Weak convergence of balanced stochastic Runge-Kutta methods for stochastic differential equations

被引：1

作者：

Rathinasamy, Anandaraman ^{[1
,2
,3
]}

Debrabant, Kristian ^{[4
]}

Nair, Priya ^{[5
]}

机构：

[1] Madras Inst Technol, Dept Appl Sci & Humanities, Chennai, India

[2] Madras Inst Technol, Dept Math, Chennai, India

[3] Anna Univ, Coll Engn Guindy campus, Chennai, India

[4] Univ Southern Denmark, Dept Math & Comp Sci, Odense, Denmark

[5] Rajalakshmi Engn Coll, Dept Math, Tandalam, India

来源：

RESEARCH IN MATHEMATICS | 2023年 / 10卷 / 01期

关键词：

balanced methods; stochastic Runge-Kutta methods; weak convergence; MEAN-SQUARE; IMPLICIT METHODS; 2ND-ORDER; APPROXIMATION; STABILITY; STIFF; SYSTEMS;

D O I：

10.1080/27684830.2022.2163546

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, weak convergence of balanced stochastic one-step methods and especially balanced stochastic Runge-Kutta (SRK) methods for Ito multidimensional stochastic differential equations is analyzed. Generalizing a corresponding result obtained by H. Schurz for the standard Euler method, it is shown that under certain conditions, balanced one-step methods preserve the weak convergence properties of their underlying methods. As an application, this allows to prove the weak convergence order of the balanced SRK methods presented in earlier work by A. Rathinasamy, P. Nair and D. Ahmadian.

引用

页数：6

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