Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162402
Title: Distributed online convex optimization with an aggregative variable
Authors: Li, Xiuxian
Yi, Xinlei
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Li, X., Yi, X. & Xie, L. (2021). Distributed online convex optimization with an aggregative variable. IEEE Transactions On Control of Network Systems, 9(1), 438-449. https://dx.doi.org/10.1109/TCNS.2021.3107480
Project: RG72/19
Journal: IEEE Transactions on Control of Network Systems
Abstract: This article investigates distributed online convex optimization in the presence of an aggregative variable without any global/central coordinators over a multiagent network. In this problem, each individual agent is only able to access partial information of time-varying global loss functions, thus requiring local information exchanges between neighboring agents. Motivated by many applications in reality, the considered local loss functions depend not only on their own decision variables, but also on an aggregative variable, such as the average of all decision variables. To handle this problem, an online distributed gradient tracking algorithm (O-DGT) is proposed with exact gradient information and it is shown that the dynamic regret is upper bounded by three terms: 1) a sublinear term; 2) a path variation term; and 3) a gradient variation term. Meanwhile, the O-DGT algorithm is also analyzed with stochastic/noisy gradients, showing that the expected dynamic regret has the same upper bound as the exact gradient case. To our best knowledge, this article is the first to study online convex optimization in the presence of an aggregative variable, which enjoys new characteristics in comparison with the conventional scenario without the aggregative variable. Finally, a numerical experiment is provided to corroborate the obtained theoretical results.
URI: https://hdl.handle.net/10356/162402
ISSN: 2325-5870
DOI: 10.1109/TCNS.2021.3107480
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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