Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks
2017
Source Publication2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
ISSN2334-3303
VolumePP
Issue99
Pages4629 - 4636
Status已发表
DOI10.1109/TAC.2019.2901829
Abstract

To date, a large collection of distributed algorithms for convex multi-agent optimization have been reported, yet only few of them converge to an optimal solution at guaranteed rates when the topologies of the agent networks are time-varying. Motivated by this, we develop a family of distributed Fenchel dual gradient methods for solving strongly convex yet non-smooth multi-agent optimization problems with nonidentical local constraints over time-varying networks. The proposed algorithms are constructed based on the application of weighted gradient methods to the Fenchel dual of the multiagent optimization problem. They are able to drive all the agents to dual optimality at an O(1/k) rate and to primal optimality at an O(1/root k) rate under a standard network connectivity condition. The competent convergence performance of the Fenchel dual gradient methods is demonstrated via numerical examples.

KeywordGradient methods Convergence Convex functions Standards Linear programming
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
Conference PlaceMelbourne, VIC
Conference Date12-15 Dec. 2017
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Indexed BySCI ; EI ; CPCI ; SCIE
Language英语
Funding ProjectNatural Science Foundation of Shanghai[16ZR1422500]
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000424696902127
PublisherIEEE
EI Accession Number20181805132494
EI KeywordsConvergence of numerical methods ; Convex optimization ; Gradient methods ; Multi agent systems
EI Classification NumberElectric Networks:703.1 ; Numerical Methods:921.6
WOS KeywordMODEL-PREDICTIVE CONTROL ; RESOURCE-ALLOCATION ; 1ST-ORDER METHODS ; DIRECTED-GRAPHS ; ALGORITHM ; CONSENSUS ; DECOMPOSITION
Original Document TypeProceedings Paper
Source DataIEEE
Citation statistics
Document Type会议论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/16323
Collection信息科学与技术学院
信息科学与技术学院_PI研究组_陆疌组
信息科学与技术学院_博士生
Corresponding AuthorJie Lu
Affiliation
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
First Author AffilicationSchool of Information Science and Technology
Corresponding Author AffilicationSchool of Information Science and Technology
First Signature AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Xuyang Wu,Jie Lu. Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:4629 - 4636.
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