IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v74y2019i3d10.1007_s10589-019-00127-4.html
   My bibliography  Save this article

Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints

Author

Listed:
  • Johannes J. Brust

    (Argonne National Laboratory)

  • Roummel F. Marcia

    (University of California Merced)

  • Cosmin G. Petra

    (Lawrence Livermore National Laboratory)

Abstract

We propose two limited-memory BFGS (L-BFGS) trust-region methods for large-scale optimization with linear equality constraints. The methods are intended for problems where the number of equality constraints is small. By exploiting the structure of the quasi-Newton compact representation, both proposed methods solve the trust-region subproblems nearly exactly, even for large problems. We derive theoretical global convergence results of the proposed algorithms, and compare their numerical effectiveness and performance on a variety of large-scale problems.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:coopap:v:74:y:2019:i:3:d:10.1007_s10589-019-00127-4
    DOI: 10.1007/s10589-019-00127-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-019-00127-4
    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/s10589-019-00127-4?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 & Jennifer B. Erway & Roummel F. Marcia, 2017. "On solving L-SR1 trust-region subproblems," Computational Optimization and Applications, Springer, vol. 66(2), pages 245-266, March.
    2. 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.
    3. Oleg Burdakov & José Martínez & Elvio Pilotta, 2002. "A Limited-Memory Multipoint Symmetric Secant Method for Bound Constrained Optimization," Annals of Operations Research, Springer, vol. 117(1), pages 51-70, November.
    Full references (including those not matched with items on IDEAS)

    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 & 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.

    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:74:y:2019:i:3:d:10.1007_s10589-019-00127-4. 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.