org.jgrapht.alg.flow

• Type Parameters:
V - the graph vertex type
E - the graph edge type

public class PadbergRaoOddMinimumCutset<V,E>
extends Object
Implementation of the algorithm by Padberg and Rao to compute Odd Minimum Cut-Sets. Let $G=(V,E)$ be an undirected, simple weighted graph, where all edge weights are positive. Let $T \subset V$ with $|T|$ even, be a set of vertices that are labelled odd. A cut-set $(U:V-U)$ is called odd if $|T \cap U|$ is an odd number. Let $c(U:V-U)$ be the weight of the cut, that is, the sum of weights of the edges which have exactly one endpoint in $U$ and one endpoint in $V-U$. The problem of finding an odd minimum cut-set in $G$ is stated as follows: Find $W \subseteq V$ such that $c(W:V-W)=min(c(U:V-U)|U \subseteq V, |T \cap U|$ is odd).

The algorithm has been published in: Padberg, M. Rao, M. Odd Minimum Cut-Sets and b-Matchings. Mathematics of Operations Research, 7(1), p67-80, 1982. A more concise description is published in: Letchford, A. Reinelt, G. Theis, D. Odd minimum cut-sets and b-matchings revisited. SIAM Journal of Discrete Mathematics, 22(4), p1480-1487, 2008.

The runtime complexity of this algorithm is dominated by the runtime complexity of the algorithm used to compute A Gomory-Hu tree on graph $G$. Consequently, the runtime complexity of this class is $O(V^4)$.

This class does not support changes to the underlying graph. The behavior of this class is undefined when the graph is modified after instantiating this class.

Author:
Joris Kinable
• ### Constructor Summary

Constructors
Constructor and Description
PadbergRaoOddMinimumCutset(Graph<V,E> network)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
PadbergRaoOddMinimumCutset(Graph<V,E> network, double epsilon)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
PadbergRaoOddMinimumCutset(Graph<V,E> network, MinimumSTCutAlgorithm<V,E> minimumSTCutAlgorithm)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
• ### Method Summary

All Methods
Modifier and Type Method and Description
double calculateMinCut(Set<V> oddVertices, boolean useTreeCompression)
Calculates the minimum odd cut.
Set<E> getCutEdges()
Returns the set of edges which run from the source partition to the sink partition, in the $s-t$ cut obtained after the last invocation of calculateMinCut(Set, boolean)
Set<V> getSinkPartition()
Returns partition $V-W$ of the cut obtained after the last invocation of calculateMinCut(Set, boolean)
Set<V> getSourcePartition()
Returns partition $W$ of the cut obtained after the last invocation of calculateMinCut(Set, boolean)
static <V> boolean isOddVertexSet(Set<V> vertices, Set<V> oddVertices)
Convenience method which test whether the given set contains an odd number of odd-labeled nodes.
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

public PadbergRaoOddMinimumCutset(Graph<V,E> network)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
Parameters:
network - input graph

public PadbergRaoOddMinimumCutset(Graph<V,E> network,
double epsilon)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
Parameters:
network - input graph
epsilon - tolerance

public PadbergRaoOddMinimumCutset(Graph<V,E> network,
MinimumSTCutAlgorithm<V,E> minimumSTCutAlgorithm)
Creates a new instance of the PadbergRaoOddMinimumCutset algorithm.
Parameters:
network - input graph
minimumSTCutAlgorithm - algorithm used to calculate the Gomory-Hu tree
• ### Method Detail

• #### calculateMinCut

public double calculateMinCut(Set<V> oddVertices,
boolean useTreeCompression)
Calculates the minimum odd cut. The implementation follows Algorithm 1 in the paper Odd minimum cut sets and b-matchings revisited by Adam Letchford, Gerhard Reinelt and Dirk Theis. The original algorithm runs on a compressed Gomory-Hu tree: a cut-tree with the odd vertices as terminal vertices. This tree has $|T|-1$ edges as opposed to $|V|-1$ for a Gomory-Hu tree defined on the input graph $G$. This compression step can however be skipped. If you want to run the original algorithm in the paper, set the parameter useTreeCompression to true. Alternatively, experiment which setting of this parameter produces the fastest results. Both settings are guaranteed to find the optimal cut. Experiments on random graphs showed that setting useTreeCompression to false was on average a bit faster.
Parameters:
oddVertices - Set of vertices which are labeled 'odd'. Note that the number of vertices in this set must be even!
useTreeCompression - parameter indicating whether tree compression should be used (recommended: false).
Returns:
weight of the minimum odd cut.
• #### isOddVertexSet

public static <V> boolean isOddVertexSet(Set<V> vertices,
Set<V> oddVertices)
Convenience method which test whether the given set contains an odd number of odd-labeled nodes.
Type Parameters:
V - vertex type
Parameters:
vertices - input set
oddVertices - subset of vertices which are labeled odd
Returns:
true if the given set contains an odd number of odd-labeled nodes.
• #### getSourcePartition

public Set<V> getSourcePartition()
Returns partition $W$ of the cut obtained after the last invocation of calculateMinCut(Set, boolean)
Returns:
partition $W$
• #### getSinkPartition

public Set<V> getSinkPartition()
Returns partition $V-W$ of the cut obtained after the last invocation of calculateMinCut(Set, boolean)
Returns:
partition $V-W$
• #### getCutEdges

public Set<E> getCutEdges()
Returns the set of edges which run from the source partition to the sink partition, in the $s-t$ cut obtained after the last invocation of calculateMinCut(Set, boolean)
Returns:
set of edges which have one endpoint in the source partition and one endpoint in the sink partition.