Distributed ergodic algorithms for mixed equilibrium problems: Absent of cut property

Lu, Kaihong; Zhu, Qiaoming; Yan, Xiaoxi

doi:10.1016/j.automatica.2022.110297

Cited by 4 publications

(3 citation statements)

References 25 publications

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Section: Online Distributed Algorithmsmentioning

confidence: 99%

“…Motivated by the single-point gradient estimate strategy [35] , consensus algorithm [36] , and the auxiliary optimization algorithm [33] , we propose the algorithm (9). The consensus term z i (t) is inspired by the consensus algorithm in [10] and [36]. Under this algorithm, each agent updates actions according to its own state, the state information received from its neighbors at the current moment and the information of gradient estimation.…”

Section: Online Distributed Algorithmsmentioning

confidence: 99%

“…In recent years, distributed optimization has attracted extensive attention in various fields [1][2][3][4][5] . This is due to its wide range of practicability in numerous areas such as distributed coordinated control [6] , power grid economic dispatch [7] and internet of things applications [8] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Zeroth-Order Methods for Online Distributed Optimization with Strongly Pseudoconvex Cost Functions

YAN,

MA,

2024

系统科学与信息学报(英文)

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This paper studies an online distributed optimization problem over multi-agent systems. In this problem, the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions. Different from most existing works on distributed optimization, here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available. To handle this problem, an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed. Under the algorithm, each agent only has access to the information associated with its own cost function and the estimate of the gradient, and exchange local state information with its immediate neighbors via a time-varying digraph. The performance of the algorithm is measured by the expectation of dynamic regret. Under mild assumptions on graphs, we prove that if the cumulative deviation of minimizer sequence grows within a certain rate, then the expectation of dynamic regret grows sublinearly. Finally, a simulation example is given to illustrate the validity of our results.

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