IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v53y2021i10p1081-1093.html
   My bibliography  Save this article

Solving Bayesian risk optimization via nested stochastic gradient estimation

Author

Listed:
  • Sait Cakmak
  • Di Wu
  • Enlu Zhou

Abstract

In this article, we aim to solve Bayesian Risk Optimization (BRO), which is a recently proposed framework that formulates simulation optimization under input uncertainty. In order to efficiently solve the BRO problem, we derive nested stochastic gradient estimators and propose corresponding stochastic approximation algorithms. We show that our gradient estimators are asymptotically unbiased and consistent, and that the algorithms converge asymptotically. We demonstrate the empirical performance of the algorithms on a two-sided market model. Our estimators are of independent interest in extending the literature of stochastic gradient estimation to the case of nested risk measures.

Suggested Citation

  • Sait Cakmak & Di Wu & Enlu Zhou, 2021. "Solving Bayesian risk optimization via nested stochastic gradient estimation," IISE Transactions, Taylor & Francis Journals, vol. 53(10), pages 1081-1093, October.
    • Handle: RePEc:taf:uiiexx:v:53:y:2021:i:10:p:1081-1093
    DOI: 10.1080/24725854.2020.1869352
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2020.1869352
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24725854.2020.1869352?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.

    Citations

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


    Cited by:

    1. Xin Yun & Yanyi Ye & Hao Liu & Yi Li & Kin-Keung Lai, 2023. "Stylized Model of Lévy Process in Risk Estimation," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    2. Zhiping Chen & Wentao Ma, 2024. "A Bayesian approach to data-driven multi-stage stochastic optimization," Journal of Global Optimization, Springer, vol. 90(2), pages 401-428, October.

    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:taf:uiiexx:v:53:y:2021:i:10:p:1081-1093. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.