@Article{JCM-43-840,
author = {Yu , YanyanXiao , Aiguo and Tang , Xiao},
title = {Tamed Stochastic Runge-Kutta-Chebyshev Methods for Stochastic Differential Equations with Non-Globally Lipschitz Coefficients},
journal = {Journal of Computational Mathematics},
year = {2025},
volume = {43},
number = {4},
pages = {840--865},
abstract = {<p style="text-align: justify;">In this paper, we introduce a new class of explicit numerical methods called the tamed stochastic Runge-Kutta-Chebyshev (t-SRKC) methods, which apply the idea of taming to the stochastic Runge-Kutta-Chebyshev (SRKC) methods. The key advantage of our explicit methods is that they can be suitable for stochastic differential equations with non-globally Lipschitz coefficients and stiffness. Under certain non-globally Lipschitz conditions, we study the strong convergence of our methods and prove that the order of strong convergence is 1/2. To show the advantages of our methods, we compare them with some existing explicit methods (including the Euler-Maruyama method, balanced Euler-Maruyama method and two types of SRKC methods) through several numerical examples. The numerical results show that our t-SRKC methods are efficient, especially for stiff stochastic differential equations.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.2402-m2023-0194},
url = {http://global-sci.org/intro/article_detail/jcm/24263.html}
}