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arXiv 1107.0571math.NAcs.NA
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随机延迟微分方程的改进分步向后欧拉方法

The improved split-step backward Euler method for stochastic differential delay equations

Central South University(中南大学)

Xiaojie Wang, Siqing Gan

AI总结:

针对具有一般可变延迟的随机延迟微分方程,提出并分析了一种改进的分步向后欧拉方法,在扩散系数全局Lipschitz且漂移系数满足单边Lipschitz条件下证明了均方收敛性,并研究了指数均方稳定性,数值实验表明该方法具有无条件稳定性和计算效率。

摘要:

针对具有一般可变延迟的随机延迟微分方程,引入并分析了一种新的改进分步向后欧拉方法。在扩散系数$g(x,y)$关于$x$和$y$全局Lipschitz,但漂移系数$f(x,y)$关于$x$满足单边Lipschitz条件且关于$y$全局Lipschitz的条件下(假设3.1),证明了该方法在均方意义下的收敛性。进一步,对于具有负单边Lipschitz常数的随机延迟微分方程,研究了该方法的指数均方稳定性。我们的结果表明,该方法具有无条件稳定性,即无需对步长$h$施加任何限制即可很好地再现底层系统的稳定性。数值实验及与现有随机延迟微分方程方法的比较说明了我们方法的计算效率。

英文摘要:

A new, improved split-step backward Euler (SSBE) method is introduced and analyzed for stochastic differential delay equations(SDDEs) with generic variable delay. The method is proved to be convergent in mean-square sense under conditions (Assumption 3.1) that the diffusion coefficient $g(x,y)$ is globally Lipschitz in both $x$ and $y$, but the drift coefficient $f(x,y)$ satisfies one-sided Lipschitz condition in $x$ and globally Lipschitz in $y$. Further, exponential mean-square stability of the proposed method is investigated for SDDEs that have a negative one-sided Lipschitz constant. Our results show that the method has the unconditional stability property in the sense that it can well reproduce stability of underlying system, without any restrictions on stepsize $h$. Numerical experiments and comparisons with existing methods for SDDEs illustrate the computational efficiency of our method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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