Efficient subgraph-based sampling of Ising-type models with frustration

TL;DR

This paper introduces a subgraph-based sampling method for Ising-type models, especially frustrated systems like spin glasses, demonstrating improved efficiency over traditional methods in certain cases.

Contribution

The paper presents a novel subgraph-based sampling technique that handles frustrated models more efficiently than existing single-site update methods.

Findings

More efficient sampling of frustrated models demonstrated

Effective for models with fixed treewidth subgraphs

Potential improvements over traditional methods in spin glasses

Abstract

Here is proposed a general subgraph-based method for efficiently sampling certain graphical models, typically using subgraphs of a fixed treewidth, and also a related method for finding minimum energy (ground) states. In the case of models with frustration, such as the spin glass, evidence is presented that this method can be more efficient than traditional single-site update methods.