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Distributed discrete-time convex optimization with nonidentical local constraints over time-varying unbalanced directed graphs

Authors :
Wenwu Yu
Hongzhe Liu
Wei Xing Zheng
Yanan Zhu
Source :
Automatica. 134:109899
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

In this paper, a class of optimization problems is investigated, where the objective function is the sum of N convex functions viewed as local functions and the constraints are N nonidentical closed convex sets . Additionally, it is aimed to solve the considered optimization problem in a distributed manner and thus a sequence of time-varying unbalanced directed graphs is introduced first to depict the information connection topologies. Then, the novel push-sum based constrained optimization algorithm (PSCOA) is developed, where the new gradient descent-like method is applied to settle the involved closed convex set constraints. Furthermore, the rigorous convergence analysis is shown under some standard and common assumptions and it is proved that the developed distributed discrete-time algorithm owns a convergence rate of O ( ln t t ) in general case. Specially, the convergence rate of O ( 1 t ) can be further obtained under the assumption that at least one objective function is strongly convex. Finally, simulation results are given to demonstrate the validity of the theoretical results.

Details

ISSN :
00051098
Volume :
134
Database :
OpenAIRE
Journal :
Automatica
Accession number :
edsair.doi...........b61233a9d5e0406f3956f2640fec404f
Full Text :
https://doi.org/10.1016/j.automatica.2021.109899