Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145309
Title: Convergence of asynchronous distributed gradient methods over stochastic networks
Authors: Xu, Jinming
Zhu, Shanying
Soh, Yeng Chai
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Xu, J., Zhu, S., Soh, Y. C., & Xie, L. (2018). Convergence of asynchronous distributed gradient methods over stochastic networks. IEEE Transactions on Automatic Control, 63(2), 434-448. doi:10.1109/TAC.2017.2730481
Journal: IEEE Transactions on Automatic Control
Abstract: We consider distributed optimization problems in which a number of agents are to seek the global optimum of a sum of cost functions through only local information sharing. In this paper, we are particularly interested in scenarios, where agents are operating asynchronously over stochastic networks subject to random failures. Most existing algorithms require coordinated and decaying stepsizes to ensure zero gap between the estimated value of each agent and the exact optimum, restricting it from asynchronous implementation and resulting in slower convergence results. To deal with this issue, we develop a new asynchronous distributed gradient method (AsynDGM) based on consensus theory. The proposed algorithm not only allows for asynchronous implementation in a completely distributed manner but also, most importantly, is able to seek the exact optimum even with constant stepsizes. We will show that the assumption of boundedness of gradients, which is widely used in the literature, can be dropped by instead imposing the standard Lipschitz continuity condition on gradients. Moreover, we derive an upper bound of stepsize within which the proposed AsynDGM can achieve a linear convergence rate for strongly convex functions with Lipschitz gradients. A canonical example of sensor fusion problems is provided to illustrate the effectiveness of the proposed algorithm.
URI: https://hdl.handle.net/10356/145309
ISSN: 1558-2523
DOI: 10.1109/TAC.2017.2730481
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TAC.2017.2730481
Fulltext Permission: none
Fulltext Availability: No Fulltext
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