Long time strong convergence analysis of one-step methods for McKean-Vlasov SDEs with superlinear growth coefficients
By: Taiyuan Liu, Yaozhong Hu, Siqing Gan
Potential Business Impact:
Makes computer models of many things more accurate.
This paper presents a strong convergence rate analysis of general discretization approximations for McKean-Vlasov SDEs with super-linear growth coefficients over infinite time horizon. Under some specified non-globally Lipschitz conditions, we derive the propagation of chaos, and the mean-square convergence rate over infinite time horizon for general one-step time discretization schemes for the underlying Mckean-Vlasov SDEs. As an application of the general result it is obtained the mean-square convergence rate over infinite time horizon for two numerical schemes: the projected Euler scheme and the backward Euler scheme for Mckean-Vlasov SDEs in non-globally Lipschitz settings. Numerical experiments are provided to validate the theoretical findings.
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