期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 1, 页码 390-397出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3056233
关键词
Linear programming; Distributed algorithms; Cost function; Convex functions; Convergence; Indexes; Time factors; Continuous-time subgradient algorithm; distributed optimization; fixed-time projection; time-varying digraph
资金
- National Natural Science Foundation of China [62073048, 61860206008, 61773081, 61833013, 61933012, 61991403, 61991400]
- Natural Science Foundation of Chongqing [cstc2020jcyj-msxmX0264]
- Fundamental Research Funds for the Central Universities [2020CDJ-LHZZ-001]
- Zhejiang Lab [2019NB0AB06]
This article studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multiagent network. A penalty-based distributed continuous-time subgradient algorithm is developed for each agent to seek the saddle point of the penalty Lagrangian function. The algorithm can obtain an exact primal optimal solution, and ensures that each local state estimate converges to its convex constraint set within fixed time.
This article studies the distributed convex optimization problem with a common decision variable, a global inequality constraint, and local constraint sets over a time-varying multiagent network, the objective function of which is a sum of agents' local convex cost functions. To solve such problem, a penalty-based distributed continuous-time subgradient algorithm with time-varying gain is developed for each agent to seek the saddle point of the penalty Lagrangian function. It is shown that an exact primal optimal solution can be obtained with certain assumption on time-varying gain. Moreover, the proposed algorithm adopts fixed-time projection scheme to ensure that for any initial state value, each local state estimate converges to its convex constraint set within fixed time. Finally, numerical examples are provided to show the effectiveness of the theoretical results.
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