The Lovasz Local Lemma is a seminal result in probabilistic combinatorics. It gives a sufficient condition on a probability space and a collection of events for the existence of an outcome that simultaneously avoids all of those events. Finding such an outcome by an efficient algorithm has been an active research topic for decades. Breakthrough work of Moser and Tardos (2009) presented an efficient algorithm for a general setting primarily characterized by a product structure on the probability space. In this work we present an efficient algorithm for a much more general setting. Our main assumption is that there exist certain functions, called resampling oracles, that can be invoked to address the undesired occurrence of the events. We show that, in all scenarios to which the original Lovasz Local Lemma applies, there exist resampling oracles, although they are not necessarily efficient. Nevertheless, for essentially all known applications of the Lovasz Local Lemma and its generalizations, we have designed efficient resampling oracles. As applications of these techniques, we present new results for packings of Latin transversals, rainbow matchings and rainbow spanning trees.
The Moser-Tardos algorithm is known to terminate under criteria more general than , while still assuming the variable model. Pegden @cite_33 showed that the cluster expansion criterion suffices, whereas Kolipaka and Szegedy @cite_9 showed more generally that Shearer's criterion suffices. We also extend our analysis to the cluster expansion criterion as well as Shearer's criterion, in the more general context of . Our bounds on the number of resampling operations are somewhat weaker than those of @cite_33 @cite_9 , but the increase is at most quadratic.
@cite_9 @cite_33
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Beck's early work [3] gave an efficient version of the Lovasz Local Lemma(LLL) with significant compromise in the parameters. Following several improvements [1,7,4,13], Moser [8], and Moser and Tardos [9] obtained asymptotically optimal results in terms of the maximal degree. For a fixed dependency graph G the exact criterion under which LLL applies is given by Shearer in [12]. For a dependency structure G, let LO(G) be the set of those probability assignments to the nodes of G for which the Lovasz Local Lemma holds. We show that: Both the sequential and parallel ersions of the Moser-Tardos algorithm are efficient up to the Shearer's bound, by giving a tighter analysis. We also prove that, whenever p ∈ LO(G) (1+e), the expected running times of the sequential and parallel versions are at most n e and O(1 e log n e), the later when e A recent theorem of [arXiv:0910.1824v2, 2010], proved using results about the cluster expansion in statistical mechanics, extends the Lovasz local lemma by weakening the conditions under which its conclusion holds. In this note, we prove an algorithmic analogue of this result, extending Moser and Tardos's recent algorithmic local lemma [J. ACM, 57 (2010), 11], and providing an alternative proof of the theorem of applicable in the Moser--Tardos algorithmic framework.