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<Head>
<Title>Boost Graph Library: Cycle Canceling for  Min Cost Max Flow</Title>
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<H1><A NAME="sec:cycle_canceling">
<TT>cycle_canceling</TT>
</H1>

<PRE>
<i>// named parameter version</i>
template &lt;class <a href="./Graph.html">Graph</a>, class P, class T, class R&gt;
void cycle_canceling(
        Graph &amp;g, 
        const bgl_named_params&lt;P, T, R&gt; &amp; params  = <i>all defaults</i>)

<i>// non-named parameter version</i>
template &lt;class <a href="./Graph.html">Graph</a>, class Pred, class Distance, class Reversed, class ResidualCapacity, class Weight&gt;
void cycle_canceling(const Graph &amp; g, Weight weight, Reversed rev, ResidualCapacity residual_capacity, Pred pred, Distance distance) 
</PRE>

<P>
The <tt>cycle_canceling()</tt> function calculates the minimum cost flow of a network with given flow. See Section <a
href="./graph_theory_review.html#sec:network-flow-algorithms">Network
Flow Algorithms</a> for a description of maximum flow. 
For given flow values <i> f(u,v)</i>  function minimizes flow cost in such a way, that for each <i>v in V</i> the
 <i> sum<sub> u in V</sub>  f(v,u) </i> is preserved. Particularly if the input flow was the maximum flow, the function produces min cost max flow.
  

 The function calculates the flow values <i>f(u,v)</i> for all <i>(u,v)</i> in
<i>E</i>, which are returned in the form of the residual capacity
<i>r(u,v) = c(u,v) - f(u,v)</i>. 

<p>
There are several special requirements on the input graph and property
map parameters for this algorithm. First, the directed graph
<i>G=(V,E)</i> that represents the network must be augmented to
include the reverse edge for every edge in <i>E</i>.  That is, the
input graph should be <i>G<sub>in</sub> = (V,{E U
E<sup>T</sup>})</i>. The <tt>ReverseEdgeMap</tt> argument <tt>rev</tt>
must map each edge in the original graph to its reverse edge, that is
<i>(u,v) -> (v,u)</i> for all <i>(u,v)</i> in <i>E</i>. 
The <tt>WeightMap</tt> has to map each edge from <i>E<sup>T</sup></i> to <i>-weight</i> of its reversed edge.
Note that edges from <i>E</i> can have negative weights. 
<p>
If weights in the graph are nonnegative, the 
<a href="./successive_shortest_path_nonnegative_weights.html"><tt>successive_shortest_path_nonnegative_weights()</tt></a> 
might be better choice for min cost max flow.

<p>
The algorithm is described in <a
href="./bibliography.html#ahuja93:_network_flows">Network Flows</a>.

<p>
In each round algorithm augments the negative cycle (in terms of weight) in the residual graph.
If there is no negative cycle in the network, the cost is optimized.

<p>
Note that, although we mention capacity in the problem description, the actual algorithm doesn't have to now it.

<p> 
In order to find the cost of the result flow use:
<a href="./find_flow_cost.html"><tt>find_flow_cost()</tt></a>.


<H3>Where Defined</H3>

<P>
<a href="../../../boost/graph/successive_shortest_path_nonnegative_weights.hpp"><TT>boost/graph/successive_shortest_path_nonnegative_weights.hpp</TT></a>

<P>

<h3>Parameters</h3>

IN: <tt>Graph&amp; g</tt>
<blockquote>
  A directed graph. The
  graph's type must be a model of <a
  href="./VertexListGraph.html">VertexListGraph</a> and <a href="./IncidenceGraph.html">IncidenceGraph</a> For each edge
  <i>(u,v)</i> in the graph, the reverse edge <i>(v,u)</i> must also
  be in the graph.
</blockquote>
  
<h3>Named Parameters</h3>

  
IN/OUT: <tt>residual_capacity_map(ResidualCapacityEdgeMap res)</tt>
<blockquote>
  This maps edges to their residual capacity. The type must be a model
  of a mutable <a
  href="../../property_map/doc/LvaluePropertyMap.html">Lvalue Property
  Map</a>. The key type of the map must be the graph's edge descriptor
  type.<br>
  <b>Default:</b> <tt>get(edge_residual_capacity, g)</tt>
</blockquote>

IN: <tt>reverse_edge_map(ReverseEdgeMap rev)</tt>
<blockquote>
  An edge property map that maps every edge <i>(u,v)</i> in the graph
  to the reverse edge <i>(v,u)</i>. The map must be a model of
  constant <a href="../../property_map/doc/LvaluePropertyMap.html">Lvalue
  Property Map</a>. The key type of the map must be the graph's edge
  descriptor type.<br>
  <b>Default:</b> <tt>get(edge_reverse, g)</tt>
</blockquote>

IN: <tt>weight_map(WeightMap w)</tt>
<blockquote>
  The weight (also know as ``length'' or ``cost'') of each edge in the
  graph.  The <tt>WeightMap</tt> type must be a model of <a
  href="../../property_map/doc/ReadablePropertyMap.html">Readable Property
  Map</a>.  The key type for this property map must be the edge
  descriptor of the graph.  The value type for the weight map must be
  <i>Addable</i> with the distance map's value type. <br>
  <b>Default:</b> <tt>get(edge_weight, g)</tt><br>
</blockquote>

UTIL: <tt>predecessor_map(PredEdgeMap pred)</tt>
<blockquote>
  Use by the algorithm to store augmenting paths. The map must be a
  model of mutable <a
  href="../../property_map/doc/LvaluePropertyMap.html">Lvalue Property Map</a>.
  The key type must be the graph's vertex descriptor type and the
  value type must be the graph's edge descriptor type.<br>

  <b>Default:</b> an <a
  href="../../property_map/doc/iterator_property_map.html">
  <tt>iterator_property_map</tt></a> created from a <tt>std::vector</tt>
  of edge descriptors of size <tt>num_vertices(g)</tt> and
  using the <tt>i_map</tt> for the index map.
</blockquote>

UTIL: <tt>distance_map(DistanceMap d_map)</tt> 
<blockquote>
  The shortest path weight from the source vertex <tt>s</tt> to each
  vertex in the graph <tt>g</tt> is recorded in this property map. The
  shortest path weight is the sum of the edge weights along the
  shortest path.  The type <tt>DistanceMap</tt> must be a model of <a
  href="../../property_map/doc/ReadWritePropertyMap.html">Read/Write
  Property Map</a>. The vertex descriptor type of the graph needs to
  be usable as the key type of the distance map. 

  <b>Default:</b> <a
  href="../../property_map/doc/iterator_property_map.html">
  <tt>iterator_property_map</tt></a> created from a
  <tt>std::vector</tt> of the <tt>WeightMap</tt>'s value type of size
  <tt>num_vertices(g)</tt> and using the <tt>i_map</tt> for the index
  map.<br>

</blockquote>

IN: <tt>vertex_index_map(VertexIndexMap i_map)</tt>
<blockquote>
  Maps each vertex of the graph to a unique integer in the range
  <tt>[0, num_vertices(g))</tt>. This property map is only needed
  if the default for the distance or predecessor map is used.
  The vertex index map must be a model of <a
  href="../../property_map/doc/ReadablePropertyMap.html">Readable Property
  Map</a>. The key type of the map must be the graph's vertex
  descriptor type.<br>
  <b>Default:</b> <tt>get(vertex_index, g)</tt>
    Note: if you use this default, make sure your graph has
    an internal <tt>vertex_index</tt> property. For example,
    <tt>adjacency_list</tt> with <tt>VertexList=listS</tt> does
    not have an internal <tt>vertex_index</tt> property.
</blockquote>

<h3>Complexity</h3>
In the integer capacity and weight case, if <i>C</i> is the initial cost of the flow, then the complexity is <i> O(C * |V| * |E|)</i>, 
where <i>O(|E|* |V|)</i> is the complexity of the bellman ford shortest paths algorithm and <i>C</i> is upper bound on number of iteration. 
In many real world cases number of iterations is much smaller than <i>C</i>.


<h3>Example</h3>

The program in <a
href="../example/cycle_canceling_example.cpp"><tt>example/cycle_canceling_example.cpp</tt></a>.


<h3>See Also</h3>

<a href="./successive_shortest_path_nonnegative_weights.html"><tt>successive_shortest_path_nonnegative_weights()</tt></a><br>
<a href="./find_flow_cost.html"><tt>find_flow_cost()</tt></a>.

<br>
<HR>
<TABLE>
<TR valign=top>
<TD nowrap>Copyright &copy; 2013</TD><TD>
Piotr Wygocki, University of Warsaw (<A HREF="mailto:wygos@mimuw.edu.pl">wygos at mimuw.edu.pl</A>)
</TD></TR></TABLE>

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