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Distributed constrained optimization with periodic dynamic quantization
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DOI：10.1016/j.automatica.2023.111364.png)
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Abstract
En 中文
This paper uses the mirror descent algorithm with periodic dynamic quantization to solve constrained distributed optimization problems with limited communication channels. Due to the imperfect network environment, obtaining accurate information is impractical, and thus a communication scheme under quantization needs to be considered. A periodic dynamic quantizer with finite quantization levels is proposed in this paper to achieve exact optimization. Moreover, a time -varying control parameter in the mirror descent algorithm is designed to control the quantization error. After a comprehensive analysis, the proposed algorithm can obtain an optimal value, and the optimal convergence rate is O(1/T0.25). (c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Distributed optimization
Mirror descent algorithm
Periodic dynamic quantization
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5.9
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1.2W
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