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Local Approximation of the Maximum Cut in Regular Graphs

机译:正则图中最大割的局部逼近

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This paper is devoted to the distributed complexity of finding an approximation of the maximum cut in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communication and achieves an approximation ratio of at least 1/2 in average. When the graph is d-regular and triangle-free, a slightly better approximation ratio can be achieved with a randomized algorithm running in a single round. Here, we investigate the round complexity of deterministic distributed algorithms for MaxCut in regular graphs. We first prove that if G is d-regular, with d even and fixed, no deterministic algorithm running in a constant number of rounds can achieve a constant approximation ratio. We then give a simple one-round deterministic algorithm achieving an approximation ratio of 1/d for d-regular graphs with d odd. We show that this is best possible in several ways, and in particular no deterministic algorithm with approximation ratio 1/d + ε (with ε > 0) can run in a constant number of rounds. We also prove results of a similar flavour for the MaxDi-Cut problem in regular oriented graphs, where we want to maximize the number of arcs oriented from the left part to the right part of the cut.
机译:本文致力于发现图中最大割的近似值的分布式复杂性。经典算法包括让每个顶点随机均匀地选择其切割边。这不需要任何通信,并且平均平均逼近率至少为1/2。当图是d-正则且无三角形时,可以通过在单回合中运行的随机算法来获得更好的近似率。在这里,我们研究规则图中MaxCut的确定性分布式算法的轮次复杂度。我们首先证明,如果G是d-正则的,且d为偶数且固定,则以恒定轮数运行的确定性算法无法获得恒定的近似率。然后,我们给出一个简单的一轮确定性算法,以d为奇数的d-正则图的近似比达到1 / d。我们证明这是最好的几种方法,尤其是没有近似比率为1 / d +ε(ε> 0)的确定性算法可以在恒定的轮数中运行。我们还证明了在规则取向图中MaxDi-Cut问题具有类似风味的结果,在该图中,我们希望最大化从切口的左侧部分到右侧部分的弧形数量。

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