A General Class of One-Step Approximation for Index-1 Stochastic Delay-Differential-Algebraic Equations
Year: 2019
Author: Tingting Qin, Chengjian Zhang
Journal of Computational Mathematics, Vol. 37 (2019), Iss. 2 : pp. 151–169
Abstract
This paper develops a class of general one-step discretization methods for solving the index-1 stochastic delay differential-algebraic equations. The existence and uniqueness theorem of strong solutions of index-1 equations is given. A strong convergence criterion of the methods is derived, which is applicable to a series of one-step stochastic numerical methods. Some specific numerical methods, such as the Euler-Maruyama method, stochastic $θ$-methods, split-step $θ$-methods are proposed, and their strong convergence results are given. Numerical experiments further illustrate the theoretical results.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1711-m2016-0810
Journal of Computational Mathematics, Vol. 37 (2019), Iss. 2 : pp. 151–169
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 19
Keywords: Stochastic delay differential-algebraic equations One-step discretization schemes Strong convergence.
Author Details
-
Multivariable Linear Algebraic Discretization of Nonlinear Parabolic Equations for Computational Analysis
Zuo, Li
Mei, Fengtai
Kaur, Amandeep
Computational Intelligence and Neuroscience, Vol. 2022 (2022), Iss. P.1
https://doi.org/10.1155/2022/6323418 [Citations: 0]