期刊
SYSTEMS & CONTROL LETTERS
卷 96, 期 -, 页码 110-117出版社
ELSEVIER
DOI: 10.1016/j.sysconle.2016.07.009
关键词
Distributed constrained optimization; Primal-dual algorithm; Augmented Lagrange method; Multi-agent network
资金
- NSFC [61273193, 61120106011, 61134013, 61573345]
- 973 program of China [2014CB845301]
- National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences [Y629091ZZ2]
The paper studies a distributed constrained optimization problem, where multiple agents connected in a network collectively minimize the sum of individual objective functions subject to a global constraint being an intersection of the local constraints assigned to the agents. Based on the augmented Lagrange method, a distributed primal-dual algorithm with a projection operation included is proposed to solve the problem. It is shown that with appropriately chosen constant step size,,the local estimates derived at all agents asymptotically reach a consensus at an optimal solution. In addition, the value of the cost function at the time-averaged estimate converges with rate O(1/k) to the optimal value for the unconstrained problem. By these properties, the proposed primal-dual algorithm is distinguished from the existing algorithms for distributed constrained optimization. The theoretical analysis is justified by numerical simulations. (C) 2016 Elsevier B.V. All rights reserved.
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