Distributed nonlinear consensus in the space of probability measures

Bishop, AN; Doucet, A

Distributed nonlinear consensus in the space of probability measures

Bishop, AN

Doucet, A

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Publication Type:: Conference Proceeding
Citation:: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 8662 - 8668
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.contributor.author	Doucet, A	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 8662 - 8668	en_US
dc.identifier.isbn	9783902823625	en_US
dc.identifier.issn	1474-6670	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33026
dc.description.abstract	© IFAC. Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120102873
dc.relation.ispartof	IFAC Proceedings Volumes (IFAC-PapersOnline)	en_US
dc.title	Distributed nonlinear consensus in the space of probability measures	en_US
dc.type	Conference Proceeding
utslib.citation.volume	19	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Cape Town, South Africa
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© IFAC. Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.

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http://hdl.handle.net/10453/33026