Mean square stability and dissipativity of two classes of theta methods for systems of stochastic delay differential equations

Mean square stability and dissipativity of two classes of theta methods for systems of stochastic delay differential equations
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随机时滞微分方程组两类theta方法的均方稳定性和耗散性

DOI:
10.1016/j.cam.2013.03.038
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发表时间:
2014-03
期刊:
J. Comput. Appl. Math.
影响因子:
--
通讯作者:
C. Huang
C. Huang
中科院分区:
其他
文献类型:
--
作者:
C. Huang

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本文首先研究了随机延迟微分方程数值方法在漂移系数和扩散系数耦合条件下的均方稳定性。这个条件允许扩散系数可以是高度非线性的,即它不一定满足线性增长或全局Lipschitz条件。证明了对于所有正步长,当θ≥ 0.5时,经典随机theta方法是渐近均方稳定的,当θ> 0.5时,分步theta方法是指数均方稳定的.在更强的假设条件下,得到了θ< 0.5的条件稳定性结果.最后,我们进一步研究了当θ> 0.5时分步θ方法的均方耗散性,证明了该方法具有一个与初始值无关的均方有界吸收集.
In this paper, we first study the mean square stability of numerical methods for stochastic delay differential equations under a coupled condition on the drift and diffusion coefficients. This condition admits that the diffusion coefficient can be highly nonlinear, ie, it does not necessarily satisfy a linear growth or global Lipschitz condition. It is proved that, for all positive stepsizes, the classical stochastic theta method with θ≥ 0.5 is asymptotically mean square stable and the split-step theta method with θ> 0.5 is exponentially mean square stable. Conditional stability results for the methods with θ< 0.5 are also obtained under a stronger assumption. Finally, we further investigate the mean square dissipativity of the split-step theta method with θ> 0.5 and prove that the method possesses a bounded absorbing set in mean square independent of initial data.
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