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Improved Distributed Approximate Matching

delete2015-11-02
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AI
DOI:10.1145/2786753delete
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摘要

摘要

En 中文
We present distributed network algorithms to compute weighted and unweighted matchings with improved approximation ratios and running times. The computational model is a network of processors exchanging O (log n )-bit messages (the CONGEST model). For unweighted graphs, we give an algorithm providing (1-ϵ)-approximation in O (log n ) time for any constant ϵ>0, improving on the classical ½-approximation in O log n ) time of Israeli and Itai [1986]. The time complexity of the algorithm depends on 1⁃ϵ exponentially in the general case, and polynomially in bipartite graphs. For weighted graphs, we present another algorithm which provides (½-ϵ) approximation in general graphs in O (logϵ -1 log n ) time, improving on the previously known algorithms which attain (¼-ϵ)-approximation in O (log n ) time or ½-approximation in O ( n ) time. All our algorithms are randomized: the complexity bounds hold both with high probability and for the expected running time.

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