Zero-free regions and concentration inequalities for hypergraph colorings in the local lemma regime
Jingcheng Liu, Yixiao Yu
TL;DR
We address zero-free regions for partition functions of CSP-like systems arising from hypergraph q-colorings under local lemma conditions, proving a Lee–Yang zero-free strip around [0,1] for k≥50 and q≥700Δ^{5/(k-10)}. The core method extends the complex-extension Markov chain framework to CSPs via a projection–lifting scheme, combining a projection to color buckets with a percolation/witness-based analysis to control complex marginals and lift bounds to the original measure. Consequences include Berry–Esseen type CLTs and Local CLTs for color-class sizes under uniform colorings, Chebyshev-type concentration, and a deterministic Barvinok-style FPTAS for the partition function in the zero-free region, with parallel zero-freeness results for CNF formulas and Fisher zeros. Altogether, the work provides a unified analytic framework for zero-freeness, central limit behavior, and deterministic counting in the local lemma regime, with potential applicability to a broad class of CSPs.
Abstract
We show that for $q$-colorings in $k$-uniform hypergraphs with maximum degree $Δ$, if $k\ge 50$ and $q\ge 700Δ^{\frac{5}{k-10}}$, there is a "Lee-Yang" zero-free strip around the interval $[0,1]$ of the partition function, which includes the special case of uniform enumeration of hypergraph colorings. As an immediate consequence, we obtain Berry-Esseen type inequalities for hypergraph $q$-colorings under such conditions, demonstrating the asymptotic normality for the size of any color class in a uniformly random coloring. Our framework also extends to the study of "Fisher zeros", leading to deterministic algorithms for approximating the partition function in the zero-free region. Our approach is based on extending the recent work of [Liu, Wang, Yin, Yu, STOC 2025] to general constraint satisfaction problems (CSP). We focus on partition functions defined for CSPs by introducing external fields to the variables. A key component in our approach is a projection-lifting scheme, which enables us to essentially lift information percolation type analysis for Markov chains from the real line to the complex plane. Last but not least, we also show a Chebyshev-type inequality under the sampling LLL condition for atomic CSPs.
