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arXiv 1906.04096math.NAcs.NA
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具有分段连续自变量的随机微分方程的不变测度的后向欧拉方法逼近

Approximation of Invariant Measures for Stochastic Differential Equations with Piecewise Continuous Arguments via Backward Euler Method

Chuchu Chen, Jialin Hong, Yulan Lu

AI总结:

针对具有分段连续自变量和乘性噪声的耗散随机微分方程,采用后向欧拉方法离散化,证明数值解在整数时刻保持马尔可夫性并具有唯一不变测度,且数值不变测度以1阶收敛于原方程的不变测度,数值实验验证了理论结果。

摘要:

对于具有分段连续自变量(PCAs)、由乘性噪声驱动且漂移系数耗散的随机微分方程(SDE),我们证明了整数时刻的解是一个马尔可夫链,并承认唯一的不变测度。为了在数值上继承具有PCAs的SDE的不变测度,我们将后向欧拉(BE)方法应用于该方程,并证明了整数时刻的数值解不仅是马尔可夫的,而且再现了唯一的数值不变测度。我们在一定假设下给出了该方法的与时间无关的弱误差分析。进一步,我们证明了数值不变测度以1阶收敛于原始不变测度。数值实验验证了理论分析。

英文摘要:

For the stochastic differential equation (SDE) which has piecewise continuous arguments (PCAs), is driven by multiplicative noises and its drift coefficients are dissipative, we show that the solution at integer time is a Markov chain and admits a unique invariant measure. In order to inherit numerically the invariant measure of SDE with PCAs, we apply the backward Euler (BE) method to the equation, and prove that the numerical solution at integer time is not only Markovian but also reproduces a unique numerical invariant measure. We present the time-independent weak error analysis for the method under certain hypothesis. Further, we show that the numerical invariant measure converges to the original one with order 1. Numerical experiments verify the theoretical analysis.

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