New bounds for the Moser-Tardos distribution
David G. Harris

TL;DR
This paper establishes new, stronger bounds on the Moser-Tardos distribution in the Lovasz Local Lemma framework, leading to improved results in combinatorics, SAT problems, and permutation-based structures.
Contribution
It introduces significantly tighter bounds on the MT-distribution, enhancing the understanding of local-to-global property transfer and enabling new combinatorial applications.
Findings
Stronger bounds on the MT-distribution in variable-assignment settings.
Nearly tight bounds on the minimum implicate size of CNF formulas.
Improved bounds on Latin transversals and permutation structures.
Abstract
The Lovasz Local Lemma (LLL) is a probabilistic tool which has been used to show the existence of a variety of combinatorial structures with good "local" properties. The "LLL-distribution" can be used to show that the resulting structures have good global properties in expectation. The simplest, variable-based setting of the LLL was covered by the seminal algorithm of Moser & Tardos (2010). This has since been extended to other probability spaces including random permutations. One can similarly define an "MT-distribution" for these algorithms, that is, the distribution of the configuration they produce. Haeupler et al. (2011) showed bounds on the MT-distribution which essentially match the LLL-distribution for the variable-assignment setting; Harris & Srinivasan showed similar results for the permutation setting. In this work, we show new bounds on the MT-distribution which are…
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