# Metropolis Monte Carlo sampling: convergence, localization transition and optimality

arXiv: https://arxiv.org/abs/2207.10488 · HTML: https://mlsanity.com/paper/2207.10488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 55 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [Equivariant flow-based sampling for lattice gauge theory](https://mlsanity.com/paper/2003.06413/) (2020)

## Abstract (arXiv)

Among random sampling methods, Markov Chain Monte Carlo algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties towards the steady state, within a random walk Metropolis scheme. Analysing the relaxation properties of some model algorithms sufficiently simple to enable analytic progress, we show that the deviations from the target steady-state distribution can feature a localization transition as a function of the characteristic length of the attempted jumps defining the random walk. While the iteration of the Monte Carlo algorithm converges to equilibrium for all choices of jump parameters, the localization transition changes drastically the asymptotic shape of the difference between the probability distribution reached after a finite number of steps of the algorithm and the target equilibrium distribution. We argue that the relaxation before and after the localisation transition is respectively limited by diffusion and rejection rates.