Форма представления | Статьи в зарубежных журналах и сборниках |
Год публикации | 2024 |
Язык | английский |
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Габидуллина Зульфия Равилевна, автор
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Doostmohammadian Mohammadreza , автор
Rabiee Hamid R. , автор
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Библиографическое описание на языке оригинала |
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, and Hamid R. Rabiee. Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks.
arXiv preprint arXiv:2408.02269, 2024
Submitted on 5 Aug 2024
https://doi.org/10.48550/arXiv.2408.02269 |
Аннотация |
arxiv.org |
Ключевые слова |
Distributed Algorithm, Non-Convex Local Optimization, Network Science, Perturbation Theory |
Название журнала |
arxiv.org
|
URL |
https://www.arxiv.org/abs/2408.02269 |
Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=302892 |
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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.
arXiv preprint arXiv:2408.02269, 2024
Submitted on 5 Aug 2024
https://doi.org/10.48550/arXiv.2408.02269 |
ru_RU |
dc.identifier.uri |
https://repository.kpfu.ru/?p_id=302892 |
ru_RU |
dc.description.abstract |
arxiv.org |
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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