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We often need to be able to match two different sets of data together, such that as many elements as possible from one set are associated with an element from the other set.

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The most obvious observation is the lack of a clear ranking between algorithms.

This allows us to keep all potential matches while providing a fast algorithm, scaling linearly with respect to the number of sessions.

Minimum edge dominating set optimisation version is the problem GT3 in Appendix B page 370.

This is referred to as NIL, and we use this to indicate that a vertex in the graph is not currently paired with anything: At the start of each phase, we perform a across the entire graph to discover if any augmenting paths exist, and the lengths of them.

Sirakoulis, in , 2011 2.

We next investigate the matching algorithms that can achieve this ideal.

Thanks for contributing an answer to Stack Overflow! The CSU EBGM algorithm as distributed and run by default in Version 5.