TY - GEN
T1 - Distributed Proximal Point Algorithm for Constrained Optimization over Unbalanced Graphs
AU - Li, Xiuxian
AU - Feng, Gang
AU - Xie, Lihua
PY - 2019/7
Y1 - 2019/7
N2 - This paper studies the convergence rate for distributed constrained optimization problems over unbalanced time-varying graphs, where the objective function is composed of an aggregate sum of local objective functions which are known to individual agents. In order to deal with the problem, a distributed proximal point algorithm (DPPA) is revisited, which does not necessitate the computation of subgradients, and the convergence is rigorously analyzed under mild assumptions with a class of general stepsizes, i.e., positive, decaying and non-summable. Besides, it is proved that the algorithm converges at the rate of O (1/√ k) in the ergodic sense with respect to the weight-averaged state of all agents, where k > 0 is the iteration number. Moreover, the efficacy of the proposed algorithm is validated by a numerical example. © 2019 IEEE.
AB - This paper studies the convergence rate for distributed constrained optimization problems over unbalanced time-varying graphs, where the objective function is composed of an aggregate sum of local objective functions which are known to individual agents. In order to deal with the problem, a distributed proximal point algorithm (DPPA) is revisited, which does not necessitate the computation of subgradients, and the convergence is rigorously analyzed under mild assumptions with a class of general stepsizes, i.e., positive, decaying and non-summable. Besides, it is proved that the algorithm converges at the rate of O (1/√ k) in the ergodic sense with respect to the weight-averaged state of all agents, where k > 0 is the iteration number. Moreover, the efficacy of the proposed algorithm is validated by a numerical example. © 2019 IEEE.
UR - https://www.scopus.com/pages/publications/85075797015
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85075797015&origin=recordpage
U2 - 10.1109/ICCA.2019.8899938
DO - 10.1109/ICCA.2019.8899938
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781728111650
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 824
EP - 829
BT - 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Control and Automation, ICCA 2019
Y2 - 16 July 2019 through 19 July 2019
ER -