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arXiv 1608.07096math.NAcs.NA
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具有乘性噪声的随机微分方程的新一类指数积分器

A New Class of Exponential Integrators for Stochastic Differential Equations With Multiplicative Noise

Uşak University(乌沙克大学);Heriot Watt University(赫瑞瓦特大学)

Utku Erdoğan, Gabriel J. Lord

AI总结:

本文提出了一种利用广义几何布朗运动精确解的新类型指数积分器,证明了其强收敛性,并在线性噪声情况下改进了欧拉方法的收敛速率,展示了该方法在非线性噪声下的有效性。

摘要:

本文介绍了用于随机微分方程的新类型的指数积分器,利用广义几何布朗运动的精确解。我们检查了欧拉和米尔斯坦版本的方案,并证明了其强收敛性。对于线性噪声的特殊情况,我们获得了欧拉版本比标准积分方法更高的收敛速率。我们研究了这些方法与其他指数积分器的效率,并证明通过引入合适的同伦参数,这些方案不仅在线性噪声下有效,在非线性噪声存在时也具有竞争力。

英文摘要:

In this paper, we present new types of exponential integrators for Stochastic Differential Equations (SDEs) that take the advantage of the exact solution of (generalised) geometric Brownian motion. We examine both Euler and Milstein versions of the scheme and prove strong convergence. For the special case of linear noise we obtain an improved rate of convergence for the Euler version over standard integration methods. We investigate the efficiency of the methods compared with other exponential integrators and show that by introducing a suitable homotopy parameter these schemes are competitive not only when the noise is linear but also in the presence of nonlinear noise terms.

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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