期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 66, 期 3, 页码 1223-1230出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2020.2989282
关键词
Coupled inequality constraints; distributed optimization; multiagent networks; proximal point algorithm (PPA)
资金
- Research Grants Council of Hong Kong [CityU-11207817]
- Ministry of Education of Singapore [MoE Tier 1 RG72/19]
This article addresses distributed optimization problems over directed and time-varying networks, proposing a distributed proximal-based algorithm to solve the problem. The algorithm is shown to lead to the global optimal solution with a general step size, and the saddle-point running evaluation error vanishes proportionally to O(1/root k) with iteration number k. A simulation example is presented to demonstrate the effectiveness of the algorithm.
This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to O(1/root k), where k > 0 is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.
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