Comparison of Two Convergence Criteria for the Variable-Assignment Lopsided Lovasz Local Lemma

  • David G. Harris


The Lopsided Lovász Local Lemma (LLLL) is a probabilistic tool which is a cornerstone of the probabilistic method of combinatorics, which shows that it is possible to avoid a collection of "bad" events as long as their probabilities and interdependencies are sufficiently small. The strongest possible criterion that can be stated in these terms is due to Shearer (1985), although it is technically difficult to apply to constructions in combinatorics.


The original formulation of the LLLL was non-constructive; a seminal algorithm of Moser & Tardos (2010) gave an efficient constructive algorithm for nearly all applications of it, including applications to $k$-SAT instances with a bounded number of occurrences per variables. Harris (2015) later gave an alternate criterion for this algorithm to converge, which appeared to give stronger bounds than the standard LLL. Unlike the LLL criterion or its variants, this criterion depends in a fundamental way on the decomposition of bad-events into variables.


In this note, we show that the criterion given by Harris can be stronger in some cases even than Shearer's criterion. We construct $k$-SAT formulas with bounded variable occurrence, and show that the criterion of Harris is satisfied while the criterion of Shearer is violated. In fact, there is an exponentially growing gap between the bounds provable from any form of the LLLL and from the bound shown by Harris.

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