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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| Published in: | Journal of the Franklin Institute Vol. 359; no. 8; pp. 3692 - 3716 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elmsford
Elsevier Ltd
01.05.2022
Elsevier Science Ltd |
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| ISSN: | 0016-0032, 1879-2693, 0016-0032 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Qin, Sitian Jia, Wenwen |
| Author_xml | – sequence: 1 givenname: Wenwen surname: Jia fullname: Jia, Wenwen – sequence: 2 givenname: Sitian orcidid: 0000-0002-4543-4940 surname: Qin fullname: Qin, Sitian email: qinsitian@hitwh.edu.cn |
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| SubjectTerms | Adaptive algorithms Algorithms Computational geometry Constraints Continuity (mathematics) Convex analysis Convexity Energy consumption Multiagent systems Numerical analysis Optimization Penalty function |
| Title | An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization |
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