Strong convergence in the infinite horizon of numerical methods for stochastic delay differential equations

Fuente: arXiv
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Autores principales: Wang, Yudong, Tian, Hongjiong
Formato: Preprint
Publicado: 2025
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author Wang, Yudong
Tian, Hongjiong
author_facet Wang, Yudong
Tian, Hongjiong
contents In this work, we present a general technique for establishing the strong convergence of numerical methods for stochastic delay differential equations (SDDEs) in the infinite horizon. This technique can also be extended to analyze certain continuous function-valued segment processes associated with the numerical methods, facilitating the numerical approximation of invariant measures of SDDEs. To illustrate the application of these results, we specifically investigate the backward and truncated Euler-Maruyama methods. Several numerical experiments are provided to demonstrate the theoretical results.
format Preprint
id arxiv_https___arxiv_org_abs_2505_14262
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Strong convergence in the infinite horizon of numerical methods for stochastic delay differential equations
Wang, Yudong
Tian, Hongjiong
Numerical Analysis
60H35, 65C30, 65L20,
G.1.7; G.3
In this work, we present a general technique for establishing the strong convergence of numerical methods for stochastic delay differential equations (SDDEs) in the infinite horizon. This technique can also be extended to analyze certain continuous function-valued segment processes associated with the numerical methods, facilitating the numerical approximation of invariant measures of SDDEs. To illustrate the application of these results, we specifically investigate the backward and truncated Euler-Maruyama methods. Several numerical experiments are provided to demonstrate the theoretical results.
title Strong convergence in the infinite horizon of numerical methods for stochastic delay differential equations
topic Numerical Analysis
60H35, 65C30, 65L20,
G.1.7; G.3
url https://arxiv.org/abs/2505.14262