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Abstract
This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to O(1/√k), where k > 0 is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.
Original language | English |
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Article number | 9075384 |
Pages (from-to) | 1223-1230 |
Journal | IEEE Transactions on Automatic Control |
Volume | 66 |
Issue number | 3 |
Online published | 21 Apr 2020 |
DOIs | |
Publication status | Published - Mar 2021 |
Research Keywords
- Coupled inequality constraints
- distributed optimization
- multiagent networks
- proximal point algorithm (PPA)
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Dive into the research topics of 'Distributed Proximal Algorithms for Multiagent Optimization with Coupled Inequality Constraints'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Enhancing Quality of Life of Elders with Dementia in Care and Attention Homes through a Facilities Management Model
LEUNG, M.-Y. (Principal Investigator / Project Coordinator), CHONG, M. L. A. (Co-Investigator), Kwok, T.C.-Y. (Co-Investigator) & PYNOOS, J. (Co-Investigator)
1/01/18 → 15/12/22
Project: Research