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Distributed solution for a Maximum Variance Unfolding Problem with sensor and robotic network applications
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0001-7309-8086
2012 (English)In: Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on, IEEE , 2012, 63-70 p.Conference paper, Published paper (Refereed)
Abstract [en]

We focus on a particular non-convex networked optimization problem, known as the Maximum Variance Unfolding problem and its dual, the Fastest Mixing Markov Process problem. These problems are of relevance for sensor networks and robotic applications. We propose to solve both these problems with the same distributed primal-dual subgradient iterations whose convergence is proven even in the case of approximation errors in the calculation of the subgradients. Furthermore, we illustrate the use of the algorithm for sensor network applications, such as localization problems, and for mobile robotic networks applications, such as dispersion problems.

Place, publisher, year, edition, pages
IEEE , 2012. 63-70 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-121262DOI: 10.1109/Allerton.2012.6483200Scopus ID: 2-s2.0-84875742420ISBN: 978-146734538-5 (print)OAI: oai:DiVA.org:kth-121262DiVA: diva2:617662
Conference
2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012, 1 October 2012 through 5 October 2012, Monticello, IL
Note

QC 20130424

Available from: 2013-04-24 Created: 2013-04-24 Last updated: 2013-04-24Bibliographically approved

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Dimarogonas, Dimos V.

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CiteExportLink to record
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Citation style
  • apa
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Language
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  • nn-NB
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  • Other locale
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Output format
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