Sequential cavity method for computing free energy and surface pressure
David Gamarnik, Dmitriy Katz

TL;DR
This paper introduces a new deterministic method for accurately computing free energy and surface pressure in lattice statistical mechanics models, leveraging correlation decay and marginal probability algorithms, with improved efficiency and estimates.
Contribution
The paper presents a novel approach based on marginal probabilities and Strong Spatial Mixing to efficiently estimate free energy and surface pressure, improving previous bounds and computational complexity.
Findings
Improved estimate for monomer-dimer exponent in 3D lattice.
Method achieves polynomial effort in inverse error, unlike prior exponential methods.
Successfully applied to hard-core and monomer-dimer models.
Abstract
We propose a new method for the problems of computing free energy and surface pressure for various statistical mechanics models on a lattice . Our method is based on representing the free energy and surface pressure in terms of certain marginal probabilities in a suitably modified sublattice of . Then recent deterministic algorithms for computing marginal probabilities are used to obtain numerical estimates of the quantities of interest. The method works under the assumption of Strong Spatial Mixing (SSP), which is a form of a correlation decay. We illustrate our method for the hard-core and monomer-dimer models, and improve several earlier estimates. For example we show that the exponent of the monomer-dimer coverings of belongs to the interval , improving best previously known estimate of (approximately) obtained in…
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