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Weighted Matchings

  • Dieter Jungnickel
Part of the Algorithms and Computation in Mathematics book series (AACIM, volume 5)

Abstract

In Chap.  13, we studied matchings of maximal cardinality. The present chapter discusses the more general case of weighted matchings, that is, the problem of finding a matching of maximal weight (with respect to a given weight function on the edges). In the bipartite case, this problem is equivalent to the assignment problem considered before, so that the methods discussed in Chap.  10 apply. Nevertheless, we will give a further algorithm for the bipartite case, the so-called Hungarian algorithm, as this is one of the best known and most important combinatorial algorithms. We then proceed by explaining the connection between matching problems and the theory of linear programming, even though we generally avoid linear programs in this book. We need this to see the deeper reason why the approach used in the Hungarian algorithm works: its success is due to the particularly simple structure of the corresponding polytope, and ultimately to the total unimodularity of the incidence matrix of a bipartite graph. In this context, the reason why the determination of maximal matchings (weighted or not) is considerably more difficult for arbitrary graphs than for bipartite ones will become apparent. As it would make little sense to describe an algorithm for the weighted matching problem in general graphs without using more of the theory of linear programming, no such algorithm is presented in this book. Nevertheless, we will include three interesting applications of weighted matchings: the so-called Chinese postman problem (featuring a postman who wants an optimal route for delivering his mail); the determination of shortest paths for the case where edges of negative weight occur; and the decoding of graphical codes. We shall conclude with a few remarks about matching problems with certain additional restrictions—a situation which occurs quite often in practice; we will see that such problems tend to be inherently more difficult.

Keywords

Bipartite Graph Perfect Match Complete Bipartite Graph Optimal Match Arbitrary Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dieter Jungnickel
    • 1
  1. 1.Institut für MathematikUniversität AugsburgAugsburgGermany

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