Tamed Runge-Kutta methods for SDEs with super-linearly growing drift and diffusion coefficients

Tamed Runge-Kutta methods for SDEs with super-linearly growing drift and diffusion coefficients
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具有超线性增长漂移和扩散系数的 SDE 驯服龙格-库塔方法

DOI:
10.1016/j.apnum.2019.11.014
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发表时间:
2020-06
影响因子:
2.8
通讯作者:
Wang Xiaojie
Wang Xiaojie
中科院分区:
数学2区
文献类型:
--
作者:
Gan Siqing;He Youzi;Wang Xiaojie

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传统的显式格式,如Euler-Maruyama,Milstein和随机Runge-Kutta方法,在求解系数超线性增长的随机微分方程(SDEs)时,通常会导致强发散和弱发散。受此启发,文献中构建并分析了显式欧拉和米尔斯坦方法的各种修改版本。在本文中,我们的目的是引入一族显式驯服随机龙格库塔(TSRK)方法的超线性增长的漂移和扩散系数的交换的SDES。强收敛速度的顺序1.0成功地确定所提出的方法在一定的非全局Lipschitz条件下。与Milstein型方法相比,新提出的无导数TSRK方法可以在计算上更有效。数值实验证实了预期的TSRK方法的强收敛速度。
Traditional explicit schemes such as the Euler-Maruyama, Milstein and stochastic Runge-Kutta methods, in general, result in strong and weak divergence when solving stochastic differential equations (SDEs) with super-linearly growing coefficients. Motivated by this, various modified versions of explicit Euler and Milstein methods were constructed and analyzed in the literature. In the present paper, we aim to introduce a family of explicit tamed stochastic Runge-Kutta (TSRK) methods for commutative SDEs with super-linearly growing drift and diffusion coefficients. Strong convergence rates of order 1.0 are successfully identified for the proposed methods under certain non-globally Lipschitz conditions. Compared to the Milstein-type methods involved with derivatives of coefficients, the newly proposed derivative-free TSRK methods can be computationally more efficient. Numerical experiments are reported to confirm the expected strong convergence rate of the TSRK methods.
DOI: 10.1080/17442509408833885
发表时间: 1994-03
期刊: Stochastics and Stochastics Reports
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