Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework
In this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly spee...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9075189/ |
| _version_ | 1828177451932450816 |
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| author | Towfiq Rahman Zhihua Qu Toru Namerikawa |
| author_facet | Towfiq Rahman Zhihua Qu Toru Namerikawa |
| author_sort | Towfiq Rahman |
| collection | DOAJ |
| description | In this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly speed up convergence of ADMM-based distributed optimization. Using Lyapunov direct approach, the proposed solution embeds control gains into weighted network matrix among the agents and uses those weights as adaptive penalty gains in the augmented Lagrangian. It is shown that the proposed closed loop gain adaptation scheme significantly improves the convergence time of underlying ADMM optimization. Convergence analysis is provided and simulation results are included to demonstrate the effectiveness of the proposed scheme. |
| first_indexed | 2024-04-12T04:57:47Z |
| format | Article |
| id | doaj.art-3c2f032a64f9414dae2e55cfb45594cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:57:47Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3c2f032a64f9414dae2e55cfb45594cc2022-12-22T03:47:03ZengIEEEIEEE Access2169-35362020-01-018804808048910.1109/ACCESS.2020.29894029075189Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM FrameworkTowfiq Rahman0https://orcid.org/0000-0003-4194-3980Zhihua Qu1Toru Namerikawa2Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USADepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USADepartment of System Design Engineering, Keio University, Kanagawa, JapanIn this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly speed up convergence of ADMM-based distributed optimization. Using Lyapunov direct approach, the proposed solution embeds control gains into weighted network matrix among the agents and uses those weights as adaptive penalty gains in the augmented Lagrangian. It is shown that the proposed closed loop gain adaptation scheme significantly improves the convergence time of underlying ADMM optimization. Convergence analysis is provided and simulation results are included to demonstrate the effectiveness of the proposed scheme.https://ieeexplore.ieee.org/document/9075189/Distributed optimizationADMMgain adaptationrate of convergenceLyapunov direct method |
| spellingShingle | Towfiq Rahman Zhihua Qu Toru Namerikawa Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework IEEE Access Distributed optimization ADMM gain adaptation rate of convergence Lyapunov direct method |
| title | Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework |
| title_full | Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework |
| title_fullStr | Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework |
| title_full_unstemmed | Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework |
| title_short | Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework |
| title_sort | improving rate of convergence via gain adaptation in multi agent distributed admm framework |
| topic | Distributed optimization ADMM gain adaptation rate of convergence Lyapunov direct method |
| url | https://ieeexplore.ieee.org/document/9075189/ |
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