KFU. Card publication. Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency.

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FEDERAL UNIVERSITY

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MOMENTUM-BASED DISTRIBUTED RESOURCE SCHEDULING OPTIMIZATION SUBJECT TO SECTOR-BOUND NONLINEARITY AND LATENCY.

Form of presentation

Articles in international journals and collections

Year of publication

2025

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Authors, employees of KFU

Gabidullina Zulfiya Ravilevna, author

Other authors

Doostmohammadian Mohammadreza , author

Rabiee Hamid Reza, author

Bibliographic description in the original language

Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee, Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency. arXiv reprint arXiv:2503.06167 https://arxiv.org/pdf/2503.06167 Submitted on 8 Mar 2025 Subjects: Systems and Control; Distributed, Parallel, and Cluster Computing; Multiagent Systems; Signal Processing; Optimization and Control https://doi.org/10.48550/arXiv.2503.06167

Annotation

arxiv.org

Keywords

Distributed constrained optimization, consensus, graph theory,,convex analysis, resource scheduling and allocation

The name of the journal

arxiv.org

URL

https://arxiv.org/pdf/2503.06167

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=311232&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gabidullina Zulfiya Ravilevna	ru_RU
dc.contributor.author	Doostmohammadian Mohammadreza	ru_RU
dc.contributor.author	Rabiee Hamid Reza	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee, Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency. arXiv reprint arXiv:2503.06167 https://arxiv.org/pdf/2503.06167 Submitted on 8 Mar 2025 Subjects: Systems and Control; Distributed, Parallel, and Cluster Computing; Multiagent Systems; Signal Processing; Optimization and Control https://doi.org/10.48550/arXiv.2503.06167	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=311232&p_lang=2	ru_RU
dc.description.abstract	arxiv.org	ru_RU
dc.description.abstract	This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an auxiliary variable to add momentum towards the optimal state. We prove that this solution is all-time feasible, implying that the coupling constraint always holds along the algorithm iterative procedure; therefore, the algorithm can be terminated at any time. This is in contrast to the ADMM-based solutions that meet constraint feasibility asymptotically. Further, we show that the proposed algorithm can handle possible link nonlinearity due to logarithmically-quantized data transmission (or any sign-preserving odd sector-bound nonlinear mapping). We prove convergence over uniformly-connected dynamic networks (i.e., a hybrid setup) that may occur in mobile and time-varying multi-agent networks. Further, the latency issue over the network is addressed by proposing delay-tolerant solutions. To our best knowledge, accelerated momentum-based convergence, nonlinear linking, all-time feasibility, uniform network connectivity, and handling (possible) time delays are not altogether addressed in the literature. These contributions make our solution practical in many real-world applications.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Distributed constrained optimization	ru_RU
dc.subject	consensus	ru_RU
dc.subject	graph theory	ru_RU
dc.subject		ru_RU
dc.subject	convex analysis	ru_RU
dc.subject	resource scheduling and allocation	ru_RU
dc.title	Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency.	ru_RU
dc.type	Articles in international journals and collections	ru_RU