Bi-alternating direction method of multipliers over graphs

Zhang, G; Heusdens, R

Bi-alternating direction method of multipliers over graphs

Zhang, G

Heusdens, R

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, 2015-August pp. 3571 - 3575
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-4521-542X	en_US
dc.contributor.author	Heusdens, R	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, 2015-August pp. 3571 - 3575	en_US
dc.identifier.isbn	9781467369978	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120326
dc.description.abstract	© 2015 IEEE. In this paper, we extend the bi-alternating direction method of multipliers (BiADMM) designed on a graph of two nodes to a graph of multiple nodes. In particular, we optimize a sum of convex functions defined over a general graph, where every edge carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is carefully constructed which naturally captures the associated graph topology. We show that under both the synchronous and asynchronous updating schemes, the extended BiADMM has the convergence rate of O(1/K) (where K denotes the iteration index) for general closed, proper and convex functions. As an example, we apply the new algorithm for distributed averaging. Experimental results show that the new algorithm remarkably outperforms the state-of-the-art methods.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2015.7178636	en_US
dc.title	Bi-alternating direction method of multipliers over graphs	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-August	en_US
utslib.for	080199 Artificial Intelligence and Image Processing not elsewhere classified	en_US
utslib.for	080599 Distributed Computing not elsewhere classified	en_US
utslib.for	090609 Signal Processing	en_US
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-August	en_US

Abstract:

© 2015 IEEE. In this paper, we extend the bi-alternating direction method of multipliers (BiADMM) designed on a graph of two nodes to a graph of multiple nodes. In particular, we optimize a sum of convex functions defined over a general graph, where every edge carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is carefully constructed which naturally captures the associated graph topology. We show that under both the synchronous and asynchronous updating schemes, the extended BiADMM has the convergence rate of O(1/K) (where K denotes the iteration index) for general closed, proper and convex functions. As an example, we apply the new algorithm for distributed averaging. Experimental results show that the new algorithm remarkably outperforms the state-of-the-art methods.

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http://hdl.handle.net/10453/120326