Jose Blanchet, Xinyun Chen, Nian Si, Peter W. Glynn (2021) Efficient Steady-State Simulation of High-Dimensional Stochastic Networks. Stochastic Systems 11(2):174-192. https://doi.org/10.1287/stsy.2021.0077
Abstract
We propose and study an asymptotically optimal Monte Carlo estimator for steady-state expectations of a d-dimensional reflected Brownian motion (RBM). Our estimator is asymptotically optimal in the sense that it requires (up to logarithmic factors in d) independent and identically distributed scalar Gaussian random variables in order to output an estimate with a controlled error. Our construction is based on the analysis of a suitable multilevel Monte Carlo strategy which, we believe, can be applied widely. This is the first algorithm with linear complexity (under suitable regularity conditions) for a steady-state estimation of RBM as the dimension increases.
Authors
Jose Blanchet, Xinyun Chen, Nian Si, Peter W Glynn
Publication date
2021/6
Journal
Stochastic Systems
Volume
11
Issue
2
Pages
174-192
Publisher
INFORMS
