A very fast inference algorithm for finite-dimensional spin glasses: Belief Propagation on the dual lattice

TL;DR

This paper introduces a novel, fast message passing algorithm called the Dual algorithm for estimating marginal probabilities in finite-dimensional spin glasses, demonstrating high accuracy and efficiency compared to existing methods.

Contribution

The paper presents a new belief propagation-based algorithm inspired by the Cluster Variational Method, offering a faster and accurate alternative for spin glass inference in finite-dimensional lattices.

Findings

Performs well across a wide temperature range

Matches Monte Carlo and exact ground state results

Is approximately 100 times faster than existing provably convergent algorithms

Abstract

Starting from a Cluster Variational Method, and inspired by the correctness of the paramagnetic Ansatz (at high temperatures in general, and at any temperature in the 2D Edwards-Anderson model) we propose a novel message passing algorithm --- the Dual algorithm --- to estimate the marginal probabilities of spin glasses on finite dimensional lattices. We show that in a wide range of temperatures our algorithm compares very well with Monte Carlo simulations, with the Double Loop algorithm and with exact calculation of the ground state of 2D systems with bimodal and Gaussian interactions. Moreover it is usually 100 times faster than other provably convergent methods, as the Double Loop algorithm.