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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Bibliographic Details
Published in:	Automatica (Oxford) Vol. 170; p. 111877
Main Authors:	Huang, Yi, Zeng, Xianlin, Sun, Jian, Meng, Ziyang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2024
Subjects:	Constant step-sizes Coupled constraints Distributed optimization Event-triggered communication Constant step-sizes Distributed optimization Event-triggered communication Coupled constraints
ISSN:	0005-1098
Online Access:	Get full text
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Summary:	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