Correlation Decay and the Absence of Zeros Property of Partition Functions

TL;DR

This paper links the correlation decay property with the absence of zeros in partition functions, showing that the interpolation method's validity implies a form of strong spatial mixing in certain graph families.

Contribution

It establishes that the interpolation method's applicability implies asymptotic strong spatial mixing, connecting two key techniques in approximating partition functions.

Findings

Interpolation method validity implies asymptotic SSM.

Applies to amenable graphs, including lattice subsets.

Uses graph polynomial representation of partition functions.

Abstract

Absence of (complex) zeros property is at the heart of the interpolation method developed by Barvinok \cite{barvinok2017combinatorics} for designing deterministic approximation algorithms for various graph counting and computing partition functions problems. Earlier methods for solving the same problem include the one based on the correlation decay property. Remarkably, the classes of graphs for which the two methods apply sometimes coincide or nearly coincide. In this paper we show that this is more than just a coincidence. We establish that if the interpolation method is valid for a family of graphs satisfying the self-reducibility property, then this family exhibits a form of correlation decay property which is asymptotic Strong Spatial Mixing (SSM) at distances $\omega(\log n)$, where $n$ is the number of nodes of the graph. This applies in particular to amenable graphs, such as graphs which are finite subsets of lattices. Our proof is based on a certain graph polynomial representation of the associated partition function. This representation is at the heart of the design of the polynomial time algorithms underlying the interpolation method itself. We conjecture that our result holds for all, and not just amenable graphs.