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Distributed online bandit optimization under random quantization.
- Source :
-
Automatica . Dec2022, Vol. 146, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- This paper considers the problem of solving distributed online optimization over a network that consists of multiple interacting nodes. Each node in the network is endowed with a sequence of loss functions, each of which is revealed to the node after a decision has been committed. The goal of the network is to minimize the cumulative loss functions of nodes in a distributed fashion, while subject to two types of information constraints, namely, message quantization and bandit feedback. To this end, a quantized distributed online bandit optimization algorithm is proposed by adopting random quantization operation and one-point gradient estimator. We show the convergence of the algorithm by establishing an O (d T 3 / 4) regret bound, where d is the dimension of states and T is the total number of rounds. Finally, an online distributed quadratic programming problem is investigated to validate the theoretical findings of the paper. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00051098
- Volume :
- 146
- Database :
- Academic Search Index
- Journal :
- Automatica
- Publication Type :
- Academic Journal
- Accession number :
- 160170747
- Full Text :
- https://doi.org/10.1016/j.automatica.2022.110590