@Article{JCM-33-6,
author = {Yuanling, Niu and Zhang, Chengjian and Kevin, Burrage},
title = {Strong Predictor-Corrector Approximation for Stochastic Delay Differential Equations},
journal = {Journal of Computational Mathematics},
year = {2015},
volume = {33},
number = {6},
pages = {587--605},
abstract = {<p style="text-align: justify;">This paper presents a strong predictor-corrector method for the numerical solution of stochastic delay differential equations (SDDEs) of Itô-type. The method is proved to be mean-square convergent of order min{$1/2, \hat{p}$} under the Lipschitz condition and the linear growth condition, where $\hat{p}$ is the exponent of Hölder condition of the initial function. Stability criteria for this type of method are derived. It is shown that for certain choices of the flexible parameter $p$ the derived method can have a better stability property than more commonly used numerical methods. That is, for some $p$, the asymptotic MS-stability bound of the method will be much larger than that of the Euler-Maruyama method. Numerical results are reported confirming convergence properties and comparing stability properties of methods with different parameters $p$. Finally, the vectorised simulation is discussed and it is shown that this implementation is much more efficient.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1507-m4505},
url = {https://global-sci.com/article/84616/strong-predictor-corrector-approximation-for-stochastic-delay-differential-equations}
}