Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion
For researchers solving high-dimensional McKean-Vlasov SDEs, this provides a provably efficient numerical method that overcomes the curse of dimensionality.
The paper introduces multilevel Picard approximations for McKean-Vlasov SDEs with nonconstant diffusion, proving that the method avoids the curse of dimensionality with polynomial cost in dimension and error tolerance, and demonstrates applicability up to dimension 1000.
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 $L^2$-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.