IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-34910-3_5.html
   My bibliography  Save this book chapter

Limited Memory Bundle Method and Its Variations for Large-Scale Nonsmooth Optimization

In: Numerical Nonsmooth Optimization

Author

Listed:
  • Napsu Karmitsa

    (Department of Mathematics and Statistics, University of Turku)

Abstract

There exist a vast variety of practical problems involving nonsmooth functions with large dimensions and nonconvex characteristics. Nevertheless, most nonsmooth solution methods have been designed to solve only small- or medium scale problems and they are heavily based on the convexity of the problem. In this chapter we describe three numerical methods for solving large-scale nonconvex NSO problems. Namely, the limited memory bundle algorithm (LMBM), the diagonal bundle method (D-Bundle), and the splitting metrics diagonal bundle method (SMDB). We also recall the convergence properties of these algorithms. To demonstrate the usability of the methods in large-scale settings, numerical experiments have been made using academic NSO problems with up to million variables.

Suggested Citation

  • Napsu Karmitsa, 2020. "Limited Memory Bundle Method and Its Variations for Large-Scale Nonsmooth Optimization," Springer Books, in: Adil M. Bagirov & Manlio Gaudioso & Napsu Karmitsa & Marko M. Mäkelä & Sona Taheri (ed.), Numerical Nonsmooth Optimization, chapter 0, pages 167-199, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-34910-3_5
    DOI: 10.1007/978-3-030-34910-3_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:sprchp:978-3-030-34910-3_5. 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.