Split-step backward balanced Milstein methods for stiff stochastic systems

被引：41

作者：

Wang, Peng ^{[1
]}

Liu, Zhenxin ^{[2
]}

机构：

[1] Jilin Univ, Inst Math, Changchun 130012, Peoples R China

[2] Jilin Univ, Coll Math, Changchun 130012, Peoples R China

来源：

APPLIED NUMERICAL MATHEMATICS | 2009年 / 59卷 / 06期

关键词：

Stochastic differential equations; Balanced Milstein method; Stochastic Taylor expansion; Mean-square stability; Stiff equations; RUNGE-KUTTA METHODS; DIFFERENTIAL-EQUATIONS;

D O I：

10.1016/j.apnum.2008.06.001

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper we discuss split-step backward balanced Milstein methods for solving Ito stochastic differential equations (SDEs). Four families of methods, a family of drifting split-step backward balanced Milstein (DSSBBM) methods, a family of modified split-step backward balanced Milstein (MSSBBM) methods, a family of drifting split-step backward double balanced Milstein (DSSBDBM) methods and a family of modified split-step backward double balanced Milstein (MSSBDBM) methods, are constructed in this paper. Their order of strong convergence is proved. The stability properties and numerical results show the effectiveness of these methods in the pathwise approximation of stiff SDEs. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.

引用

页码：1198 / 1213

页数：16

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