Improved Convergence Rates for Distributed Resource Allocation

Angelia Nedich, Alex Olshevsky, Wei Shi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation optimization problem over a connected network. By observing a connection between the resource allocation and the consensus optimization, we propose a novel class of algorithms for solving the resource allocation problem with improved convergence guarantees. Specifically, we introduce an algorithm for solving the resource allocation problem with an o(1/k) convergence rate when the agents' objective functions are generally convex and per agent local constraints are allowed; we then introduce a gradient-based algorithm for the case when per agent local constraints are absent and show that such scheme achieves geometric convergence with an improved scalability. We also provide a projection-gradient-based algorithm which can handle smooth objective and simple constraints more efficiently.

    Original languageEnglish (US)
    Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages172-177
    Number of pages6
    ISBN (Electronic)9781538613955
    DOIs
    StatePublished - Jan 18 2019
    Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
    Duration: Dec 17 2018Dec 19 2018

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    Volume2018-December
    ISSN (Print)0743-1546

    Conference

    Conference57th IEEE Conference on Decision and Control, CDC 2018
    CountryUnited States
    CityMiami
    Period12/17/1812/19/18

    Fingerprint

    Resource Allocation
    Resource allocation
    Convergence Rate
    Geometric Convergence
    Gradient Projection
    Decentralized
    Scalability
    Objective function
    Gradient
    Optimization Problem
    Optimization
    Class

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Modeling and Simulation
    • Control and Optimization

    Cite this

    Nedich, A., Olshevsky, A., & Shi, W. (2019). Improved Convergence Rates for Distributed Resource Allocation. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 172-177). [8619322] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619322

    Improved Convergence Rates for Distributed Resource Allocation. / Nedich, Angelia; Olshevsky, Alex; Shi, Wei.

    2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 172-177 8619322 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Nedich, A, Olshevsky, A & Shi, W 2019, Improved Convergence Rates for Distributed Resource Allocation. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619322, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 172-177, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619322
    Nedich A, Olshevsky A, Shi W. Improved Convergence Rates for Distributed Resource Allocation. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 172-177. 8619322. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8619322
    Nedich, Angelia ; Olshevsky, Alex ; Shi, Wei. / Improved Convergence Rates for Distributed Resource Allocation. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 172-177 (Proceedings of the IEEE Conference on Decision and Control).
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