IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-319-68640-0_16.html
   My bibliography  Save this book chapter

The Complexity of Approximation Reoptimization Algorithms for Discrete Optimization

In: Optimization Methods and Applications

Author

Listed:
  • Victor A. Mikhailyuk

    (Lesya Ukrainka Eastern European National University)

Abstract

The objective of postoptimality analysis and reoptimization using approximation methods is applying knowledge of the solution of the initial instance I of the problem (problem with the set values of input parameters) in order to either achieve a better quality of approximation (approximation ratio) of I′ (revised instance) or create a more efficient algorithm for determining an optimal or close to optimal solution of I′. This paper offers theoretical foundations for the acquisition, exploration, and use of bounds in the postoptimality analysis, as well as further development and improvement of approximation algorithms of reoptimization for discrete optimization problems. In particular, the upper and lower bounds on approximation ratio of approximate reoptimization algorithms for the constraint satisfaction problems (CSPs) are obtained using linear and semidefinite relaxations of the initial problem. In addition, sufficient conditions for the existence of polynomial approximation or threshold reoptimization algorithms for CSPs are obtained.

Suggested Citation

  • Victor A. Mikhailyuk, 2017. "The Complexity of Approximation Reoptimization Algorithms for Discrete Optimization," Springer Optimization and Its Applications, in: Sergiy Butenko & Panos M. Pardalos & Volodymyr Shylo (ed.), Optimization Methods and Applications, pages 313-344, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-68640-0_16
    DOI: 10.1007/978-3-319-68640-0_16
    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 search 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:spochp:978-3-319-68640-0_16. 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.