Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Ascent
AuthorLin, Y; Shames, I; Nesic, D
Source TitleProceedings of the ... IEEE Conference on Decision & Control / IEEE Control Systems Society. IEEE Conference on Decision & Control
AffiliationElectrical and Electronic Engineering
Document TypeConference Paper
CitationsLin, 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 StatusThis item is embargoed and will be available on 2021-03-12
ARC Grant codeARC/DP170104099
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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