Fixed-Time Projection Algorithm for Distributed Constrained Optimization on Time-Varying Digraphs

• 成果类型: Article
• 署名作者: Chen, Gang; Yang, Qing; Song, Yongduan; Lewis, Frank L.
• 署名单位: Chongqing University; University of Texas System; University of Texas Arlington
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3056233
• 发表日期: 2022
• 页码: 390-397
• 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

摘要

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.