Aggregation Methods for Computing Steady-States in Statistical Physics
Numerical Analysis
2022-09-23 v1 Statistical Mechanics
Numerical Analysis
Chemical Physics
Abstract
We give a new proof of local convergence of a multigrid method called iterative aggregation/disaggregation (IAD) for computing steady-states of Markov chains. Our proof leads naturally to a precise and interpretable estimate of the asymptotic rate of convergence. We study IAD as a model of more complex methods from statistical physics for computing nonequilibrium steady-states, such as the nonequilibrium umbrella sampling method of Warmflash, et al. We explain why it may be possible to use methods like IAD to efficiently calculate steady-states of models in statistical physics and how to choose parameters to optimize efficiency.
Cite
@article{arxiv.2209.11164,
title = {Aggregation Methods for Computing Steady-States in Statistical Physics},
author = {Gabriel Earle and Brian Van Koten},
journal= {arXiv preprint arXiv:2209.11164},
year = {2022}
}