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Distributed Proximal Algorithms for Multi-Agent Optimization with Coupled Inequality Constraints
[article]
2020
arXiv
pre-print
This paper 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 (DPPD) algorithm, is proposed based on the celebrated
arXiv:1907.03245v2
fatcat:g6cinqfypbfv7ie2uvikij4kaa