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Distributed event-triggered algorithm for convex optimization with coupled constraints
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DOI：10.1016/j.automatica.2024.111877.png)
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Abstract
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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. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keywords:
Distributed optimization
Event-triggered communication
Constant step-sizes
Coupled constraints
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