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Main Authors: Li, Xiang, Mo, Yingjun, Yang, Haoran
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2505.03104
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author Li, Xiang
Mo, Yingjun
Yang, Haoran
author_facet Li, Xiang
Mo, Yingjun
Yang, Haoran
contents Consider the following stochastic differential equation driven by multiplicative noise on $\mathbb{R}^d$ with a superlinearly growing drift coefficient, \begin{align*} \mathrm{d} X_t = b (X_t) \, \mathrm{d} t + σ(X_t) \, \mathrm{d} B_t. \end{align*} It is known that the corresponding explicit Euler schemes may not converge. In this article, we analyze an explicit and easily implementable numerical method for approximating such a stochastic differential equation, i.e. its tamed Euler-Maruyama approximation. Under partial dissipation conditions ensuring the ergodicity, we obtain the uniform-in-time convergence rates of the tamed Euler-Maruyama process under $L^{1}$-Wasserstein distance and total variation distance.
format Preprint
id arxiv_https___arxiv_org_abs_2505_03104
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Tamed Euler-Maruyama method for SDEs with non-globally Lipschitz drift and multiplicative noise
Li, Xiang
Mo, Yingjun
Yang, Haoran
Probability
Consider the following stochastic differential equation driven by multiplicative noise on $\mathbb{R}^d$ with a superlinearly growing drift coefficient, \begin{align*} \mathrm{d} X_t = b (X_t) \, \mathrm{d} t + σ(X_t) \, \mathrm{d} B_t. \end{align*} It is known that the corresponding explicit Euler schemes may not converge. In this article, we analyze an explicit and easily implementable numerical method for approximating such a stochastic differential equation, i.e. its tamed Euler-Maruyama approximation. Under partial dissipation conditions ensuring the ergodicity, we obtain the uniform-in-time convergence rates of the tamed Euler-Maruyama process under $L^{1}$-Wasserstein distance and total variation distance.
title Tamed Euler-Maruyama method for SDEs with non-globally Lipschitz drift and multiplicative noise
topic Probability
url https://arxiv.org/abs/2505.03104