| Форма представления | Статьи в зарубежных журналах и сборниках |
| Год публикации | 2024 |
| Язык | русский |
|
Габидуллина Зульфия Равилевна, автор
|
|
Doostmohammadian Mohammadreza , автор
Rabiee Hamid R. , автор
|
| Библиографическое описание на языке оригинала |
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, and Hamid R. Rabiee,
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks.
IEEE Transactions on Network Science and Engineering, IEEE (USA) (Early Access).
Print ISSN: 2327-4697.
Online ISSN: 2327-4697.
DOI: 10.1109/TNSE.2024.3439744.
https://ieeexplore.ieee.org/document/10629194
|
| Аннотация |
IEEE Transactions on Network Science and Engineering, |
| Ключевые слова |
Distributed Algorithm, Non-Convex Local Optimization, Network Science, Perturbation Theory |
| Название журнала |
IEEE Transactions on Network Science and Engineering,
|
| URL |
https://ieeexplore.ieee.org/document/10629194 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=303141 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Габидуллина Зульфия Равилевна |
ru_RU |
| dc.contributor.author |
Doostmohammadian Mohammadreza |
ru_RU |
| dc.contributor.author |
Rabiee Hamid R. |
ru_RU |
| dc.date.accessioned |
2024-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2024-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2024 |
ru_RU |
| dc.identifier.citation |
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, and Hamid R. Rabiee,
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks.
IEEE Transactions on Network Science and Engineering, IEEE (USA) (Early Access).
Print ISSN: 2327-4697.
Online ISSN: 2327-4697.
DOI: 10.1109/TNSE.2024.3439744.
https://ieeexplore.ieee.org/document/10629194
|
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=303141 |
ru_RU |
| dc.description.abstract |
IEEE Transactions on Network Science and Engineering, |
ru_RU |
| dc.description.abstract |
Decentralized optimization strategies are useful for a range of applications from networked estimation to distributed
machine learning. This paper studies finite-sum minimization problems described over a network of nodes and proposes a
computationally efficient algorithm that not only solves distributed convex problems but also optimally finds the solution to local
non-convex objective functions. The algorithm is in single-timescale with no extra inner consensus loop and evaluates one gradient
entry per node per time in contrast to batch gradient optimization in some literature. Further, the algorithm addresses link-level
nonlinearity representing, for example, logarithmic quantization of the exchanged data. Leveraging perturbation-based theory along
with algebraic Laplacian network analysis allows to prove optimal convergence and dynamics stability over time-varying and switching
networks. The time-varying network setup might be due to packet drops and link failures. Despite the nonlinear nature of the dynamics,
we prove exact convergence in the face of odd sign-preserving sector-bound nonlinear data transmission over the links. Illustrative
numerical simulations further highlight our contributions. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
Distributed Algorithm |
ru_RU |
| dc.subject |
Non-Convex Local Optimization |
ru_RU |
| dc.subject |
Network Science |
ru_RU |
| dc.subject |
Perturbation Theory |
ru_RU |
| dc.title |
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks |
ru_RU |
| dc.type |
Статьи в зарубежных журналах и сборниках |
ru_RU |
|
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