“Matching (graph theory)”的意思、由来-开放百科全书

In the mathematical discipline of graph theory, a matching or independent edge set in a graph is a set of edges without common vertices. Finding a matching in a bipartite graph can be treated as a network flow problem.

Definition

Given a graph G = (V,E), a matching M in G is a set of pairwise non-adjacent edges, none of which are loops; that is, no two edges share a common vertex.

A vertex is matched (or saturated) if it is an endpoint of one of the edges in the matching. Otherwise the vertex is unmatched.

A maximal matching is a matching M of a graph G with the property that if any edge not in M is added to M, it is no longer a matching, that is, M is maximal if it is not a subset of any other matching in graph G. In other words, 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 matching^[1]) is a matching that contains the largest possible number of edges. There may be many maximum matchings. The matching number

of a graph

is the size of a maximum matching. Note that every maximum matching is maximal, but not every maximal matching is a maximum matching. The following figure shows examples of maximum matchings in the same three graphs.

A perfect matching (a.k.a. 1-factor) is a matching which matches all vertices of the graph. That is, every vertex of the graph is incident to exactly one edge of the matching. Figure (b) above is an example of a perfect matching. Every perfect matching is maximum and hence maximal. In some literature, the term complete matching is used. In the above figure, only part (b) shows a perfect matching. A perfect matching is also a minimum-size edge cover. Thus, {{math|ν(G) ≤ ρ(G) }}, that is, the size of a maximum matching is no larger than the size of a minimum edge cover. 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. If, for every vertex in a graph, there is a near-perfect matching that omits only that vertex, the graph is also called factor-critical.

One can prove that a matching is maximum if and only if it does not have any augmenting path. (This result is sometimes called Berge's lemma.)

Properties

In any graph without isolated vertices, the sum of the matching number and the edge covering number equals the number of vertices.^[4] If there is a perfect matching, then both the matching number and the edge cover number are {{math|{{!}}V {{!}} / 2}}.

If {{math|A}} and {{math|B}} are two maximal matchings, then {{math|{{!}}A{{!}} ≤ 2{{!}}B{{!}}}} and {{math|{{!}}B{{!}} ≤ 2{{!}}A{{!}}}}. To see this, observe that each edge in {{math|B \\ A}} can be adjacent to at most two edges in {{math|A \\ B}} because {{math|A}} is a matching; moreover each edge in {{math|A \\ B}} is adjacent to an edge in {{math|B \\ A}} by maximality of {{math|B}}, hence

In particular, this shows that any maximal matching is a 2-approximation of a maximum matching and also a 2-approximation of a minimum maximal matching. This inequality is tight: for example, if {{math|G}} is a path with 3 edges and 4 vertices, the size of a minimum maximal matching is 1 and the size of a maximum matching is 2.

Matching polynomials

A generating function of the number of k-edge matchings in a graph is called a matching polynomial. Let G be a graph and m_k be the number of k-edge matchings. One matching polynomial of G is

where n is the number of vertices in the graph. Each type has its uses; for more information see the article on matching polynomials.

Algorithms and computational complexity

Maximum matching

A fundamental problem in combinatorial optimization is finding a maximum matching. This problem has various algorithms for different classes of graphs:

Maximal matchings

A maximal matching can be found with a simple greedy algorithm. A maximum matching is also a maximal matching, and hence it is possible to find a largest maximal matching in polynomial time. However, no polynomial-time algorithm is known for finding a minimum maximal matching, that is, a maximal matching that contains the smallest possible number of edges.

Note that a maximal matching with k edges is an edge dominating set with k edges. Conversely, if we are given a minimum edge dominating set with k edges, we can construct a maximal matching with k edges in polynomial time. Therefore, the problem of finding a minimum maximal matching is essentially equal to the problem of finding a minimum edge dominating set.^[10] Both of these two optimization problems are known to be NP-hard; the decision versions of these problems are classical examples of NP-complete problems.^[11] Both problems can be approximated within factor 2 in polynomial time: simply find an arbitrary maximal matching M.^[12]

Counting problems

The number of matchings in a graph is known as the Hosoya index of the graph. It is #P-complete to compute this quantity, even for bipartite graphs.^[13] It is also #P-complete to count perfect matchings, even in bipartite graphs, because computing the permanent of an arbitrary 0–1 matrix (another #P-complete problem) is the same as computing the number of perfect matchings in the bipartite graph having the given matrix as its biadjacency matrix. However, there exists a fully polynomial time randomized approximation scheme for counting the number of bipartite matchings.^[14] A remarkable theorem of Kasteleyn states that the number of perfect matchings in a planar graph can be computed exactly in polynomial time via the FKT algorithm.

The number of perfect matchings in a complete graph K_n (with n even) is given by the double factorial (n − 1)!!.^[15] The numbers of matchings in complete graphs, without constraining the matchings to be perfect, are given by the telephone numbers.^[16]

Finding all maximally-matchable edges

One of the basic problems in matching theory is to find in a given graph all edges that may be extended to a maximum matching

in the graph. (Such edges are called maximally-matchable edges, or allowed edges.) The best deterministic algorithm for solving this problem in general graphs runs in time

In the case of bipartite graphs, it is possible to find a single maximum matching and then use it in order to find all

one of the maximum matchings is known upfront,^[20] the overall runtime of the algorithm is

Online bipartite matching

The problem of developing an online algorithm for matching was first considered by Karp et al.^[21] In the online setting, nodes on one side of the bipartite graph arrive one at a time and must either be immediately matched to the other side of the graph or discarded. This is a natural generalization of the secretary problem and has applications to online ad auctions.^[22] The best online algorithm, for the unweighted maximization case with a random arrival model, attains a competitive ratio of

.^[23]

Characterizations and notes

Kőnig's theorem states that, in bipartite graphs, the maximum matching is equal in size to the minimum vertex cover. Via this result, the minimum vertex cover, maximum independent set, and maximum vertex biclique problems may be solved in polynomial time for bipartite graphs.

Hall's marriage theorem provides a characterization of bipartite graphs which have a perfect matching and the Tutte theorem provides a characterization for arbitrary graphs.

A perfect matching is a spanning 1-regular subgraph, a.k.a. a 1-factor. In general, a spanning k-regular subgraph is a k-factor.

Applications

Matching in general graphs

Matching in bipartite graphs

See also

References

1. ^Alan Gibbons, Algorithmic Graph Theory, Cambridge University Press, 1985, Chapter 5.
2. ^http://diestel-graph-theory.com/basic.html
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8. ^{{citation|last1=Borradaile|first1=Glencora|title=Multiple-source multiple-sink maximum flow in directed planar graphs in near-linear time|journal=SIAM Journal on Computing|volume=46|issue=4|pages=1280–1303|year=2017|arxiv=1105.2228|doi=10.1137/15M1042929|mr=3681377|last2=Klein|first2=Philip N.|last3=Mozes|first3=Shay|last4=Nussbaum|first4=Yahav|last5=Wulff–Nilsen|first5=Christian}}
9. ^{{citation|last1=Fredman|first1=Michael L.|title=Fibonacci heaps and their uses in improved network optimization algorithms|journal=Journal of the ACM|volume=34|issue=3|pages=596–615|year=1987|doi=10.1145/28869.28874|last2=Tarjan|first2=Robert Endre}}
10. ^{{citation | first1=Mihalis | last1=Yannakakis | first2=Fanica | last2=Gavril | title=Edge dominating sets in graphs | journal=SIAM Journal on Applied Mathematics | year=1980 | volume=38 | pages=364–372 | doi=10.1137/0138030 | issue=3}}.
11. ^{{citation | last1=Garey | first1=Michael R. | authorlink1=Michael R. Garey | last2=Johnson | first2=David S. | authorlink2=David S. Johnson | year = 1979 | title = A Guide to the Theory of NP-Completeness | publisher = W.H. Freeman | isbn=0-7167-1045-5}}. Edge dominating set (decision version) is discussed under the dominating set problem, which is the problem GT2 in Appendix A1.1. Minimum maximal matching (decision version) is the problem GT10 in Appendix A1.1.
12. ^{{citation | last1=Ausiello | first1=Giorgio | last2=Crescenzi | first2=Pierluigi | last3=Gambosi | first3=Giorgio | last4=Kann | first4=Viggo | last5=Marchetti-Spaccamela | first5=Alberto | last6=Protasi | first6=Marco | title=Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties | publisher=Springer | year=2003}}. Minimum edge dominating set (optimisation version) is the problem GT3 in Appendix B (page 370). Minimum maximal matching (optimisation version) is the problem GT10 in Appendix B (page 374). See also Minimum Edge Dominating Set and Minimum Maximal Matching in the web compendium.
13. ^Leslie Valiant, The Complexity of Enumeration and Reliability Problems, SIAM J. Comput., 8(3), 410–421
14. ^{{cite journal| last1 = Bezáková| first1 = Ivona| last2 = Štefankovič| first2 = Daniel| last3 = Vazirani| first3 = Vijay V.| authorlink3 = Vijay Vazirani| last4 = Vigoda| first4 = Eric| year = 2008| title = Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems| journal = SIAM Journal on Computing| volume = 37| issue = 5| pages = 1429–1454| doi = 10.1137/050644033| citeseerx= 10.1.1.80.687}}
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algorithm for ear decompositions of matching-covered graphs | title=Proc. ACM/SIAM Symposium on Discrete Algorithms (SODA) | year=2005 | pages=415–423 }}.
18. ^{{citation | first1=Michael O. | last1=Rabin | first2=Vijay V. | last2=Vazirani | title=Maximum matchings in general graphs through randomization | journal=Journal of Algorithms | year=1989 | volume=10 | pages=557–567 | doi=10.1016/0196-6774(89)90005-9}}.
19. ^{{citation | first1=Tamir | last1=Tassa | title=Finding all maximally-matchable edges in a bipartite graph| year=2012 | journal=Theoretical Computer Science | volume = 423 | pages = 50–58 | doi = 10.1016/j.tcs.2011.12.071}}.
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21. ^{{cite journal |title=An Optimal Algorithm for On-line Bipartite Matching |url=https://people.eecs.berkeley.edu/~vazirani/pubs/online.pdf}}
22. ^{{cite book |title=Online Matching and Ad Allocation |url=http://www.cs.cmu.edu/~arielpro/15896s15/docs/paper13b.pdf |accessdate=285-299}}
23. ^{{cite book |title=Online Matching and Ad Allocation |url=http://www.cs.cmu.edu/~arielpro/15896s15/docs/paper13b.pdf |accessdate=285-299}}
24. ^See, e.g., {{citation|title=On some solved and unsolved problems of chemical graph theory|last1=Trinajstić|first1=Nenad|authorlink=Nenad Trinajstić|last2=Klein|first2=Douglas J.|last3=Randić|first3=Milan |authorlink3=Milan Randić|journal=International Journal of Quantum Chemistry|year=1986|volume=30|issue=S20|pages=699–742|doi=10.1002/qua.560300762}}.