IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v50y2011i3p597-617.html
   My bibliography  Save this article

Gradient algorithms for quadratic optimization with fast convergence rates

Author

Listed:
  • Luc Pronzato
  • Anatoly Zhigljavsky

Abstract

No abstract is available for this item.

Suggested Citation

  • Luc Pronzato & Anatoly Zhigljavsky, 2011. "Gradient algorithms for quadratic optimization with fast convergence rates," Computational Optimization and Applications, Springer, vol. 50(3), pages 597-617, December.
    • Handle: RePEc:spr:coopap:v:50:y:2011:i:3:p:597-617
    DOI: 10.1007/s10589-010-9319-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-010-9319-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-010-9319-5?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.

    References listed on IDEAS

    as
    1. R. Haycroft & L. Pronzato & H. P. Wynn & A. Zhigljavsky, 2009. "Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory," Springer Optimization and Its Applications, in: Luc Pronzato & Anatoly Zhigljavsky (ed.), Optimal Design and Related Areas in Optimization and Statistics, chapter 2, pages 13-37, Springer.
    2. L. Pronzato & H.P. Wynn & A. Zhigljavsky, 2009. "A Dynamical-System Analysis of the Optimum s-Gradient Algorithm," Springer Optimization and Its Applications, in: Luc Pronzato & Anatoly Zhigljavsky (ed.), Optimal Design and Related Areas in Optimization and Statistics, chapter 3, pages 39-80, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. di Serafino, Daniela & Ruggiero, Valeria & Toraldo, Gerardo & Zanni, Luca, 2018. "On the steplength selection in gradient methods for unconstrained optimization," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 176-195.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antanas Žilinskas & Jonathan Gillard & Megan Scammell & Anatoly Zhigljavsky, 2021. "Multistart with early termination of descents," Journal of Global Optimization, Springer, vol. 79(2), pages 447-462, February.

    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:50:y:2011:i:3:p:597-617. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.