IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v129y2006i2d10.1007_s10957-006-9066-z.html
   My bibliography  Save this article

Novel Algorithms for Noisy Minimization Problems with Applications to Neural Networks Training

Author

Listed:
  • K. Sirlantzis

    (University of Kent)

  • J. D. Lamb

    (University of Aberdeen Business School, King’s College)

  • W. B. Liu

    (University of Kent)

Abstract

The supervisor and searcher cooperation framework (SSC), introduced in Refs. 1 and 2, provides an effective way to design efficient optimization algorithms combining the desirable features of the two existing ones. This work aims to develop efficient algorithms for a wide range of noisy optimization problems including those posed by feedforward neural networks training. It introduces two basic SSC algorithms. The first seems suited for generic problems. The second is motivated by neural networks training problems. It introduces also inexact variants of the two algorithms, which seem to possess desirable properties. It establishes general theoretical results about the convergence and speed of SSC algorithms and illustrates their appealing attributes through numerical tests on deterministic, stochastic, and neural networks training problems.

Suggested Citation

  • K. Sirlantzis & J. D. Lamb & W. B. Liu, 2006. "Novel Algorithms for Noisy Minimization Problems with Applications to Neural Networks Training," Journal of Optimization Theory and Applications, Springer, vol. 129(2), pages 325-340, May.
    • Handle: RePEc:spr:joptap:v:129:y:2006:i:2:d:10.1007_s10957-006-9066-z
    DOI: 10.1007/s10957-006-9066-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-006-9066-z
    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/s10957-006-9066-z?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. W. Liu & Y. H. Dai, 2001. "Minimization Algorithms Based on Supervisor and Searcher Cooperation," Journal of Optimization Theory and Applications, Springer, vol. 111(2), pages 359-379, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jun-Fang Xu & Jing Xu & Shi-Zhu Li & Tia-Wu Jia & Xi-Bao Huang & Hua-Ming Zhang & Mei Chen & Guo-Jing Yang & Shu-Jing Gao & Qing-Yun Wang & Xiao-Nong Zhou, 2013. "Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 7(3), pages 1-11, March.

    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. Zhitao Xu & Li Gao, 2019. "The Uzawa-MBB type algorithm for nonsymmetric saddle point problems," Computational Optimization and Applications, Springer, vol. 73(3), pages 933-956, July.
    2. Zhong-bao Wang & Xue Chen & Jiang Yi & Zhang-you Chen, 2022. "Inertial projection and contraction algorithms with larger step sizes for solving quasimonotone variational inequalities," Journal of Global Optimization, Springer, vol. 82(3), pages 499-522, March.

    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:129:y:2006:i:2:d:10.1007_s10957-006-9066-z. 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.