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Titlebook: Distributed Optimization in Networked Systems; Algorithms and Appli Qingguo Lü,Xiaofeng Liao,Shanfu Gao Book 2023 The Editor(s) (if applica

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書目名稱Distributed Optimization in Networked Systems
副標(biāo)題Algorithms and Appli
編輯Qingguo Lü,Xiaofeng Liao,Shanfu Gao
視頻videohttp://file.papertrans.cn/282/281931/281931.mp4
概述Introduces readers to state-of-the-art and advanced distributed optimization algorithms in networked systems.Proposes effective strategies for rapid convergence and efficient execution of distributed
叢書名稱Wireless Networks
圖書封面Titlebook: Distributed Optimization in Networked Systems; Algorithms and Appli Qingguo Lü,Xiaofeng Liao,Shanfu Gao Book 2023 The Editor(s) (if applica
描述.This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike..
出版日期Book 2023
關(guān)鍵詞Distributed optimization; Networked systems; Acceleration; Communication efficiency; Computational effic
版次1
doihttps://doi.org/10.1007/978-981-19-8559-1
isbn_softcover978-981-19-8561-4
isbn_ebook978-981-19-8559-1Series ISSN 2366-1186 Series E-ISSN 2366-1445
issn_series 2366-1186
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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https://doi.org/10.1057/9780230253001omentum terms and employs non-uniform step-sizes. This approach can effectively overcome the abovementioned limitations of column-stochastic directed networks in the implementation. The implementation of D-DNGT is straightforward if each node locally chooses a suitable step-size and privately regula
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https://doi.org/10.1057/9780230253001ctation when each constituent function (smooth) is strongly convex if the constant step-size is less than an explicitly calculated upper constraint. Regarding the current distributed methods, the suggested technique not only has a low computation cost in terms of the overall number of local gradient
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https://doi.org/10.1057/9780230270589orithm, D-DLM, which integrates a distributed gradient tracking method with two momentum terms and non-uniform step-sizes in the update of the Lagrangian multipliers. Next, we give proof that if the maximum step-size and the maximum momentum coefficient are positive and sufficiently small, the D-DLM
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