   661     void preflowPreproc(VecNode& first, NNMap& next, 
       
   662 			VecNode& level_list, NNMap& left, NNMap& right)
       
   663     {
       
   664       for(NodeIt v(*_g); v!=INVALID; ++v) level.set(v,_node_num);
       
   665       std::queue<Node> bfs_queue;
       
   666       
       
   667       if ( flow_prop == GEN_FLOW || flow_prop == PRE_FLOW ) {
       
   668 	//Reverse_bfs from t in the residual graph,
       
   669 	//to find the starting level.
       
   670 	level.set(_target,0);
       
   671 	bfs_queue.push(_target);
       
   672 	
       
   673 	while ( !bfs_queue.empty() ) {
       
   674 	  
       
   675 	  Node v=bfs_queue.front();
       
   676 	  bfs_queue.pop();
       
   677 	  int l=level[v]+1;
       
   678 	  
       
   679 	  for(InEdgeIt e(*_g,v) ; e!=INVALID; ++e) {
       
   680 	    if ( !_surely.positive((*_capacity)[e] - (*_flow)[e] )) continue;
       
   681 	    Node w=_g->source(e);
       
   682 	    if ( level[w] == _node_num && w != _source ) {
       
   683 	      bfs_queue.push(w);
       
   684 	      Node z=level_list[l];
       
   685 	      if ( z!=INVALID ) left.set(z,w);
       
   686 	      right.set(w,z);
       
   687 	      level_list[l]=w;
       
   688 	      level.set(w, l);
       
   689 	    }
       
   690 	  }
       
   691 	  
       
   692 	  for(OutEdgeIt e(*_g,v) ; e!=INVALID; ++e) {
       
   693 	    if ( !_surely.positive((*_flow)[e]) ) continue;
       
   694 	    Node w=_g->target(e);
       
   695 	    if ( level[w] == _node_num && w != _source ) {
       
   696 	      bfs_queue.push(w);
       
   697 	      Node z=level_list[l];
       
   698 	      if ( z!=INVALID ) left.set(z,w);
       
   699 	      right.set(w,z);
       
   700 	      level_list[l]=w;
       
   701 	      level.set(w, l);
       
   702 	    }
       
   703 	  }
       
   704 	} //while
       
   705       } //if
       
   706 
       
   707 
       
   708       switch (flow_prop) {
       
   709 	case NO_FLOW:  
       
   710 	for(EdgeIt e(*_g); e!=INVALID; ++e) _flow->set(e,0);
       
   711 	case ZERO_FLOW:
       
   712 	for(NodeIt v(*_g); v!=INVALID; ++v) excess.set(v,0);
       
   713 	
       
   714 	//Reverse_bfs from t, to find the starting level.
       
   715 	level.set(_target,0);
       
   716 	bfs_queue.push(_target);
       
   717 	
       
   718 	while ( !bfs_queue.empty() ) {
       
   719 	  
       
   720 	  Node v=bfs_queue.front();
       
   721 	  bfs_queue.pop();
       
   722 	  int l=level[v]+1;
       
   723 	  
       
   724 	  for(InEdgeIt e(*_g,v) ; e!=INVALID; ++e) {
       
   725 	    Node w=_g->source(e);
       
   726 	    if ( level[w] == _node_num && w != _source ) {
       
   727 	      bfs_queue.push(w);
       
   728 	      Node z=level_list[l];
       
   729 	      if ( z!=INVALID ) left.set(z,w);
       
   730 	      right.set(w,z);
       
   731 	      level_list[l]=w;
       
   732 	      level.set(w, l);
       
   733 	    }
       
   734 	  }
       
   735 	}
       
   736 	
       
   737 	//the starting flow
       
   738 	for(OutEdgeIt e(*_g,_source) ; e!=INVALID; ++e) {
       
   739 	  Num c=(*_capacity)[e];
       
   740 	  if ( !_surely.positive(c) ) continue;
       
   741 	  Node w=_g->target(e);
       
   742 	  if ( level[w] < _node_num ) {
       
   743 	    if ( excess[w] == 0 && w!=_target ) { //putting into the stack
       
   744 	      next.set(w,first[level[w]]);
       
   745 	      first[level[w]]=w;
       
   746 	    }
       
   747 	    _flow->set(e, c);
       
   748 	    excess.set(w, excess[w]+c);
       
   749 	  }
       
   750 	}
       
   751 	break;
       
   752 
       
   753 	case GEN_FLOW:
       
   754 	for(NodeIt v(*_g); v!=INVALID; ++v) excess.set(v,0);
       
   755 	{
       
   756 	  Num exc=0;
       
   757 	  for(InEdgeIt e(*_g,_target) ; e!=INVALID; ++e) exc+=(*_flow)[e];
       
   758 	  for(OutEdgeIt e(*_g,_target) ; e!=INVALID; ++e) exc-=(*_flow)[e];
       
   759           if (!_surely.positive(exc)) {
       
   760             exc = 0;
       
   761           }
       
   762           excess.set(_target,exc);
       
   763 	}
       
   764 
       
   765 	//the starting flow
       
   766 	for(OutEdgeIt e(*_g,_source); e!=INVALID; ++e)	{
       
   767 	  Num rem=(*_capacity)[e]-(*_flow)[e];
       
   768 	  if ( !_surely.positive(rem) ) continue;
       
   769 	  Node w=_g->target(e);
       
   770 	  if ( level[w] < _node_num ) {
       
   771 	    if ( excess[w] == 0 && w!=_target ) { //putting into the stack
       
   772 	      next.set(w,first[level[w]]);
       
   773 	      first[level[w]]=w;
       
   774 	    }   
       
   775 	    _flow->set(e, (*_capacity)[e]);
       
   776 	    excess.set(w, excess[w]+rem);
       
   777 	  }
       
   778 	}
       
   779 	
       
   780 	for(InEdgeIt e(*_g,_source); e!=INVALID; ++e) {
       
   781 	  if ( !_surely.positive((*_flow)[e]) ) continue;
       
   782 	  Node w=_g->source(e);
       
   783 	  if ( level[w] < _node_num ) {
       
   784 	    if ( excess[w] == 0 && w!=_target ) {
       
   785 	      next.set(w,first[level[w]]);
       
   786 	      first[level[w]]=w;
       
   787 	    }  
       
   788 	    excess.set(w, excess[w]+(*_flow)[e]);
       
   789 	    _flow->set(e, 0);
       
   790 	  }
       
   791 	}
       
   792 	break;
       
   793 
       
   794 	case PRE_FLOW:	
       
   795 	//the starting flow
       
   796 	for(OutEdgeIt e(*_g,_source) ; e!=INVALID; ++e) {
       
   797 	  Num rem=(*_capacity)[e]-(*_flow)[e];
       
   798 	  if ( !_surely.positive(rem) ) continue;
       
   799 	  Node w=_g->target(e);
       
   800 	  if ( level[w] < _node_num ) _flow->set(e, (*_capacity)[e]);
       
   801 	}
       
   802 	
       
   803 	for(InEdgeIt e(*_g,_source) ; e!=INVALID; ++e) {
       
   804 	  if ( !_surely.positive((*_flow)[e]) ) continue;
       
   805 	  Node w=_g->source(e);
       
   806 	  if ( level[w] < _node_num ) _flow->set(e, 0);
       
   807 	}
       
   808 	
       
   809 	//computing the excess
       
   810 	for(NodeIt w(*_g); w!=INVALID; ++w) {
       
   811 	  Num exc=0;
       
   812 	  for(InEdgeIt e(*_g,w); e!=INVALID; ++e) exc+=(*_flow)[e];
       
   813 	  for(OutEdgeIt e(*_g,w); e!=INVALID; ++e) exc-=(*_flow)[e];
       
   814           if (!_surely.positive(exc)) {
       
   815             exc = 0;
       
   816           }
       
   817 	  excess.set(w,exc);
       
   818 	  
       
   819 	  //putting the active nodes into the stack
       
   820 	  int lev=level[w];
       
   821 	    if ( exc != 0 && lev < _node_num && Node(w) != _target ) {
       
   822 	      next.set(w,first[lev]);
       
   823 	      first[lev]=w;
       
   824 	    }
       
   825 	}
       
   826 	break;
       
   827       } //switch
       
   828     } //preflowPreproc
       
   829 
       
   830 
       
   831     void relabel(Node w, int newlevel, VecNode& first, NNMap& next, 
       
   832 		 VecNode& level_list, NNMap& left,
       
   833 		 NNMap& right, int& b, int& k, bool what_heur )
       
   834     {
       
   835 
       
   836       int lev=level[w];
       
   837 
       
   838       Node right_n=right[w];
       
   839       Node left_n=left[w];
       
   840 
       
   841       //unlacing starts
       
   842       if ( right_n!=INVALID ) {
       
   843 	if ( left_n!=INVALID ) {
       
   844 	  right.set(left_n, right_n);
       
   845 	  left.set(right_n, left_n);
       
   846 	} else {
       
   847 	  level_list[lev]=right_n;
       
   848 	  left.set(right_n, INVALID);
       
   849 	}
       
   850       } else {
       
   851 	if ( left_n!=INVALID ) {
       
   852 	  right.set(left_n, INVALID);
       
   853 	} else {
       
   854 	  level_list[lev]=INVALID;
       
   855 	}
       
   856       }
       
   857       //unlacing ends
       
   858 
       
   859       if ( level_list[lev]==INVALID ) {
       
   860 
       
   861 	//gapping starts
       
   862 	for (int i=lev; i!=k ; ) {
       
   863 	  Node v=level_list[++i];
       
   864 	  while ( v!=INVALID ) {
       
   865 	    level.set(v,_node_num);
       
   866 	    v=right[v];
       
   867 	  }
       
   868 	  level_list[i]=INVALID;
       
   869 	  if ( !what_heur ) first[i]=INVALID;
       
   870 	}
       
   871 
       
   872 	level.set(w,_node_num);
       
   873 	b=lev-1;
       
   874 	k=b;
       
   875 	//gapping ends
       
   876 
       
   877       } else {
       
   878 
       
   879 	if ( newlevel == _node_num ) level.set(w,_node_num);
       
   880 	else {
       
   881 	  level.set(w,++newlevel);
       
   882 	  next.set(w,first[newlevel]);
       
   883 	  first[newlevel]=w;
       
   884 	  if ( what_heur ) b=newlevel;
       
   885 	  if ( k < newlevel ) ++k;      //now k=newlevel
       
   886 	  Node z=level_list[newlevel];
       
   887 	  if ( z!=INVALID ) left.set(z,w);
       
   888 	  right.set(w,z);
       
   889 	  left.set(w,INVALID);
       
   890 	  level_list[newlevel]=w;
       
   891 	}
       
   892       }
       
   893     } //relabel
       
   894 
       
   895   }; 
       
   896 
       
   897   ///\ingroup max_flow
       
   898   ///\brief Function type interface for Preflow algorithm.
       
   899   ///
       
   900   ///Function type interface for Preflow algorithm.
       
   901   ///\sa Preflow
       
   902   template<class GR, class CM, class FM>
       
   903   Preflow<GR,typename CM::Value,CM,FM> preflow(const GR &g,
       
   904 			    typename GR::Node source,
       
   905 			    typename GR::Node target,
       
   906 			    const CM &cap,
       
   907 			    FM &flow
       
   908 			    )
       
   909   {
       
   910     return Preflow<GR,typename CM::Value,CM,FM>(g,source,target,cap,flow);
       
   911   }
       
   912 
       
   913 } //namespace lemon
       
   914 
       
   915 #endif //LEMON_PREFLOW_H