Geometric Ergodicity and Strong Error Estimates for Tamed Schemes of Super-linear SODEs

BIT Numerical Mathematics 2026 Open access

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Authors: Zhihui Liu, Xiao-Ming Wu
DOI: 10.1007/s10543-026-01147-3
Cited by: 0
References: 19

Abstract

Abstract We construct a family of explicit tamed Euler–Maruyama (TEM) schemes, which can preserve the same Lyapunov function for super-linear stochastic ordinary differential equations (SODEs) driven by multiplicative noise. These TEM schemes are shown to inherit the geometric ergodicity of the considered SODEs and converge with optimal strong convergence orders. Numerical experiments verify our theoretical results.

Fields of Study & Subjects

Numerical methods for differential equations Advanced Numerical Analysis Techniques Advanced Numerical Methods in Computational Mathematics

Publication details

Journal / source
BIT Numerical Mathematics
Publisher
Springer Science and Business Media LLC
Volume
66
Issue
3
Publication date
27 July 2026
Language
EN