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A Regularized Saddle-Point Algorithm for Networked Optimization with Resource Allocation Constraints
Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 2, NL-2628 CD Delft, Netherlands..
Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 2, NL-2628 CD Delft, Netherlands..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. Royal Inst Technol KTH, Sch Elect Engn, ACCESS Linnaeus Ctr, S-10044 Stockholm, Sweden..
2012 (English)In: 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2012, p. 7476-7481Conference paper, Published paper (Refereed)
Abstract [en]

We propose a regularized saddle-point algorithm for convex networked optimization problems with resource allocation constraints. Standard distributed gradient methods suffer from slow convergence and require excessive communication when applied to problems of this type. Our approach offers an alternative way to address these problems, and ensures that each iterative update step satisfies the resource allocation constraints. We derive step-size conditions under which the distributed algorithm converges geometrically to the regularized optimal value, and show how these conditions are affected by the underlying network topology. We illustrate our method on a robotic network application example where a group of mobile agents strive to maintain a moving target in the barycenter of their positions.

Place, publisher, year, edition, pages
IEEE , 2012. p. 7476-7481
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-245326ISI: 000434483700028ISBN: 978-1-4673-2066-5 (print)OAI: oai:DiVA.org:kth-245326DiVA, id: diva2:1295642
Conference
51st IEEE Annual Conference on Decision and Control (CDC), DEC 10-13, 2012, HI
Note

QC 20190312

Available from: 2019-03-12 Created: 2019-03-12 Last updated: 2019-03-12Bibliographically approved

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Johansson, Mikael

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