Comparison of two convergence criteria for the variable-assignment Lopsided Lovasz Local Lemma

TL;DR

This paper compares two convergence criteria for the variable-assignment Lopsided Lovasz Local Lemma, demonstrating that Harris's criterion can be stronger than Shearer's in certain cases, especially for bounded-variable $k$-SAT formulas.

Contribution

It shows that Harris's criterion can outperform Shearer's criterion in some instances, providing explicit $k$-SAT examples where Harris's condition holds but Shearer's does not.

Findings

Harris's criterion can be stronger than Shearer's in some cases.

Constructed $k$-SAT formulas where Harris's criterion is satisfied but Shearer's is violated.

Exponential gap between bounds from Harris's criterion and Shearer's criterion.

Abstract

The Lopsided Lovasz Local Lemma (LLLL) is a cornerstone probabilistic tool for showing 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 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 algorithm for nearly all its applications, including to $k$-SAT instances where each variable appears in a bounded number of clauses. Harris (2015) later gave an alternate criterion for this algorithm to converge; 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.