Primal-Dual Algorithm for Distributed Optimization with Coupled Constraints

This paper focuses on distributed consensus optimization problems with coupled constraints over time-varying multi-agent networks, where the global objective is the finite sum of all agents’ private local objective functions, and decision variables of agents are subject to coupled equality and inequ...

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Vydáno v:Journal of optimization theory and applications Ročník 201; číslo 1; s. 252 - 279
Hlavní autoři: Gong, Kai, Zhang, Liwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2024
Springer Nature B.V
Témata:
Algorithms
Applications of Mathematics
Approximation
Calculus of Variations and Optimal Control; Optimization
Constraints
Convergence
Decomposition
Engineering
Machine learning
Mathematics
Mathematics and Statistics
Methods
Multiagent systems
Operations Research/Decision Theory
Optimization
Theory of Computation
Variables
Distributed optimization
Time-varying networks
Coupled constraints
Convergence
ISSN:0022-3239, 1573-2878
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Shrnutí:This paper focuses on distributed consensus optimization problems with coupled constraints over time-varying multi-agent networks, where the global objective is the finite sum of all agents’ private local objective functions, and decision variables of agents are subject to coupled equality and inequality constraints and a compact convex subset. Each agent exchanges information with its neighbors and processes local data. They cooperate to agree on a consensual decision vector that is an optimal solution to the considered optimization problems. We integrate ideas behind dynamic average consensus and primal-dual methods to develop a distributed algorithm and establish its sublinear convergence rate. In numerical simulations, to illustrate the effectiveness of the proposed algorithm, we compare it with some related methods by the Neyman–Pearson classification problem.
Bibliografie:ObjectType-Article-1
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-024-02393-7