A distributed optimization algorithm with guaranteed optimality subject to lossy information-sharing over directed networks
This paper addresses the distributed optimization problem subject to lossy information-sharing. In the setting, each agent is assumed to have an individual cost function that is strongly convex and smooth. The goal of agents is to cooperatively minimize the sum of the local cost functions associated...
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| Vydáno v: | Journal of the Franklin Institute Ročník 362; číslo 16; s. 107865 |
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| Jazyk: | angličtina |
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Elsevier Inc
15.10.2025
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| ISSN: | 0016-0032 |
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| Abstract | This paper addresses the distributed optimization problem subject to lossy information-sharing. In the setting, each agent is assumed to have an individual cost function that is strongly convex and smooth. The goal of agents is to cooperatively minimize the sum of the local cost functions associated with each agent through information-sharing over unbalanced directed networks. Unfortunately, inherent additive noise in communication networks makes the diffused information no longer accurate. Such lossy information-sharing poses a fundamental challenge to the cooperation mechanism among agents, and it destroys the process of seeking the optimal solution if not adequately regarded. Inspired by the robust gradient tracking strategy, a distributed optimization algorithm with guaranteed optimality is proposed to cope with such difficulty. Also, the heavy-ball momentum and uncoordinated step sizes are integrated into the algorithm to improve convergence and flexibility. After theoretical analysis, the designed algorithm converges almost surely to the optimal solution at a linear rate. Finally, the performance of the proposed approach is verified with numerical simulations. |
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| AbstractList | This paper addresses the distributed optimization problem subject to lossy information-sharing. In the setting, each agent is assumed to have an individual cost function that is strongly convex and smooth. The goal of agents is to cooperatively minimize the sum of the local cost functions associated with each agent through information-sharing over unbalanced directed networks. Unfortunately, inherent additive noise in communication networks makes the diffused information no longer accurate. Such lossy information-sharing poses a fundamental challenge to the cooperation mechanism among agents, and it destroys the process of seeking the optimal solution if not adequately regarded. Inspired by the robust gradient tracking strategy, a distributed optimization algorithm with guaranteed optimality is proposed to cope with such difficulty. Also, the heavy-ball momentum and uncoordinated step sizes are integrated into the algorithm to improve convergence and flexibility. After theoretical analysis, the designed algorithm converges almost surely to the optimal solution at a linear rate. Finally, the performance of the proposed approach is verified with numerical simulations. |
| ArticleNumber | 107865 |
| Author | Liu, Shuai Wang, Dong Chen, Mingfei |
| Author_xml | – sequence: 1 givenname: Shuai orcidid: 0000-0001-9992-0146 surname: Liu fullname: Liu, Shuai – sequence: 2 givenname: Dong surname: Wang fullname: Wang, Dong email: dwang@dlut.edu.cn – sequence: 3 givenname: Mingfei surname: Chen fullname: Chen, Mingfei |
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| Cites_doi | 10.1109/TSMC.2019.2960770 10.1109/JSTSP.2011.2118740 10.1109/TAC.2017.2737582 10.1109/TAC.2019.2942513 10.1109/TAC.2022.3212006 10.1109/TCYB.2015.2491368 10.1109/JSAC.2021.3118428 10.1016/j.automatica.2024.111925 10.1109/TCYB.2019.2901256 10.1016/j.automatica.2024.111596 10.1016/j.jfranklin.2021.05.024 10.1109/TAC.2008.2009515 10.1109/TCYB.2020.2970454 10.1109/TCYB.2020.2972403 10.1109/TSP.2019.2932876 10.1016/0041-5553(64)90137-5 10.1109/TAC.2020.2972824 10.1109/TFUZZ.2017.2686373 10.1016/j.jfranklin.2023.09.001 10.1016/j.jfranklin.2024.01.010 10.1109/TSP.2008.2007111 10.1137/16M1084316 10.1057/palgrave.jors.2600425 10.1109/TCYB.2021.3127278 10.1016/j.arcontrol.2019.05.006 10.1016/j.jfranklin.2023.08.015 |
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| Keywords | Linear convergence rate Uncoordinated step sizes Lossy information-sharing Distributed algorithm |
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| Snippet | This paper addresses the distributed optimization problem subject to lossy information-sharing. In the setting, each agent is assumed to have an individual... |
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| SubjectTerms | Distributed algorithm Linear convergence rate Lossy information-sharing Uncoordinated step sizes |
| Title | A distributed optimization algorithm with guaranteed optimality subject to lossy information-sharing over directed networks |
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