Email this article 
DECENTRALIZED CONDITIONAL GRADIENT METHOD ON TIME-VARIABLE GRAPHS
- Authors: Vedernikov R.A.1, Rogozin A.V.1, Gasnikov A.V.2,3
-
Affiliations:
- Moscow Institute of Physics and Technology
- Institute for Information Transmission Problems of the RAS (Kharkevich Institute)
- Caucasian Mathematical Center of the Adyghe State University
- Issue: No 6 (2023)
- Pages: 27-35
- Section: DATA ANALYSIS
- URL: https://ruspoj.com/0132-3474/article/view/675727
- DOI: https://doi.org/10.31857/S0132347423060080
- EDN: https://elibrary.ru/FDENUK
- ID: 675727
Cite item
Open Access
Access granted
Subscription or Fee Access
Abstract
In this paper, we consider a generalization of the decentralized Frank-Wulff algorithm for network time variables, study the convergence properties of the algorithm, and carry out the corresponding numerical experiments. The changing network is modeled as a deterministic or stochastic sequence of graphs.
About the authors
R. A. Vedernikov
Moscow Institute of Physics and Technology
Author for correspondence.
Email: vedernikov.ra@phystech.edu
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9
A. V. Rogozin
Moscow Institute of Physics and Technology
Author for correspondence.
Email: aleksandr.rogozin@phystech.edu
Russia, 141701, Moscow region, Dolgoprudny, Institutskiy per., 9
A. V. Gasnikov
Institute for Information Transmission Problems of the RAS (Kharkevich Institute); Caucasian Mathematical Center of the Adyghe State University
Author for correspondence.
Email: gasnikov@yandex.ru
Russia, 127051, Moscow, Bolshoi Karetny lane, 19, build. 1; Republic of Adygea, 385016, Maykop, st. Pervomaiskaya, 208
References
- Braun G., Carderera A., Combettes C.W. Hassani H., Karbasi A. Mokhtari A., Pokutta S. arXiv (2022) https://arxiv.org/pdf/2211.14103.pdf
- Левитин Е.С., Поляк Б.Т. Методы минимизации при наличии ограничений. Журнал вычислительной математики и математической физики 6.5. 1966. P. 787–823.
- Nedic Angelia. Distributed gradient methods for convex machine learning problems in networks: Distributed optimization. IEEE Signal Processing Magazine 37.3. 2020. P. 92–101.
- Forero Pedro A., Alfonso Cano, and Georgios B. Giannakis. Consensus-based distributed linear support vector machines. Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. 2010.
- Gan Lingwen, Ufuk Topcu, and Steven H. Low. Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems 28.2. 2012. P. 940–951.
- Ram Sundhar Srinivasan, Venugopal V. Veeravalli, and Angelia Nedic. Distributed non-autonomous power control through distributed convex optimization. IEEE INFOCOM 2009. IEEE, 2009.
- Ren Wei, and Randal W. Beard. Distributed consensus in multi-vehicle cooperative control. V. 27. № 2. London: Springer London, 2008.
- Rogozin A., Gasnikov A., Beznosikov A., Kovalev D. Decentralized convex optimization over time-varying graphs: a survey. arXiv (2022) https://arxiv.org/pdf/2210.09719.pdf
- Wai Hoi-To et al. Decentralized Frank–Wolfe algorithm for convex and nonconvex problems. IEEE Transactions on Automatic Control 62.11. 2017. P. 5522–5537.
- Райгородский А.М. Модели случайных графов и их применения. Труды Московского физико-технического института, 2010.
