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arXiv 2403.17596math.PR
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有界且可测测试函数下随机微分方程的高阶弱近似

High order weak approximation of Stochastic Differential Equations for bounded and measurable test functions

Clément Rey

AI总结:

提出一种针对有界可测测试函数的随机微分方程高阶弱近似方法,通过在随机时间网格上评估数值格式并利用正则化性质,实现任意速率的收敛。

摘要:

我们提出了一种以任意速率近似随机微分方程(SDEs)解的方法。该近似是针对有界且可测的测试函数推导的。具体而言,我们证明,利用数值格式对光滑测试函数的标准弱近似性质(如Euler格式的一阶弱收敛),通过为SDE的半群构建量身定制的近似,我们能够对仅有界且可测的测试函数以任何期望速率实现收敛。这是通过在随机时间网格上评估格式(如Euler)来实现的。为了建立收敛性,我们利用了在弱一致Hörmander条件下成立的格式正则化性质。

英文摘要:

We present a method for approximating solutions of Stochastic Differential Equations (SDEs) with arbitrary rates. This approximation is derived for bounded and measurable test functions. Specifically, we demonstrate that, leveraging the standard weak approximation properties of numerical schemes for smooth test functions (such as first-order weak convergence for the Euler scheme) we can achieve convergence for simply bounded and measurable test functions at any desired rate by constructing a tailored approximation for the semigroup of the SDE. This is achieved by evaluating the scheme (e.g., Euler) on a random time grid. To establish convergence, we exploit the regularization properties of the scheme, which hold under a weak uniform Hörmander condition.

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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