Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion
Numerical Analysis
2025-11-25 v2 Numerical Analysis
Probability
Abstract
We introduce multilevel Picard (MLP) approximations for McKean--Vlasov stochastic differential equations (SDEs) with nonconstant diffusion coefficient. Under standard Lipschitz assumptions on the coefficients, we show that the MLP algorithm approximates the solution of the SDE in the -sense without the curse of dimensionality. The latter means that its computational cost grows at most polynomially in both the dimension and the reciprocal of the prescribed error tolerance. In two numerical experiments, we demonstrate its applicability by approximating McKean--Vlasov SDEs in dimensions up to 1000.
Cite
@article{arxiv.2502.03205,
title = {Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion},
author = {Ariel Neufeld and Tuan Anh Nguyen and Philipp Schmocker},
journal= {arXiv preprint arXiv:2502.03205},
year = {2025}
}