Low-temperature Ising dynamics with random initializations

TL;DR

This paper develops a framework to analyze how random initializations in the low-temperature Ising model can significantly speed up mixing times, especially when starting from a mixture of ground states, and extends results to various graph families.

Contribution

It introduces the concept of weak spatial mixing within a phase and applies it to prove faster mixing times for the Ising model from random initializations across different graphs.

Findings

Mixing time is polynomial in 2D and quasi-polynomial in higher dimensions below critical temperature.

Framework applies to general graphs, including random regular graphs.

Achieves optimal mixing time of O(N log N) on random regular graphs at low temperatures.

Abstract

It is well known that Glauber dynamics on spin systems typically suffer exponential slowdowns at low temperatures. This is due to the emergence of multiple metastable phases in the state space, separated by narrow bottlenecks that are hard for the dynamics to cross. It is a folklore belief that if the dynamics is initialized from an appropriate random mixture of ground states, one for each phase, then convergence to the Gibbs distribution should be much faster. However, such phenomena have largely evaded rigorous analysis, as most tools in the study of Markov chain mixing times are tailored to worst-case initializations. In this paper we develop a general framework towards establishing this conjectured behavior for the Ising model. In the classical setting of the Ising model on an $N$-vertex torus in $\mathbb Z^d$, our framework implies that the mixing time for the Glauber dynamics, initialized from a $\frac 12$-$\frac 12$ mixture of the all-plus and all-minus configurations, is $N^{1+o(1)}$ in dimension $d=2$, and at most quasi-polynomial in all dimensions $d\ge 3$, at all temperatures below the critical one. The key innovation in our analysis is the introduction of the notion of "weak spatial mixing within a phase", a low-temperature adaptation of the classical concept of weak spatial mixing. We show both that this new notion is strong enough to control the mixing time from the above random initialization (by relating it to the mixing time with plus boundary condition at $O(\log N)$ scales), and that it holds at all low temperatures in all dimensions. This framework naturally extends to much more general families of graphs. To illustrate this, we also use the same approach to establish optimal $O(N\log N)$ mixing for the Ising Glauber dynamics on random regular graphs at sufficiently low temperatures, when initialized from the same random mixture.