Commutativity in the Algorithmic Lovasz Local Lemma

TL;DR

This paper introduces a new commutativity condition for the Algorithmic Lovász Local Lemma, enabling arbitrary flaw selection rules and efficient parallelization, with applications to matchings and permutations.

Contribution

It formulates a novel commutativity condition that broadens the applicability of the Algorithmic Lovász Local Lemma and supports arbitrary flaw selection rules.

Findings

The commutativity condition guarantees correctness for arbitrary flaw selection.

Existing resampling oracles for matchings and permutations satisfy the condition.

Parallelization is feasible under an additional assumption.

Abstract

We consider the recent formulation of the Algorithmic Lov\'asz Local Lemma [10,2,3] for finding objects that avoid `bad features', or `flaws'. It extends the Moser-Tardos resampling algorithm [17] to more general discrete spaces. At each step the method picks a flaw present in the current state and goes to a new state according to some prespecified probability distribution (which depends on the current state and the selected flaw). However, it is less flexible than the Moser-Tardos method since [10,2,3] require a specific flaw selection rule, whereas [17] allows an arbitrary rule (and thus can potentially be implemented more efficiently). We formulate a new "commutativity" condition, and prove that it is sufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additional assumption. We then show that existing resampling oracles for perfect matchings and permutations do satisfy this condition.