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Proceedings ArticleDOI

Almost-optimum speed-ups of algorithms for bipartite matching and related problems

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TLDR
An algorithm for the assignment problem (minimum cost perfect bipartite matching) that improves the best known sequential algorithm, and is within a factor of log of the bestknown bound for the problem without costs (maximum cardinality matching).
Abstract
We present algorithms for matching and related problems that run on an EREW PRAM with p processors. Given is a bipartite graph G with n vertices, m edges, and integral edge costs at most N in magnitude. We give an algorithm for the assignment problem (minimum cost perfect bipartite matching) that runs in O(√nm log (nN)(log(2p))/p) time and O(m) space, for p ≤ m/(√nlog2n). For p = 1 this improves the best known sequential algorithm, and is within a factor of log (nN) of the best known bound for the problem without costs (maximum cardinality matching). For p > 1 the time is within a factor of log p of optimum speed-up. Extensions include an algorithm for maximum cardinality bipartite matching with slightly better processor bounds, and similar results for bipartite degree-constrained subgraph problems (with and without costs). Our ideas also extend to general graph matching problems.

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References
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Proceedings ArticleDOI

Matching is as easy as matrix inversion

TL;DR: A new algorithm for finding a maximum matching in a general graph that its only computationally non-trivial step is the inversion of a single integer matrix, the isolating lemma, and other applications to parallel computation and randomized reductions are shown.
Journal ArticleDOI

Network Flow and Testing Graph Connectivity

TL;DR: An algorithm of Dinic for finding the maximum flow in a network is described and it is shown that if the vertex capacities are all equal to one, the algorithm requires at most $O(|V|^{1/2} \cdot |E|)$ time.