期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 5, 页码 2552-2559出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3079192
关键词
Optimization; Convex functions; Distributed algorithms; Convergence; Linear programming; Multi-agent systems; Graph theory; Distributed optimization; finite-time consensus; general constraints; primal-dual algorithm
资金
- National Natural Science Foundation of China [62073076]
- General Joint Fund of the Equipment Advance Research Program of Ministry of Education [6141A020223]
- Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [BM2017002]
- Australian Research Council [DP120104986]
This article studies convex optimization problems with general constraints and proposes a distributed algorithm to solve the problem. The optimality condition of the optimization problem is developed using saddle point theory, and a continuous-time primal-dual algorithm is constructed accordingly.
This article studies the convex optimization problem with general constraints, where its global objective function is composed of the sum of local objective functions. The objective is to design a distributed algorithm to cooperatively resolve the optimization problem under the condition that only the information of each node's own local cost function and its neighbors' states can be obtained. To this end, the optimality condition of the researched optimization problem is developed in terms of the saddle point theory. On this basis, the corresponding continuous-time primal-dual algorithm is constructed for the considered constrained convex optimization problem under time-varying undirected and connected graphs. In the case that the parameters involved in the proposed algorithm satisfy certain inequality, the states of all nodes will reach consensus in finite time. Meanwhile, the average state is globally convergent to the optimal solution of the considered optimization problem under some mild and standard assumptions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据