Improved Inapproximability Results for Counting Independent Sets in the Hard-Core Model

Abstract

We study the computational complexity of approximately counting the number of independent sets of a graph with maximum degree Δ. More generally, for an input graph G = (V,E) and an activity λ > 0, we are interested in the quantity Z G (λ) defined as the sum over independent sets I weighted as w(I) = λ |I|. In statistical physics, Z G (λ) is the partition function for the hard-core model, which is an idealized model of a gas where the particles have non-negibile size. Recently, an interesting phase transition was shown to occur for the complexity of approximating the partition function. Weitz showed an FPAS for the partition function for any graph of maximum degree Δ when Δ is constant and \(\lambda<\lambda_c(\mathbb{T}_\Delta):=(\Delta-1)^{\Delta-1}/(\Delta-2)^\Delta\). The quantity \(\lambda_c(\mathbb{T}_\Delta)\) is the critical point for the so-called uniqueness threshold on the infinite, regular tree of degree Δ. On the other side, Sly proved that there does not exist efficient (randomized) approximation algorithms for \(\lambda_c(\mathbb{T}_\Delta)<\lambda<\lambda_c(\mathbb{T}_\Delta)+\varepsilon(\Delta)\), unless NP=RP, for some function ε(Δ) > 0. We remove the upper bound in the assumptions of Sly’s result for Δ ≠ 4,5, that is, we show that there does not exist efficient randomized approximation algorithms for all \(\lambda>\lambda_c(\mathbb{T}_\Delta)\) for Δ = 3 and Δ ≥ 6. Sly’s inapproximability result uses a clever reduction, combined with a second-moment analysis of Mossel, Weitz and Wormald which prove torpid mixing of the Glauber dynamics for sampling from the associated Gibbs distribution on almost every regular graph of degree Δ for the same range of λ as in Sly’s result. We extend Sly’s result by improving upon the technical work of Mossel et al., via a more detailed analysis of independent sets in random regular graphs.KeywordsPartition FunctionInput GraphGibbs DistributionUnique MaximumGlauber DynamicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.