A neurodynamic approach to distributed optimization with globally coupled constraints

Le, X; Chen, S; Yan, Z; Xi, J

A neurodynamic approach to distributed optimization with globally coupled constraints

Le, X Chen, S Yan, Z

Xi, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2018, 48 (11), pp. 3149 - 3158
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Le, X	en_US
dc.contributor.author	Chen, S	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Xi, J	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2018, 48 (11), pp. 3149 - 3158	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128145
dc.description.abstract	© 2013 IEEE. In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2017.2760908	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A neurodynamic approach to distributed optimization with globally coupled constraints	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	48	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2013 IEEE. In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

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http://hdl.handle.net/10453/128145