IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-58405-3_9.html
   My bibliography  Save this book chapter

Scylla: A Matrix-Free Fix-Propagate-and-Project Heuristic for Mixed-Integer Optimization

In: Operations Research Proceedings 2023

Author

Listed:
  • Gioni Mexi

    (Interactive Optimization and Learning, Zuse Institute Berlin)

  • Mathieu Besançon

    (Interactive Optimization and Learning, Zuse Institute Berlin
    Université Grenoble Alpes, Inria, LIG, CNRS)

  • Suresh Bolusani

    (Interactive Optimization and Learning, Zuse Institute Berlin)

  • Antonia Chmiela

    (Interactive Optimization and Learning, Zuse Institute Berlin)

  • Alexander Hoen

    (Interactive Optimization and Learning, Zuse Institute Berlin)

  • Ambros Gleixner

    (Interactive Optimization and Learning, Zuse Institute Berlin
    HTW Berlin)

Abstract

We introduce Scylla, a primal heuristic for mixed-integer optimization problems. It exploits approximate solves of the Linear Programming relaxations through the matrix-free Primal-Dual Hybrid Gradient algorithm with specialized termination criteria, and derives integer-feasible solutions via fix-and-propagate procedures and feasibility-pump-like updates to the objective function. Computational experiments show that the method is particularly suited to instances with hard linear relaxations.

Suggested Citation

  • Gioni Mexi & Mathieu Besançon & Suresh Bolusani & Antonia Chmiela & Alexander Hoen & Ambros Gleixner, 2025. "Scylla: A Matrix-Free Fix-Propagate-and-Project Heuristic for Mixed-Integer Optimization," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 65-72, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_9
    DOI: 10.1007/978-3-031-58405-3_9
    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

    Keywords

    ;
    ;
    ;

    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:lnopch:978-3-031-58405-3_9. 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.