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A maximal matching is a matching M of a graph G that is not a subset of any other matching. A matching M of a graph G is maximal if every edge in G has a non-empty intersection with at least one edge in M. The following figure shows examples of maximal matchings (red) in three graphs. A maximum matching (also known as maximum-cardinality ...
In particular, computing the matching polynomial on n-vertex graphs of treewidth k is fixed-parameter tractable: there exists an algorithm whose running time, for any fixed constant k, is a polynomial in n with an exponent that does not depend on k (Courcelle, Makowsky & Rotics 2001).
The case of exact graph matching is known as the graph isomorphism problem. [1] The problem of exact matching of a graph to a part of another graph is called subgraph isomorphism problem. Inexact graph matching refers to matching problems when exact matching is impossible, e.g., when the number of vertices in the two graphs are different. In ...
The fifth corner (1/2,1/2,1/2) does not represent a matching - it represents a fractional matching in which each edge is "half in, half out". Note that this is the largest fractional matching in this graph - its weight is 3/2, in contrast to the three integral matchings whose size is only 1. As another example, in the 4-cycle there are 4 edges.
A perfect matching can only occur when the graph has an even number of vertices. A near-perfect matching is one in which exactly one vertex is unmatched. This can only occur when the graph has an odd number of vertices, and such a matching must be maximum. In the above figure, part (c) shows a near-perfect matching.
A fractional matching in a hypergraph is a function that assigns a fraction in [0,1] to each hyperedge, such that for every vertex v in V, the sum of fractions of hyperedges containing v is at most 1. A matching is a special case of a fractional matching in which all fractions are either 0 or 1.
Giving everyone their second choice ensures that any other match would be disliked by one of the parties. In general, the family of solutions to any instance of the stable marriage problem can be given the structure of a finite distributive lattice , and this structure leads to efficient algorithms for several problems on stable marriages.
INPUT: Graph G, matching M on G OUTPUT: augmenting path P in G or empty path if none found B01 function find_augmenting_path(G, M) : P B02 F ← empty forest B03 unmark all vertices and edges in G, mark all edges of M B05 for each exposed vertex v do B06 create a singleton tree { v} and add the tree to F B07 end for B08 while there is an ...