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OP-8747Status unassessedProbability

Metropolis on [0,1]2[0,1]^2

Markov chain mixing times · Historical workshop question

Editorial mapping of AIMPL source category: Probability. http://aimpl.org/markovmixing/ Not an author-supplied MSC code.

Historical list · status unassessedAs of 2026-09-08Source & evidence ↓

Problem statement

Obtain a good bound on the mixing time of the Metropolis algorithm for this stationary distribution in terms of AA. Generalize this to [0,1]d[0,1]^d.

Lean 4

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Background

Workshop introduction

In general, we will use the following notation in the problem list. We consider aperiodic irreducible Markov chains on a state space, Ω\Omega with unique invariant (stationary) distribution π\pi. In the discrete time setting we allow the matrix (P)x,y∈Ω(P)_{x,y\in\Omega} with Pxy=P(x,y)P_{xy}=P(x,y) to denote the transition matrix of the Markov chain.

A chain is reversible if π(x)P(x,y)=π(y)P(y,x)\pi(x)P(x,y)=\pi(y)P(y,x) for all x,y∈Ωx,y\in\Omega. Q(x,y)=π(x)P(x,y)Q(x,y)=\pi(x)P(x,y) is the edge measure on (x,y)(x,y). We denote by t\mboxmixt_{\mbox{mix}} the total variation mixing time defined as

t\mboxmix(ϵ)=inf⁡{t:sup⁡ω0∈Ω∥Pt(ω0,⋅)−π(⋅)∥TV≤ϵ} ,t_{\mbox{mix}}(\epsilon)=\inf\{t:\sup_{\omega_0\in\Omega}\|P^t(\omega_0,\cdot)-\pi(\cdot)\|_{TV}\leq \epsilon\}\,,

and set t\mboxmix=t\mboxmix(ϵ)t_{\mbox{mix}}=t_{\mbox{mix}}(\epsilon), where the total variation distance

∥μ−ν∥TV=12∥μ−ν∥ℓ1 .\|\mu-\nu\|_{TV}=\tfrac 12 \|\mu-\nu\|_{\ell^1}\,.

Question context

Consider the stationary distribution on [0,1]2[0,1]^2 given by

πA(p1,p2)=Z−1exp⁡(−A∣p1−p2∣2)\pi_A(p_1,p_2)=\mathcal Z^{-1} \exp(-A|p_1-p_2|^2)

parametrized by A∈[10,1000]A\in [10,1000].

Progress & source assessment

Historical workshop record. Current open/solved status has not been independently reassessed.

Status note in the source

The current best known upper bound is exp⁡(A2)\exp(A^2).

References
  1. [1]Markov chain mixing times link

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