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ECO-PANDA: A COMPUTATIONALLY ECONOMIC, GEOMETRICALLY CONVERGING DUAL OPTIMIZATION METHOD ON TIME-VARYING UNDIRECTED GRAPHS
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 5257-5261Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we consider distributed convex optimization over time-varying undirected graphs. We propose a linearized version of primarily averaged network dual ascent (PANDA) that keeps the advantages of PANDA while requiring less computational costs. The proposed method, economic primarily averaged network dual ascent (Eco-PANDA), provably converges at R-linear rate to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. Therefore, the method is competitive, in terms of type of rate, with both DIGing and PANDA. The proposed method halves the communication costs of methods like DIGing while still converging R-linearly and having the same per iterate complexity.

Place, publisher, year, edition, pages
IEEE , 2019. p. 5257-5261
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords [en]
Distributed Optimization, Time-varying networks, Dual methods
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-261053DOI: 10.1109/ICASSP.2019.8683797ISI: 000482554005098Scopus ID: 2-s2.0-85069439146ISBN: 978-1-4799-8131-1 (print)OAI: oai:DiVA.org:kth-261053DiVA, id: diva2:1356667
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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Maros, MarieJaldén, Joakim

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  • apa
  • harvard1
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  • de-DE
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