Deterministic parallel algorithms for fooling polylogarithmic juntas and the Lovasz Local Lemma

TL;DR

This paper develops deterministic parallel algorithms that efficiently fool polylogarithmic juntas and extend the Lovász Local Lemma to larger neighborhoods, improving processor complexity and enabling applications like defective vertex coloring.

Contribution

It introduces new derandomization techniques, including variable partitioning and Fourier code constructions, reducing complexity and extending the Lovász Local Lemma to polylogarithmic neighborhoods.

Findings

Processor complexity is nearly independent of junta width w.

New NC1 algorithm for fooling neighborhoods of size up to polylogarithmic in n.

Extended Lovász Local Lemma application to larger neighborhoods for graph coloring.

Abstract

Many randomized algorithms can be derandomized efficiently using either the method of conditional expectations or probability spaces with low (almost-) independence. A series of papers, beginning with Luby (1993) and continuing with Berger & Rompel (1991) and Chari et al. (2000), showed that these techniques can be combined to give deterministic parallel algorithms for combinatorial optimization problems involving sums of $w$-juntas. We improve these algorithms through derandomized variable partitioning and a new code construction for fooling Fourier characters over $GF(2)$. This reduces the processor complexity to essentially independent of $w$ while the running time is reduced from exponential in $w$ to linear in $w$. As a key subroutine, we give a new algorithm to generate a probability space which can fool a given set of neighborhoods. Schulman (1992) gave an NC algorithm to do so for neighborhoods of size $w \leq O(\log n)$. Our new algorithm is NC1, with essentially optimal time and processor complexity, when $w = O(\log n)$; it remains NC up to $w = \text{polylog}(n)$. This answers an open problem of Schulman. One major application of these algorithms is an NC algorithm for the Lov\'{a}sz Local Lemma. Previous NC algorithms, including the seminal algorithm of Moser & Tardos (2010) and the work of Chandrasekaran et. al (2013), required that (essentially) the bad-events could span only $O(\log n)$ variables; we relax this to $\text{polylog}(n)$ variables. We use this for an $\text{NC}^2$ algorithm for defective vertex coloring, which works for arbitrary degree graphs.