期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 62, 期 8, 页码 4259-4265出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2681200
关键词
Constrained consensus; convergence; distributed optimization; multiagent network
资金
- National Natural Science Foundation of China [61473333, 61573344]
In this technical note, we are concerned with constrained consensus algorithms for distributed convex optimization with a sum of convex objective functions subject to local bound and equality constraints. In multiagent networks, each agent has its own data on objective function and constraints. All the agents cooperatively find the minimizer, while each agent can only communicate with its neighbors. The consensus of multiagent networks with time-invariant and undirected graphs is proven by the Lyapunov method. Compared with existing consensus algorithms for distributed optimization with diminishing step sizes, the proposed algorithms with fixed step size have better convergence rate. Simulation results on a numerical example are presented to substantiate the performance and characteristics of the proposed algorithms.
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