Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Ascent

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    Author
    Lin, Y; Shames, I; Nesic, D
    Date
    2020-03-12
    Source Title
    Proceedings of the ... IEEE Conference on Decision & Control / IEEE Control Systems Society. IEEE Conference on Decision & Control
    Publisher
    IEEE
    University of Melbourne Author/s
    Shames, Iman; Nesic, Dragan; Lin, Yankai
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Conference Paper
    Citations
    Lin, Y., Shames, I. & Nesic, D. (2020). Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Ascent. Proceedings of the 2019 IEEE 58th Conference on Decision and Control (CDC), 2019-December, pp.6380-6385. IEEE. https://doi.org/10.1109/CDC40024.2019.9029518.
    Access Status
    This item is embargoed and will be available on 2021-03-12
    URI
    http://hdl.handle.net/11343/251784
    DOI
    10.1109/CDC40024.2019.9029518
    ARC Grant code
    ARC/DP170104099
    Abstract
    We study a class of distributed optimization problems of minimizing the sum of potentially non-differentiable convex objective functions (without requiring strong convexity). A novel approach to the analysis of asynchronous distributed optimization is developed. An iterative algorithm based on dual decomposition and block coordinate ascent is implemented in an edge based manner. We extend available results in the literature by allowing multiple and potentially overlapping blocks to be updated at the same time with non-uniform probabilities assigned to different blocks. Sublinear convergence with probability one is proved for the algorithm under the aforementioned weak assumptions. A numerical example is provided to illustrate the effectiveness of the algorithm.

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