The adaptive EM schemes for McKean-Vlasov SDEs with common noise in finite and infinite horizons
By: Hu Liu, Shuaibin Gao, Junhao Hu
Potential Business Impact:
Makes computer math models more accurate for tricky problems.
This paper is dedicated to investigating the adaptive Euler-Maruyama (EM) schemes for the approximation of McKean-Vlasov stochastic differential equations (SDEs) with common noise. When the drift and diffusion coefficients both satisfy the superlinear growth conditions, the $L^p$ convergence rates in finite and infinite horizons are revealed, which reacts to the particle number and step size. Subsequently, there is an illustration of the theory results by means of two numerical examples.
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