Improved Distributed Algorithms for the Lovász Local Lemma and Edge Coloring

Peter Davies
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引用次数: 6

Abstract

The Lov\'asz Local Lemma is a classic result in probability theory that is often used to prove the existence of combinatorial objects via the probabilistic method. In its simplest form, it states that if we have $n$ `bad events', each of which occurs with probability at most $p$ and is independent of all but $d$ other events, then under certain criteria on $p$ and $d$, all of the bad events can be avoided with positive probability. While the original proof was existential, there has been much study on the algorithmic Lov\'asz Local Lemma: that is, designing an algorithm which finds an assignment of the underlying random variables such that all the bad events are indeed avoided. Notably, the celebrated result of Moser and Tardos [JACM '10] also implied an efficient distributed algorithm for the problem, running in $O(\log^2 n)$ rounds. For instances with low $d$, this was improved to $O(d^2+\log^{O(1)}\log n)$ by Fischer and Ghaffari [DISC '17], a result that has proven highly important in distributed complexity theory (Chang and Pettie [SICOMP '19]). We give an improved algorithm for the Lov\'asz Local Lemma, providing a trade-off between the strength of the criterion relating $p$ and $d$, and the distributed round complexity. In particular, in the same regime as Fischer and Ghaffari's algorithm, we improve the round complexity to $O(\frac{d}{\log d}+\log^{O(1)}\log n)$. At the other end of the trade-off, we obtain a $\log^{O(1)}\log n$ round complexity for a substantially wider regime than previously known. As our main application, we also give the first $\log^{O(1)}\log n$-round distributed algorithm for the problem of $\Delta+o(\Delta)$-edge coloring a graph of maximum degree $\Delta$. This is an almost exponential improvement over previous results: no prior $\log^{o(1)} n$-round algorithm was known even for $2\Delta-2$-edge coloring.
Lovász局部引理和边着色的改进分布式算法
Lovász局部引理是概率论中的一个经典结果,常用于通过概率方法证明组合对象的存在性。最简单的形式是,如果我们有$n$“坏事件”,每个事件发生的概率最多为$p$,并且独立于除$d$之外的所有其他事件,那么在$p$和$d$上的某些标准下,所有坏事件都可以以正概率避免。虽然最初的证明是存在的,但对算法Lovász局部引理的研究很多:即设计一种算法,该算法可以找到底层随机变量的分配,从而避免所有不良事件。值得注意的是,Moser和Tardos [JACM '10]的著名结果也暗示了该问题的高效分布式算法,运行$O(\log^2 n)$轮。对于低$d$的例子,Fischer和Ghaffari [DISC '17]将其改进为$O(d^2+\log^{O(1)}\log n)$,这一结果已被证明在分布式复杂性理论中非常重要(Chang和Pettie [SICOMP '19])。我们给出了Lovász局部引理的改进算法,提供了$p$和$d$相关准则的强度和分布轮复杂度之间的权衡。特别是,在与Fischer和Ghaffari算法相同的情况下,我们将轮复杂度提高到$O(\frac{d}{\log d}+\log^{O(1)}\log n)$。在权衡的另一端,我们获得了比以前已知的更广泛的制度的$\log^{O(1)}\log n$轮复杂度。作为我们的主要应用,我们还给出了最大次为$\Delta$的图的$\Delta+o(\Delta)$ -边着色问题的第一个$\log^{O(1)}\log n$ -轮分布算法。与之前的结果相比,这几乎是一个指数级的改进:即使对于$2\Delta-2$边缘着色,也没有已知的先前的$\log^{o(1)} n$ -round算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
6.50
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