Spatial mixing and the connective constant: Optimal bounds
Alistair Sinclair, Piyush Srivastava, Daniel \v{S}tefankovi\v{c},, Yitong Yin

TL;DR
This paper establishes optimal bounds on decay of correlations for the monomer-dimer and hard core models in graphs with bounded connective constant, enabling efficient approximation algorithms for counting matchings and independent sets.
Contribution
It introduces a new message-based framework to prove decay of correlations in graphs with bounded connective constant, leading to the first optimal bounds and FPTASs for these models.
Findings
Proved best possible decay of correlation rates in graphs with bounded connective constant.
Developed a new message approach applicable to unbounded degree graphs.
Improved bounds for decay of correlations on regular lattices.
Abstract
We study the problem of deterministic approximate counting of matchings and independent sets in graphs of bounded connective constant. More generally, we consider the problem of evaluating the partition functions of the monomer-dimer model (which is defined as a weighted sum over all matchings where each matching is given a weight in terms of a fixed parameter gamma called the monomer activity) and the hard core model (which is defined as a weighted sum over all independent sets where an independent set I is given a weight in terms of a fixed parameter lambda called the vertex activity). The connective constant is a natural measure of the average degree of a graph which has been studied extensively in combinatorics and mathematical physics, and can be bounded by a constant even for certain unbounded degree graphs such as those sampled from the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Stochastic processes and statistical mechanics · Topological and Geometric Data Analysis
