Geometric Bounds on the Fastest Mixing Markov Chain

TL;DR

This paper establishes geometric bounds on the fastest mixing Markov chain on a graph using vertex conductance, introduces chains with near-uniform distribution to bypass mixing barriers, and discusses extensions to continuous-time and inhomogeneous chains.

Contribution

It provides a Cheeger-type inequality for the fastest mixing Markov chain based on vertex conductance and proposes near-uniform equilibrium distributions to achieve faster mixing.

Findings

Cheeger-type bounds for fastest mixing time using vertex conductance

Construction of chains with near-uniform distribution achieving faster mixing

Extension of results to continuous-time and inhomogeneous Markov chains

Abstract

In the Fastest Mixing Markov Chain problem, we are given a graph $G = (V, E)$ and desire the discrete-time Markov chain with smallest mixing time $\tau$ subject to having equilibrium distribution uniform on $V$ and non-zero transition probabilities only across edges of the graph. It is well-known that the mixing time $\tau_\textsf{RW}$ of the lazy random walk on $G$ is characterised by the edge conductance $\Phi$ of $G$ via Cheeger's inequality: $\Phi^{-1} \lesssim \tau_\textsf{RW} \lesssim \Phi^{-2} \log |V|$. Analogously, we characterise the fastest mixing time $\tau^\star$ via a Cheeger-type inequality but for a different geometric quantity, namely the vertex conductance $\Psi$ of $G$: $\Psi^{-1} \lesssim \tau^\star \lesssim \Psi^{-2} (\log |V|)^2$. This characterisation forbids fast mixing for graphs with small vertex conductance. To bypass this fundamental barrier, we consider Markov chains on $G$ with equilibrium distribution which need not be uniform, but rather only $\varepsilon$-close to uniform in total variation. We show that it is always possible to construct such a chain with mixing time $\tau \lesssim \varepsilon^{-1} (\operatorname{diam} G)^2 \log |V|$. Finally, we discuss analogous questions for continuous-time and time-inhomogeneous chains.