On simplifying the primal-dual method of multipliers

Zhang, G; Heusdens, R

On simplifying the primal-dual method of multipliers

Zhang, G

Heusdens, R

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2016, 2016-May pp. 4826 - 4830
Issue Date:: 2016-05-18

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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	2016-05-18	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2016, 2016-May pp. 4826 - 4830	en_US
dc.identifier.isbn	9781479999880	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106054
dc.description.abstract	© 2016 IEEE. Recently, the primal-dual method of multipliers (PDMM) has been proposed to solve a convex optimization problem defined over a general graph. In this paper, we consider simplifying PDMM for a subclass of the convex optimization problems. This subclass includes the consensus problem as a special form. By using algebra, we show that the update expressions of PDMM can be simplified significantly. We then evaluate PDMM for training a support vector machine (SVM). The experimental results indicate that PDMM converges considerably faster than the alternating direction method of multipliers (ADMM).	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2016.7472594	en_US
dc.title	On simplifying the primal-dual method of multipliers	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-May	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	2016-May	en_US

Abstract:

© 2016 IEEE. Recently, the primal-dual method of multipliers (PDMM) has been proposed to solve a convex optimization problem defined over a general graph. In this paper, we consider simplifying PDMM for a subclass of the convex optimization problems. This subclass includes the consensus problem as a special form. By using algebra, we show that the update expressions of PDMM can be simplified significantly. We then evaluate PDMM for training a support vector machine (SVM). The experimental results indicate that PDMM converges considerably faster than the alternating direction method of multipliers (ADMM).

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http://hdl.handle.net/10453/106054