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arXiv 0708.2850math.NAcs.NA
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线性随机系统的高效强积分器

Efficient strong integrators for linear stochastic systems

Heriot-Watt University(赫瑞-瓦特大学)

Gabriel Lord, Simon J. A. Malham, Anke Wiese

AI总结:

针对由任意数量Wiener过程驱动的线性随机微分方程,提出基于Neumann和Magnus展开的数值格式,证明Magnus展开法在均方意义下更精确,并推导任意维随机积分条件期望的最大收敛速率。

摘要:

我们提出了数值格式,用于求解由任意数量Wiener过程驱动的线性随机微分方程的强解。这些格式基于Neumann(随机泰勒)展开和Magnus展开。首先,我们考虑当线性扩散向量场相互交换但不与线性漂移向量场交换的情况。我们证明,基于Magnus展开的数值方法在均方意义下比相应的随机泰勒积分格式更精确。其次,我们推导了由条件期望近似的任意多维随机积分的最大收敛速率。因此,对于具有非交换向量场的一般非线性随机微分方程,我们推导了任意阶方法的误差与计算成本之间的显式关系。第三,我们在两个数值研究中考虑了结果,其中一个应用出现在随机线性二次最优控制中。

英文摘要:

We present numerical schemes for the strong solution of linear stochastic differential equations driven by an arbitrary number of Wiener processes. These schemes are based on the Neumann (stochastic Taylor) and Magnus expansions. Firstly, we consider the case when the governing linear diffusion vector fields commute with each other, but not with the linear drift vector field. We prove that numerical methods based on the Magnus expansion are more accurate in the mean-square sense than corresponding stochastic Taylor integration schemes. Secondly, we derive the maximal rate of convergence for arbitrary multi-dimensional stochastic integrals approximated by their conditional expectations. Consequently, for general nonlinear stochastic differential equations with non-commuting vector fields, we deduce explicit formulae for the relation between error and computational costs for methods of arbitrary order. Thirdly, we consider the consequences in two numerical studies, one of which is an application arising in stochastic linear-quadratic optimal control.

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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