Efficient Steady-state Simulation of High-dimensional Stochastic Networks
Probability
2020-01-29 v2
Abstract
We propose and study an asymptotically optimal Monte Carlo estimator for steady-state expectations of a d-dimensional reflected Brownian motion. Our estimator is asymptotically optimal in the sense that it requires (up to logarithmic factors in ) i.i.d. Gaussian random variables in order to output an estimate with a controlled error. Our construction is based on the analysis of a suitable multi-level Monte Carlo strategy which, we believe, can be applied widely. This is the first algorithm with linear complexity (under suitable regularity conditions) for steady-state estimation of RBM as the dimension increases.
Cite
@article{arxiv.2001.08384,
title = {Efficient Steady-state Simulation of High-dimensional Stochastic Networks},
author = {Jose Blanchet and Xinyun Chen and Peter Glynn and Nian Si},
journal= {arXiv preprint arXiv:2001.08384},
year = {2020}
}