On the convergence rate of the bi-alternating direction method of multipliers

Zhang, G; Heusdens, R; Kleijn, WB

On the convergence rate of the bi-alternating direction method of multipliers

Zhang, G

Heusdens, R Kleijn, WB

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2014, pp. 3869 - 3873
Issue Date:: 2014-01-01

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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.contributor.author	Kleijn, WB	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2014, pp. 3869 - 3873	en_US
dc.identifier.isbn	9781479928927	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120084
dc.description.abstract	In this paper, we analyze the convergence rate of the bi-alternating direction method of multipliers (BiADMM). Differently from ADMM that optimizes an augmented Lagrangian function, Bi-ADMM optimizes an augmented primal-dual Lagrangian function. The new function involves both the objective functions and their conjugates, thus incorporating more information of the objective functions than the augmented Lagrangian used in ADMM. We show that BiADMM has a convergence rate of O(K-1) (K denotes the number of iterations) for general convex functions. We consider the lasso problem as an example application. Our experimetal results show that BiADMM outperforms not only ADMM, but fast-ADMM as well. © 2014 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.2014.6854326	en_US
dc.title	On the convergence rate of the bi-alternating direction method of multipliers	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 analyze the convergence rate of the bi-alternating direction method of multipliers (BiADMM). Differently from ADMM that optimizes an augmented Lagrangian function, Bi-ADMM optimizes an augmented primal-dual Lagrangian function. The new function involves both the objective functions and their conjugates, thus incorporating more information of the objective functions than the augmented Lagrangian used in ADMM. We show that BiADMM has a convergence rate of O(K-1) (K denotes the number of iterations) for general convex functions. We consider the lasso problem as an example application. Our experimetal results show that BiADMM outperforms not only ADMM, but fast-ADMM as well. © 2014 IEEE.

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http://hdl.handle.net/10453/120084