Distributed Online Stochastic-Constrained Convex Optimization With Bandit Feedback

This article studies the distributed online stochastic convex optimization problem with the time-varying constraint over a multiagent system constructed by various agents. The sequences of cost functions and constraint functions, both of which have dynamic parameters following time-varying distribut...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2024-01, Vol.54 (1), p.63-75
Main Authors: Wang, Cong, Xu, Shengyuan, Yuan, Deming
Format: Article
Language:English
Subjects:
Algorithms
bandit feedback
Constraints
Convex analysis
Convex functions
Convexity
Cost function
Costs
Distributed online stochastic optimization
Feedback
Heuristic algorithms
Iterative methods
Multi-agent systems
Multiagent systems
Optimization
Regularization
Sequences
time-varying constraint
Time-varying systems
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Summary:This article studies the distributed online stochastic convex optimization problem with the time-varying constraint over a multiagent system constructed by various agents. The sequences of cost functions and constraint functions, both of which have dynamic parameters following time-varying distributions, are unacquainted to the agent ahead of time. Agents in the network are able to interact with their neighbors through a sequence of strongly connected and time-varying graphs. We develop the adaptive distributed bandit primal-dual algorithm whose step size and regularization sequences are adaptive and have no prior knowledge about the total iteration span T . The adaptive distributed bandit primal-dual algorithm applies bandit feedback with a one-point or two-point gradient estimator to evaluate gradient values. It is illustrated in this article that if the drift of the benchmark sequence is sublinear, then the adaptive distributed bandit primal-dual algorithm exhibits sublinear expected dynamic regret and constraint violation using both two kinds of gradient estimator to compute gradient information. We present a numerical experiment to show the performance of the proposed method.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2022.3177644