In the literatur there exist approximation methods for McKean-Vlasov stochastic differential equations which have a computational effort of order 3. In this article we introduce full-history recursive multilevel Picard (MLP) approximations for McKean-Vlasov stochastic differential equations. We prove that these MLP approximations have computational effort of order 2+ which is essentially optimal in high dimensions.
@article{arxiv.2103.10870,
title = {Multilevel Picard approximations for McKean-Vlasov stochastic differential equations},
author = {Martin Hutzenthaler and Thomas Kruse and Tuan Anh Nguyen},
journal= {arXiv preprint arXiv:2103.10870},
year = {2022}
}