A Decentralized Primal-Dual Method for Constrained Minimization of a Strongly Convex Function
نویسندگان
چکیده
We propose decentralized primal-dual methods for cooperative multiagent consensus optimization problems over both static and time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific convex functions conic constraint sets defined by nonlinear functions; hence, optimal decision should lie in intersection these private sets. Under strong convexity assumption, we provide convergence rates suboptimality, infeasibility, violation terms number required; examine effect underlying network topology on rates.
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ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2022
ISSN: ['0018-9286', '1558-2523', '2334-3303']
DOI: https://doi.org/10.1109/tac.2021.3130082