IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v110y2001i2d10.1023_a1017539732327.html
   My bibliography  Save this article

Partitioned Random Search for Global Optimization with Sampling Cost and Discounting Factor

Author

Listed:
  • H. Q. Ye
  • Z. B. Tang

Abstract

The method of partitioned random search has been proposed in recent years to obtain an as good as possible solution for the global optimization problem (1). A practical algorithm has been developed and applied to real-life problems. However, the design of this algorithm was based mainly on intuition. The theoretical foundation of the method is an important issue in the development of efficient algorithms for such problems. In this paper, we generalize previous theoretical results and propose a sequential sampling policy for the partitioned random search for global optimization with sampling cost and discounting factor. A proof of the optimality of the proposed sequential sampling policy is given by using the theory of optimal stopping.

Suggested Citation

  • H. Q. Ye & Z. B. Tang, 2001. "Partitioned Random Search for Global Optimization with Sampling Cost and Discounting Factor," Journal of Optimization Theory and Applications, Springer, vol. 110(2), pages 445-455, August.
    • Handle: RePEc:spr:joptap:v:110:y:2001:i:2:d:10.1023_a:1017539732327
    DOI: 10.1023/A:1017539732327
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1017539732327
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1017539732327?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. Z. B. Tang, 1998. "Optimal Sequential Sampling Policy of Partitioned Random Search and Its Approximation," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 431-448, August.
    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.

      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:joptap:v:110:y:2001:i:2:d:10.1023_a:1017539732327. 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.