IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v90y2025i2d10.1007_s10589-024-00638-9.html
   My bibliography  Save this article

An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems

Author

Listed:
  • Huiling Zhang

    (Shanghai University)

  • Zi Xu

    (Shanghai University
    Shanghai University)

Abstract

Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the algorithm is proved to be $$\tilde{\mathcal {O}}(\varepsilon ^{-6.5})$$ O ~ ( ε - 6.5 ) to obtain an $$\varepsilon $$ ε -stationary point, which achieves the best-known complexity bound for single-loop algorithms to solve the stochastic nonconvex-concave minimax problems under the stationarity of the objective function.

Suggested Citation

  • Huiling Zhang & Zi Xu, 2025. "An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems," Computational Optimization and Applications, Springer, vol. 90(2), pages 557-582, March.
    • Handle: RePEc:spr:coopap:v:90:y:2025:i:2:d:10.1007_s10589-024-00638-9
    DOI: 10.1007/s10589-024-00638-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-024-00638-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10589-024-00638-9?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:spr:coopap:v:90:y:2025:i:2:d:10.1007_s10589-024-00638-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.