Distributed decision-coupled constrained optimization via Proximal-Tracking
| Autor: | Maria Prandini, Alessandro Falsone |
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| Rok vydání: | 2022 |
| Předmět: |
Smoothness
Mathematical optimization Gradient-tracking Computer science Constrained optimization Proximal algorithm Lipschitz continuity Convexity Distributed optimization Computer Science::Multiagent Systems Control and Systems Engineering Convergence (routing) Differentiable function Minification Electrical and Electronic Engineering Constant (mathematics) Decision-coupled optimization |
| Zdroj: | Automatica. 135:109938 |
| ISSN: | 0005-1098 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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