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arXiv 2401.09747math.NAcs.NA
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非全局Lipschitz条件下随机微分方程随机周期解的随机theta方法

Stochastic theta methods for random periodic solution of stochastic differential equations under non-globally Lipschitz conditions

Ziheng Chen, Liangmin Cao, Lin Chen

AI总结:

该研究在非全局Lipschitz条件下证明了随机微分方程随机周期解及对应随机theta方法数值解的存在唯一性,推导了不同噪声下的均方收敛阶,并通过数值实验验证了理论结果。

摘要:

本工作聚焦于随机微分方程(SDEs)随机周期解的数值逼近。在非全局Lipschitz条件下,我们证明了所考虑方程的随机周期解,以及由θ取值于(1/2,1]的随机theta(ST)方法生成的数值逼近的存在性与唯一性。研究表明,对任意步长,每种ST方法的随机周期解在均方意义下强收敛于SDEs的随机周期解。更确切地说,乘性噪声SDEs的均方收敛阶为1/2,加性噪声SDEs的均方收敛阶为1。最后报告了数值结果以验证这些理论发现。

英文摘要:

This work focuses on the numerical approximations of random periodic solutions of stochastic differential equations (SDEs). Under non-globally Lipschitz conditions, we prove the existence and uniqueness of random periodic solutions for the considered equations and its numerical approximations generated by the stochastic theta (ST) methods with theta within (1/2,1]. It is shown that the random periodic solution of each ST method converges strongly in the mean square sense to that of SDEs for all step size. More precisely, the mean square convergence order is 1/2 for SDEs with multiplicative noise and 1 for SDEs with additive noise. Numerical results are finally reported to confirm these theoretical findings.

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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