IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v498y2025ics0096300325001225.html
   My bibliography  Save this article

Convergence and stability in mean square of the stochastic θ-methods for systems of NSDDEs under a coupled monotonicity condition

Author

Listed:
  • Niu, Mengyao
  • Niu, Yuanling
  • Wei, Jiaxin

Abstract

Our research is devoted to investigating the convergence and stability in mean square of the stochastic θ-methods applied to neutral stochastic differential delay equations (NSDDEs) with super-linearly growing coefficients. Under a coupled monotonicity condition, we show that the numerical approximations of the stochastic θ-methods with θ∈[12,1] converge to the exact solution of NSDDEs strongly with order 12. Moreover, it is shown that the stochastic θ-methods are capable of preserving the stability of the exact solution of original equations for any given stepsize h>0. Finally, several numerical examples are presented to illustrate the theoretical findings.

Suggested Citation

  • Niu, Mengyao & Niu, Yuanling & Wei, Jiaxin, 2025. "Convergence and stability in mean square of the stochastic θ-methods for systems of NSDDEs under a coupled monotonicity condition," Applied Mathematics and Computation, Elsevier, vol. 498(C).
    • Handle: RePEc:eee:apmaco:v:498:y:2025:i:c:s0096300325001225
    DOI: 10.1016/j.amc.2025.129395
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325001225
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2025.129395?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:498:y:2025:i:c:s0096300325001225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.