An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the...

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Veröffentlicht in:Journal of the Franklin Institute Jg. 359; H. 8; S. 3692 - 3716
Hauptverfasser: Jia, Wenwen, Qin, Sitian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elmsford Elsevier Ltd 01.05.2022
Elsevier Science Ltd
Schlagworte:
Adaptive algorithms
Algorithms
Computational geometry
Constraints
Continuity (mathematics)
Convex analysis
Convexity
Energy consumption
Multiagent systems
Numerical analysis
Optimization
Penalty function
ISSN:0016-0032, 1879-2693, 0016-0032
Online-Zugang:Volltext
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Zusammenfassung:This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2022.03.046