IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v170y2016i2d10.1007_s10957-016-0932-z.html
   My bibliography  Save this article

Dual Averaging with Adaptive Random Projection for Solving Evolving Distributed Optimization Problems

Author

Listed:
  • Shreyas Vathul Subramanian

    (Purdue University)

  • Daniel A. DeLaurentis

    (Purdue University)

  • Dengfeng Sun

    (Purdue University)

Abstract

We study a sequential form of the distributed dual averaging algorithm that minimizes the sum of convex functions in a special case where the number of functions increases gradually. This is done by introducing an intermediate ‘pivot’ stage posed as a convex feasibility problem that minimizes average constraint violation with respect to a family of convex sets. Under this approach, we introduce a version of the minimum sum optimization problem that incorporates an evolving design space. Proof of mathematical convergence of the algorithm is complemented by an application problem that involves finding the location of a noisy, mobile source using an evolving wireless sensor network. Results obtained confirm that the new designs in the evolved design space are superior to the ones found in the original design space due to the unique path followed to reach the optimum.

Suggested Citation

  • Shreyas Vathul Subramanian & Daniel A. DeLaurentis & Dengfeng Sun, 2016. "Dual Averaging with Adaptive Random Projection for Solving Evolving Distributed Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 493-511, August.
    • Handle: RePEc:spr:joptap:v:170:y:2016:i:2:d:10.1007_s10957-016-0932-z
    DOI: 10.1007/s10957-016-0932-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-016-0932-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/s10957-016-0932-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. Q. Li & W. S. Wong, 2009. "Optimal Estimator for Distributed Anonymous Observers," Journal of Optimization Theory and Applications, Springer, vol. 140(1), pages 55-75, January.
    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:170:y:2016:i:2:d:10.1007_s10957-016-0932-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.