Convergence of generalized linear coordinate-descent message-passing for quadratic optimization

Zhang, G; Heusdens, R

Convergence of generalized linear coordinate-descent message-passing for quadratic optimization

Zhang, G

Heusdens, R

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE International Symposium on Information Theory - Proceedings, 2012, pp. 1997 - 2001
Issue Date:: 2012-10-22

Closed Access

	Filename	Description	Size
	converge.pdf	Published version	134.19 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2012-10-22	en_US
dc.identifier.citation	IEEE International Symposium on Information Theory - Proceedings, 2012, pp. 1997 - 2001	en_US
dc.identifier.isbn	9781467325790	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119618
dc.description.abstract	We study the generalized linear coordinate-descent (GLiCD) algorithm for the quadratic optimization problem. As an extension of the linear coordinate-descent (LiCD) algorithm, the GLiCD algorithm incorporates feedback from last iteration in generating new messages. We show that if the amount of feedback signal from last iteration is above a threshold and the GLiCD algorithm converges, it computes the optimal solution. Based on the result, we further show that if the feedback signal is large enough, the GLiCD algorithm is guaranteed to converge. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE International Symposium on Information Theory - Proceedings	en_US
dc.relation.isbasedon	10.1109/ISIT.2012.6283649	en_US
dc.title	Convergence of generalized linear coordinate-descent message-passing for quadratic 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:

We study the generalized linear coordinate-descent (GLiCD) algorithm for the quadratic optimization problem. As an extension of the linear coordinate-descent (LiCD) algorithm, the GLiCD algorithm incorporates feedback from last iteration in generating new messages. We show that if the amount of feedback signal from last iteration is above a threshold and the GLiCD algorithm converges, it computes the optimal solution. Based on the result, we further show that if the feedback signal is large enough, the GLiCD algorithm is guaranteed to converge. © 2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119618