IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v80y2021i1d10.1007_s10589-021-00297-0.html
   My bibliography  Save this article

Compact representations of structured BFGS matrices

Author

Listed:
  • Johannes J. Brust

    (Argonne National Laboratory)

  • Zichao (Wendy) Di

    (Argonne National Laboratory)

  • Sven Leyffer

    (Argonne National Laboratory)

  • Cosmin G. Petra

    (Lawrence Livermore National Laboratory)

Abstract

For general large-scale optimization problems compact representations exist in which recursive quasi-Newton update formulas are represented as compact matrix factorizations. For problems in which the objective function contains additional structure, recent structured quasi-Newton methods exploit available second-derivative information and approximate unavailable second derivatives. This article develops the compact representations of two structured Broyden-Fletcher-Goldfarb-Shanno update formulas. The compact representations enable efficient limited memory and initialization strategies. Two limited memory line search algorithms are described for which extensive numerical results demonstrate the efficacy of the algorithms, including comparisons to IPOPT on large machine learning problems, and to L-BFGS on a real world large scale ptychographic imaging application.

Suggested Citation

  • Johannes J. Brust & Zichao (Wendy) Di & Sven Leyffer & Cosmin G. Petra, 2021. "Compact representations of structured BFGS matrices," Computational Optimization and Applications, Springer, vol. 80(1), pages 55-88, September.
  • Handle: RePEc:spr:coopap:v:80:y:2021:i:1:d:10.1007_s10589-021-00297-0
    DOI: 10.1007/s10589-021-00297-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-021-00297-0
    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/s10589-021-00297-0?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. Johannes Brust & Oleg Burdakov & Jennifer B. Erway & Roummel F. Marcia, 2019. "A dense initialization for limited-memory quasi-Newton methods," Computational Optimization and Applications, Springer, vol. 74(1), pages 121-142, September.
    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. Espinoza, Oscar & Corradi, Bruno & González, Luis & Sandoval, Luis & McGinn, Noel & Maldonado, Karina & Larrondo, Yahira, 2023. "The effects of free tuition on the persistence of university students in Chile," International Journal of Educational Development, Elsevier, vol. 101(C).

    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. Johannes J. Brust & Roummel F. Marcia & Cosmin G. Petra, 2019. "Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints," Computational Optimization and Applications, Springer, vol. 74(3), pages 669-701, December.

    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:80:y:2021:i:1:d:10.1007_s10589-021-00297-0. 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.