Generalized linear coordinate-descent message-passing for convex optimization

Zhang, G; Heusdens, R

Generalized linear coordinate-descent message-passing for convex optimization

Zhang, G

Heusdens, R

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2012, pp. 2009 - 2012
Issue Date:: 2012-10-23

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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	2012-10-23	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2012, pp. 2009 - 2012	en_US
dc.identifier.isbn	9781467300469	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120329
dc.description.abstract	In this paper we propose a generalized linear coordinate-descent (GLiCD) algorithm for a class of unconstrained convex optimization problems. The considered objective function can be decomposed into edge-functions and node-functions of a graphical model. The messages of the GLiCD algorithm are in a form of linear functions, as compared to the min-sum algorithm of which the form of messages depends on the objective function. Thus, the implementation of the GLiCD algorithm is much simpler than that of the min-sum algorithm. A theorem is stated according to which the algorithm converges to the optimal solution if the objective function satisfies a diagonal-dominant condition. As an application, the GLiCD algorithm is exploited in solving the averaging problem in sensor networks, where the performance is compared to that of the min-sum algorithm. © 2012 IEEE.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2012.6288302	en_US
dc.title	Generalized linear coordinate-descent message-passing for convex optimization	en_US
dc.type	Conference Proceeding
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

Abstract:

In this paper we propose a generalized linear coordinate-descent (GLiCD) algorithm for a class of unconstrained convex optimization problems. The considered objective function can be decomposed into edge-functions and node-functions of a graphical model. The messages of the GLiCD algorithm are in a form of linear functions, as compared to the min-sum algorithm of which the form of messages depends on the objective function. Thus, the implementation of the GLiCD algorithm is much simpler than that of the min-sum algorithm. A theorem is stated according to which the algorithm converges to the optimal solution if the objective function satisfies a diagonal-dominant condition. As an application, the GLiCD algorithm is exploited in solving the averaging problem in sensor networks, where the performance is compared to that of the min-sum algorithm. © 2012 IEEE.

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http://hdl.handle.net/10453/120329