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arXiv 1502.05034math.PRcs.NAmath.NA
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连续时间随机游走用于随机微分方程的数值求解

Continuous-time Random Walks for the Numerical Solution of Stochastic Differential Equations

Rutgers University(罗格斯大学);New York University(纽约大学)

Nawaf Bou-Rabee, Eric Vanden-Eijnden

AI总结:

本文提出连续时间数值方案用于求解金融数学、种群动力学、化学动力学等领域的随机微分方程,通过空间离散化Kolmogorov方程,生成可精确模拟的马尔可夫跳跃过程,证明其在稳定性及准确性上的优势。

摘要:

本文介绍了一种连续时间的数值方案,用于模拟数学金融、种群动力学、化学动力学、流行病学、生物物理和聚集体流体等领域的随机微分方程(SDEs)。这些方案通过空间离散化与SDE相关的Kolmogorov方程,使得得到的半离散方程生成一个可精确用蒙特卡罗方法实现的马尔可夫跳跃过程。在此构造中,近似解的空间增量可被均匀地限制在空间上,这保证了这些方案在有限时间和长时间模拟SDEs时的数值稳定性。通过直接分析近似的生成元,我们证明当应用于具有局部Lipschitz连续漂移场和弱耗散性的SDE时,近似具有尖锐的随机Lyapunov函数。我们利用这个随机Lyapunov函数扩展近似的局部semimartingale表示。这种扩展允许分析近似的复杂性。利用Banach空间上线性算子半群的理论,我们证明近似在表示有限和无限时间统计时是(弱)准确的,其精度阶与生成元相同。证明是在固定和可变空间步长的背景下进行的。理论和数值研究证实了这些陈述,并提供了证据,表明这些方案在某些方面优于基于时间离散化的标准方法。特别是,它们是准确的,消除了在模拟具有边界(或受限域)的SDE时的非物理移动,防止在模拟刚性SDE时出现轨迹爆炸,且在解决首次退出问题时避免时间插值误差。

英文摘要:

This paper introduces time-continuous numerical schemes to simulate stochastic differential equations (SDEs) arising in mathematical finance, population dynamics, chemical kinetics, epidemiology, biophysics, and polymeric fluids. These schemes are obtained by spatially discretizing the Kolmogorov equation associated with the SDE in such a way that the resulting semi-discrete equation generates a Markov jump process that can be realized exactly using a Monte Carlo method. In this construction the spatial increment of the approximation can be bounded uniformly in space, which guarantees that the schemes are numerically stable for both finite and long time simulation of SDEs. By directly analyzing the generator of the approximation, we prove that the approximation has a sharp stochastic Lyapunov function when applied to an SDE with a drift field that is locally Lipschitz continuous and weakly dissipative. We use this stochastic Lyapunov function to extend a local semimartingale representation of the approximation. This extension permits to analyze the complexity of the approximation. Using the theory of semigroups of linear operators on Banach spaces, we show that the approximation is (weakly) accurate in representing finite and infinite-time statistics, with an order of accuracy identical to that of its generator. The proofs are carried out in the context of both fixed and variable spatial step sizes. Theoretical and numerical studies confirm these statements, and provide evidence that these schemes have several advantages over standard methods based on time-discretization. In particular, they are accurate, eliminate nonphysical moves in simulating SDEs with boundaries (or confined domains), prevent exploding trajectories from occurring when simulating stiff SDEs, and solve first exit problems without time-interpolation errors.

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