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November 2017 Universality of cutoff for the Ising model
Eyal Lubetzky, Allan Sly
Ann. Probab. 45(6A): 3664-3696 (November 2017). DOI: 10.1214/16-AOP1146

Abstract

On any locally-finite geometry, the stochastic Ising model is known to be contractive when the inverse-temperature $\beta$ is small enough, via classical results of Dobrushin and of Holley in the 1970s. By a general principle proposed by Peres, the dynamics is then expected to exhibit cutoff. However, so far cutoff for the Ising model has been confirmed mainly for lattices, heavily relying on amenability and log Sobolev inequalities. Without these, cutoff was unknown at any fixed $\beta>0$, no matter how small, even in basic examples such as the Ising model on a binary tree or a random regular graph.

We use the new framework of information percolation to show that, in any geometry, there is cutoff for the Ising model at high enough temperatures. Precisely, on any sequence of graphs with maximum degree $d$, the Ising model has cutoff provided that $\beta<\kappa/d$ for some absolute constant $\kappa$ (a result which, up to the value of $\kappa$, is best possible). Moreover, the cutoff location is established as the time at which the sum of squared magnetizations drops to 1, and the cutoff window is $O(1)$, just as when $\beta=0$.

Finally, the mixing time from almost every initial state is not more than a factor of $1+\varepsilon_{\beta}$ faster then the worst one (with $\varepsilon_{\beta}\to0$ as $\beta\to0$), whereas the uniform starting state is at least $2-\varepsilon_{\beta}$ times faster.

Citation

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Eyal Lubetzky. Allan Sly. "Universality of cutoff for the Ising model." Ann. Probab. 45 (6A) 3664 - 3696, November 2017. https://doi.org/10.1214/16-AOP1146

Information

Received: 1 June 2015; Revised: 1 July 2016; Published: November 2017
First available in Project Euclid: 27 November 2017

zbMATH: 06838104
MathSciNet: MR3729612
Digital Object Identifier: 10.1214/16-AOP1146

Subjects:
Primary: 0J27, 60B10, 60K35, 82C20

Rights: Copyright © 2017 Institute of Mathematical Statistics

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Vol.45 • No. 6A • November 2017
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