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.

翻译：在里特拉图尔,麦肯-弗拉索夫(McKan-Vlasov)的随机差异方程式有近似方法,这些方程式的计算努力量为3美元。在本篇文章中,我们为麦肯-弗拉索夫(McKan-Vlassov)的随机差异方程式引入了全历史循环多级Picard(MLP)近似值。我们证明这些MLP的近似值的计算努力量为2美元+美元,在高维度中基本上是最理想的。