2018 IEEE Conference on Decision and Control (CDC) 2018
DOI: 10.1109/cdc.2018.8619377
|View full text |Cite
|
Sign up to set email alerts
|

Distributed Derivative-free Optimization in Large Communication Networks with Sparse Activity

Abstract: This paper addresses a distributed optimization problem in a communication network where nodes are active sporadically. Each active node applies some learning method to control its action to maximize the global utility function, which is defined as the sum of the local utility functions of active nodes.We deal with stochastic optimization problem with the setting that utility functions are disturbed by some non-additive stochastic process. We consider a more challenging situation where the learning method has … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 28 publications
0
1
0
Order By: Relevance
“…In our previous work [11], [10], [12], we considered a different framework where each node controls its own action to perform ZOSCO. The network model was also different: each node has a constant probability to communicate with any other node of the network.…”
Section: Introductionmentioning
confidence: 99%
“…In our previous work [11], [10], [12], we considered a different framework where each node controls its own action to perform ZOSCO. The network model was also different: each node has a constant probability to communicate with any other node of the network.…”
Section: Introductionmentioning
confidence: 99%