Lopsidependency in the Moser-Tardos framework: Beyond the Lopsided Lovasz Local Lemma
David G. Harris

TL;DR
This paper introduces a new, stronger criterion for the Moser-Tardos algorithm's convergence in the Lopsided Lovász Local Lemma setting, leading to improved bounds, algorithms, and applications such as for bounded-occurrence k-SAT problems.
Contribution
It presents a novel criterion that surpasses previous LLLL criteria for the Moser-Tardos algorithm, along with new bounds, algorithms, and an improved parallel approach for the LLLL.
Findings
New convergence criterion for Moser-Tardos algorithm surpassing LLLL.
Improved bounds for bounded-occurrence k-SAT satisfiability.
A nearly all-encompassing parallel algorithm for the LLLL.
Abstract
The Lopsided Lov\'{a}sz Local Lemma (LLLL) is a powerful probabilistic principle which has been used in a variety of combinatorial constructions. While originally a general statement about probability spaces, it has recently been transformed into a variety of polynomial-time algorithms. The resampling algorithm of Moser & Tardos (2010) is the most well-known example of this. A variety of criteria have been shown for the LLLL; the strongest possible criterion was shown by Shearer, and other criteria which are easier to use computationally have been shown by Bissacot et al (2011), Pegden (2014), Kolipaka & Szegedy (2011), and Kolipaka, Szegedy, Xu (2012). We show a new criterion for the Moser-Tardos algorithm to converge. This criterion is stronger than the LLLL criterion; this is possible because it does not apply in the same generality as the original LLLL; yet, it is strong enough to…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Bayesian Methods and Mixture Models · Machine Learning and Algorithms
