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arXiv 1708.08145math.NAcs.NA
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弱阶一的最优显式稳定积分器:用于刚性和遍历随机微分方程

Optimal explicit stabilized integrator of weak order one for stiff and ergodic stochastic differential equations

Assyr Abdulle, Ibrahim Almuslimani, Gilles Vilmart

AI总结:

本文提出一种新的显式稳定方案,用于刚性和遍历随机微分方程,该方案在无噪声情况下与经典确定性稳定方案一致,并在均方稳定性领域达到最优扩展稳定性域。

摘要:

本文介绍了一种新的显式稳定方案,用于刚性和遍历随机微分方程(SDEs)。在无噪声情况下,新方法与经典确定性稳定方案(或切比雪夫方法)一致,并继承其最优稳定性域大小,该大小随着方法内部阶段数的平方增长。对于均方稳定的刚性随机问题,该方案具有最优扩展均方稳定性域,其增长速率与确定性稳定性域大小相同,优于现有刚性SDEs方法。结合后处理技术,新方法在采样一类遍历SDEs的不变测度时达到二阶收敛率,实现了非马尔可夫方案[1]的稳定版本。

英文摘要:

A new explicit stabilized scheme of weak order one for stiff and ergodic stochastic differential equations (SDEs) is introduced. In the absence of noise, the new method coincides with the classical deterministic stabilized scheme (or Chebyshev method) for diffusion dominated advection-diffusion problems and it inherits its optimal stability domain size that grows quadratically with the number of internal stages of the method. For mean-square stable stiff stochastic problems, the scheme has an optimal extended mean-square stability domain that grows at the same quadratic rate as the deterministic stability domain size in contrast to known existing methods for stiff SDEs [A. Abdulle and T. Li. Commun. Math. Sci., 6(4), 2008, A. Abdulle, G. Vilmart, and K. C. Zygalakis, SIAM J. Sci. Comput., 35(4), 2013]. Combined with postprocessing techniques, the new methods achieve a convergence rate of order two for sampling the invariant measure of a class of ergodic SDEs, achieving a stabilized version of the non-Markovian scheme introduced in [B. Leimkuhler, C. Matthews, and M. V. Tretyakov, Proc. R. Soc. A, 470, 2014].

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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