• Login
    View Item 
    •   Home
    • Research
    • Preprints
    • View Item
    •   Home
    • Research
    • Preprints
    • 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 Stochastic Penalty Model for Convex and Nonconvex Optimization with Big Constraints

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1810.13387.pdf
    Size:
    5.885Mb
    Format:
    PDF
    Description:
    Preprint
    Download
    Type
    Preprint
    Authors
    Mishchenko, Konstantin cc
    Richtarik, Peter cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-10-31
    Permanent link to this record
    http://hdl.handle.net/10754/653118
    
    Metadata
    Show full item record
    Abstract
    The last decade witnessed a rise in the importance of supervised learningapplications involving {\em big data} and {\em big models}. Big data refers tosituations where the amounts of training data available and needed causesdifficulties in the training phase of the pipeline. Big model refers tosituations where large dimensional and over-parameterized models are needed forthe application at hand. Both of these phenomena lead to a dramatic increase inresearch activity aimed at taming the issues via the design of newsophisticated optimization algorithms. In this paper we turn attention to the{\em big constraints} scenario and argue that elaborate machine learningsystems of the future will necessarily need to account for a large number ofreal-world constraints, which will need to be incorporated in the trainingprocess. This line of work is largely unexplored, and provides ampleopportunities for future work and applications. To handle the {\em bigconstraints} regime, we propose a {\em stochastic penalty} formulation which{\em reduces the problem to the well understood big data regime}. Ourformulation has many interesting properties which relate it to the originalproblem in various ways, with mathematical guarantees. We give a number ofresults specialized to nonconvex loss functions, smooth convex functions,strongly convex functions and convex constraints. We show through experimentsthat our approach can beat competing approaches by several orders of magnitudewhen a medium accuracy solution is required.
    Publisher
    arXiv
    arXiv
    1810.13387
    Additional Links
    https://arxiv.org/pdf/1810.13387
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  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.