IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v200y2012i1p183-19810.1007-s10479-011-0987-z.html
   My bibliography  Save this article

Optimization of the quantile criterion for the convex loss function by a stochastic quasigradient algorithm

Author

Listed:
  • Andrey Kibzun
  • Evgeniy Matveev

Abstract

A stochastic quasigradient algorithm is suggested for solving the quantile optimization problem with a convex loss function. The algorithm is based on stochastic finite-difference approximations of gradients of the quantile function by using the order statistics. The algorithm convergence almost surely is proved. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Andrey Kibzun & Evgeniy Matveev, 2012. "Optimization of the quantile criterion for the convex loss function by a stochastic quasigradient algorithm," Annals of Operations Research, Springer, vol. 200(1), pages 183-198, November.
    • Handle: RePEc:spr:annopr:v:200:y:2012:i:1:p:183-198:10.1007/s10479-011-0987-z
    DOI: 10.1007/s10479-011-0987-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-011-0987-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-011-0987-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. Gupta, Somesh Das, 1980. "Brunn-Minkowski inequality and its aftermath," Journal of Multivariate Analysis, Elsevier, vol. 10(3), pages 296-318, September.
    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. Jiaqiao Hu & Yijie Peng & Gongbo Zhang & Qi Zhang, 2022. "A Stochastic Approximation Method for Simulation-Based Quantile Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2889-2907, November.

    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. Caplin, Andrew & Nalebuff, Barry, 1991. "Aggregation and Social Choice: A Mean Voter Theorem," Econometrica, Econometric Society, vol. 59(1), pages 1-23, January.
    2. Tamás L. Balogh & Christian Ewerhart, 2015. "On the origin of r-concavity and related concepts," ECON - Working Papers 187, Department of Economics - University of Zurich.

    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:annopr:v:200:y:2012:i:1:p:183-198:10.1007/s10479-011-0987-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.