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Log-concave sampling: Metropolis-Hastings algorithms are fast

Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright, Bin Yu; 20(183):1−42, 2019.

Abstract

We study the problem of sampling from a strongly log-concave density supported on $\mathbb{R}^d$, and prove a non-asymptotic upper bound on the mixing time of the Metropolis-adjusted Langevin algorithm (MALA). The method draws samples by simulating a Markov chain obtained from the discretization of an appropriate Langevin diffusion, combined with an accept-reject step. Relative to known guarantees for the unadjusted Langevin algorithm (ULA), our bounds show that the use of an accept-reject step in MALA leads to an exponentially improved dependence on the error-tolerance. Concretely, in order to obtain samples with TV error at most $\delta$ for a density with condition number $\kappa$, we show that MALA requires $\mathcal{O} (\kappa d \log(1/\delta) )$ steps from a warm start, as compared to the $\mathcal{O} (\kappa^2 d/\delta^2 )$ steps established in past work on ULA. We also demonstrate the gains of a modified version of MALA over ULA for weakly log-concave densities. Furthermore, we derive mixing time bounds for the Metropolized random walk (MRW) and obtain $\mathcal{O}(\kappa)$ mixing time slower than MALA. We provide numerical examples that support our theoretical findings, and demonstrate the benefits of Metropolis-Hastings adjustment for Langevin-type sampling algorithms.

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