Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Exact thresholds for Ising–Gibbs samplers on general graphs

Loading...
Thumbnail Image

Date

Authors

Mossel, Elchanan
Sly, Allan

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Mathematical Statistics

Abstract

We establish tight results for rapid mixing of Gibbs samplers for the Ferromagnetic Ising model on general graphs. We show that if (d − 1)tanhβ < 1, then there exists a constant C such that the discrete time mixing time of Gibbs samplers for the ferromagnetic Ising model on any graph of n vertices and maximal degree d, where all interactions are bounded by β, and arbitrary external fields are bounded by Cn log n. Moreover, the spectral gap is uniformly bounded away from 0 for all such graphs, as well as for infinite graphs of maximal degree d. We further show that when d tanhβ < 1, with high probability over the Erdos–Rényi random graph G(n, d/n), it holds that the mixing time of Gibbs samplers is n¹⁺ᶱ(1/log log n). Both results are tight, as it is known that the mixing time for random regular and Erdos–Rényi random graphs is, with high probability, exponential ˝ in n when (d − 1)tanhβ > 1, and d tanhβ > 1, respectively. To our knowledge our results give the first tight sufficient conditions for rapid mixing of spin systems on general graphs. Moreover, our results are the first rigorous results establishing exact thresholds for dynamics on random graphs in terms of spatial thresholds on trees.

Description

Citation

Source

The Annals of Probability

Book Title

Entity type

Access Statement

Open Access

License Rights

Restricted until