Distributed event-triggered algorithm for convex optimization with coupled constraints

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes...

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Veröffentlicht in:Automatica (Oxford) Jg. 170; S. 111877
Hauptverfasser: Huang, Yi, Zeng, Xianlin, Sun, Jian, Meng, Ziyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2024
Schlagworte:
Constant step-sizes
Coupled constraints
Distributed optimization
Event-triggered communication
Constant step-sizes
Distributed optimization
Event-triggered communication
Coupled constraints
ISSN:0005-1098
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Zusammenfassung:This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of O(1/k) for general convex objective functions, where k>0 is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.
ISSN:0005-1098
DOI:10.1016/j.automatica.2024.111877