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Ergodic Estimates of One-Step Numerical Approximations for Superlinear SODEs

2025/10/24 by Liu, Xin, Liu, Zhihui
#37M25 #60H35 #65C30 #FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.2510.21279

Abstract

This paper establishes the first-order convergence rate for the ergodic error of numerical approximations to a class of stochastic ODEs (SODEs) with superlinear coefficients and multiplicative noise. By leveraging the generator approach to the Stein method, we derive a general error representation formula for one-step numerical schemes. Under suitable dissipativity and smoothness conditions, we prove that the error between the accurate invariant measure π and the numerical invariant measure πτ is of order \mathscrO(τ), which is sharp. Our framework applies to several recently studied schemes, including the tamed Euler, projected Euler, and backward Euler methods.

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