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Distributed decision-coupled constrained optimization via Proximal-Tracking
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DOI：10.1016/j.automatica.2021.109938.png)
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Abstract
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In this paper we deal with decision-coupled problems involving multiple agents over a network. Each agent has its own local objective function and local constraints, and all agents aim at finding the value of a common decision vector that minimizes the sum of all agents' cost functions and satisfies all local constraints. To this purpose, we introduce a Proximal-Tracking distributed optimization algorithm that integrates dynamic average consensus within the proximal minimization method. Convergence to an optimal consensus solution is guaranteed for any value of a constant penalty parameter, under a convexity assumption only, without requiring differentiability, Lipschitz continuity, or smoothness of the local objective functions. Numerical simulations show the effectiveness of the proposed scheme. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
\ Distributed optimization
Decision-coupled optimization
Proximal algorithm
Gradient-tracking
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