IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v91y2025i2d10.1007_s10589-024-00606-3.html
   My bibliography  Save this article

Convergence of a quasi-Newton method for solving systems of nonlinear underdetermined equations

Author

Listed:
  • N. Vater

    (Universität Würzburg)

  • A. Borzì

    (Universität Würzburg)

Abstract

The development and convergence analysis of a quasi-Newton method for the solution of systems of nonlinear underdetermined equations is investigated. These equations arise in many application fields, e.g., supervised learning of large overparameterised neural networks, which require the development of efficient methods with guaranteed convergence. In this paper, a new approach for the computation of the Moore–Penrose inverse of the approximate Jacobian coming from the Broyden update is presented and a semi-local convergence result for a damped quasi-Newton method is proved. The theoretical results are illustrated in detail for the case of systems of multidimensional quadratic equations, and validated in the context of eigenvalue problems and supervised learning of overparameterised neural networks.

Suggested Citation

  • N. Vater & A. Borzì, 2025. "Convergence of a quasi-Newton method for solving systems of nonlinear underdetermined equations," Computational Optimization and Applications, Springer, vol. 91(2), pages 973-996, June.
  • Handle: RePEc:spr:coopap:v:91:y:2025:i:2:d:10.1007_s10589-024-00606-3
    DOI: 10.1007/s10589-024-00606-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-024-00606-3
    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/s10589-024-00606-3?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:91:y:2025:i:2:d:10.1007_s10589-024-00606-3. 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.