Multilevel Picard approximations for McKean-Vlasov stochastic differential equations
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.
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