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• W3209734918 abstract "Distributed primal-dual methods have been widely used for solving large-scale constrained optimization problems. The majority of existing results focus on the problems with decoupled constraints. Some recent works have studied the problems subject to separable globally coupled constraints. This paper considers the distributed optimization problems with globally coupled constraints over networks without requiring the separability of the globally coupled constraints. This is made possible by the local estimates of the constraint violations. For solving such a problem, we propose a primal-dual algorithm in the augmented Lagrangian framework, combining the average consensus technique. We first establish a non-ergodic convergence rate of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$mathcal {O}(1/k)$</tex-math></inline-formula> in terms of the objective residual for solving a distributed constrained convex optimization problem, where <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$k$</tex-math></inline-formula> is the iteration counter. Specifically, the global objective function is the aggregate of the local convex and possibly non-smooth costs, and the coupled constraint is the sum of the local linear equality constraints. The numerical results illustrate the performance of the proposed method." @default.
• W3209734918 created "2021-11-08" @default.
• W3209734918 creator A5019248683 @default.
• W3209734918 creator A5027163111 @default.
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• W3209734918 date "2022-01-01" @default.
• W3209734918 modified "2023-10-14" @default.
• W3209734918 title "Distributed Primal-Dual Method for Convex Optimization With Coupled Constraints" @default.
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• W3209734918 doi "https://doi.org/10.1109/tsp.2021.3123888" @default.
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