期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 66, 期 3, 页码 1102-1115出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2020.2994024
关键词
Signal processing algorithms; Convex functions; Linear programming; Convergence; Distributed algorithms; Cost function; Asynchronous algorithm; distributed convex optimization; epigraph form; inequality constraints; unbalanced directed networks
资金
- Fundamental Research Funds for the Central Universities [XDJK2019AC001]
- Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province [DRN19011, cx2019005]
- Innovation Support Program for Chongqing Overseas Returnees [cx2019005]
- National Natural Science Foundation of China [61773321]
- QNRF (a member of Qatar Foundation) [NPRP 9-166-1-031]
- Australian Research Council [DP180103217, FT190100156, IH180100020]
- UNSW Digital Grid Futures Institute, UNSW, Sydney, under a Cross Disciplinary Fund Scheme
This article focuses on distributed convex optimization problems over an unbalanced directed multiagent network with inequality constraints, proposing a new distributed asynchronous broadcast-based optimization algorithm to collaboratively solve the optimization problem. Simulation results substantiate the feasibility of the algorithm and validate the theoretical findings.
This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. Furthermore, we particularly discuss the scenario in which the interactions among agents over the whole network are subjected to possible link failures. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures. Under two standard assumptions that the communication network is directly strongly connected and the subgradients of all local objective functions are bounded, we provide an explicit analysis for convergence of the algorithm. Simulation results obtained by three numerical experiments substantiate the feasibility of the algorithm and validate the theoretical findings.
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