Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems

Summary This article studies the distributed nonsmooth convex optimization problems for second‐order multiagent systems. The objective function is the summation of local cost functions which are convex but nonsmooth. Each agent only knows its local cost function, local constraint set, and neighbor i...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 30; H. 17; S. 7574 - 7592
Hauptverfasser: Wang, Qing, Chen, Jie, Zeng, Xianlin, Xin, Bin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 25.11.2020
Schlagworte:
Algorithms
Computational geometry
Convex analysis
Convexity
Cost function
Multiagent systems
nonsmooth convex optimization
Operators (mathematics)
optimal consensus
Optimization
proximal‐gradient algorithm
Resource allocation
ISSN:1049-8923, 1099-1239
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Zusammenfassung:Summary This article studies the distributed nonsmooth convex optimization problems for second‐order multiagent systems. The objective function is the summation of local cost functions which are convex but nonsmooth. Each agent only knows its local cost function, local constraint set, and neighbor information. By virtue of proximal operator and Lagrangian methods, novel continuous‐time distributed proximal‐gradient algorithms with derivative feedback are proposed to solve the nonsmooth convex optimization for the consensus and resource allocation of multiagent systems, respectively. With the proposed algorithms, both the consensus and resource allocation problems are solved. Moreover, the system can converge to the optimal solution. Finally, simulation examples are given to illustrate the effectiveness of the proposed algorithms.
Bibliographie:Funding information
Consulting Research Project of the Chinese Academy of Engineering, 2019‐XZ‐7; National Natural Science Foundation of China, 61673058; 61603378; 61720106011; 61822304; Peng Cheng Laboratory
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5199