КФУ. Карточка публикации. Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks

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NONLINEAR PERTURBATION-BASED NON-CONVEX OPTIMIZATION OVER TIME-VARYING NETWORKS

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2024

Язык

английский

Авторы, сотрудники КФУ

Габидуллина Зульфия Равилевна, автор

Другие авторы

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. 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

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https://repository.kpfu.ru/?p_id=302892

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. 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