• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    A fully stochastic primal-dual algorithm

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    Bianchi, Pascal
    Hachem, Walid
    Salim, Adil cc
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-07-14
    Online Publication Date
    2020-07-14
    Print Publication Date
    2021-03
    Embargo End Date
    2021-07-14
    Submitted Date
    2019-08-19
    Permanent link to this record
    http://hdl.handle.net/10754/664383
    
    Metadata
    Show full item record
    Abstract
    A new stochastic primal-dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions / operators that enter the optimization problem are given as statistical expectations. These expectations are unknown but revealed across time through i.i.d realizations. The proposed algorithm is proven to converge to a saddle point of the Lagrangian function. In the framework of the monotone operator theory, the convergence proof relies on recent results on the stochastic Forward Backward algorithm involving random monotone operators. An example of convex optimization under stochastic linear constraints is considered.
    Citation
    Bianchi, P., Hachem, W., & Salim, A. (2020). A fully stochastic primal-dual algorithm. Optimization Letters. doi:10.1007/s11590-020-01614-y
    Publisher
    Springer Nature
    Journal
    Optimization Letters
    DOI
    10.1007/s11590-020-01614-y
    arXiv
    1901.08170
    Additional Links
    http://link.springer.com/10.1007/s11590-020-01614-y
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11590-020-01614-y
    Scopus Count
    Collections
    Articles; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.