Harnessing the Bethe free energy

TL;DR

This paper provides rigorous conditions under which the replica symmetric cavity method accurately computes the partition function for sparse random graph models in combinatorics, physics, and computer science.

Contribution

It establishes sufficient conditions for the success of the cavity method, supported by a novel regularity lemma for probability measures on large product spaces.

Findings

Proves the correctness of the cavity method under certain conditions.

Introduces a new regularity lemma for probability measures on product spaces.

Bridges physics predictions with rigorous mathematical proofs.

Abstract

A wide class of problems in combinatorics, computer science and physics can be described along the following lines. There are a large number of variables ranging over a finite domain that interact through constraints that each bind a few variables and either encourage or discourage certain value combinations. Examples include the $k$-SAT problem or the Ising model. Such models naturally induce a Gibbs measure on the set of assignments, which is characterised by its partition function. The present paper deals with the partition function of problems where the interactions between variables and constraints are induced by a sparse random (hyper)graph. According to physics predictions, a generic recipe called the "replica symmetric cavity method" yields the correct value of the partition function if the underlying model enjoys certain properties [Krzkala et al., PNAS 2007]. Guided by this conjecture, we prove general sufficient conditions for the success of the cavity method. The proofs are based on a "regularity lemma" for probability measures on sets of the form $\Omega^n$ for a finite $\Omega$ and a large $n$ that may be of independent interest.