科研之友
微信
新浪微博
Facebook
分享链接
摘要
Online distributed optimization of multi-agent systems is often used to deal with the optimization problems in dynamic environments, and real-time data streams need to be transmitted between nodes. In many cases, each node cannot obtain all the information of the individual objective including gradient information), and there are communication constraints in the transmission of information between nodes. In this paper, considering the advantages of the mirror descent algorithm in the sense of non-Euclidean projection in processing high-dimensional data and large-scale online learning, the function value information of the individual objective function at two points is used to estimate the missing gradient information, and an adaptive quantizer is designed according to the property of mirror descent algorithm, and an adaptive quantized distributed online mirror descent algorithm based on the bandit feedback is proposed. Then the relationship between the quantization error bound and the regret bound is analyzed. The regret bound of the proposed algorithm can be obtained as O(√T) when the parameters are chosen appropriately. Finally, the effectiveness of the algorithm and theoretical results is verified by numerical simulations. ? 2023 South China University of Technology.
- 单位
