Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods-Reference-Cited by-同舟云学术

Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods

Published:2024-02-05 Issue: Volume: Page:1-36
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Dvurechensky Pavel¹^ORCID,Ostroukhov Petr²³⁴^ORCID,Gasnikov Alexander²³⁵,Uribe César A.⁶,Ivanova Anastasiya⁷⁸

Affiliation:

1. Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany

2. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

3. Institute for Information Transmission Problems RAS, Moscow, Russia

4. Mohamed bin Zayed University of Artificial Intelligence, Abu-Dhabi, UAE

5. Skoltech, Moscow, Russia

6. Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA

7. HSE University, Moscow, Russian Federation

8. Univ. Grenoble Alpes, LJK, Grenoble, France

Funder

Ministry of Science and Higher Education

Deutsche Forschungsgemeinschaft

National Science Foundation

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2023.2296443

Reference48 articles.

1. N. Agarwal and E. Hazan Lower bounds for higher-order convex optimization in Proceedings of the 31st Conference On Learning Theory S. Bubeck V. Perchet and P. Rigollet eds. Proceedings of Machine Learning Research Vol. 75 06–09 Jul. PMLR 2018 pp. 774–792.

2. A. Alacaoglu Q. Tran Dinh O. Fercoq and V. Cevher Smooth primal–dual coordinate descent algorithms for nonsmooth convex optimization in Advances in Neural Information Processing Systems 30 I. Guyon U.V. Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan and R. Garnett eds. Curran Associates Inc. 2017 pp. 5852–5861.

3. Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints;Anikin A.S.;Comput. Math. Math. Phys.,2017

4. Oracle complexity of second-order methods for smooth convex optimization

5. Estimate sequence methods: extensions and approximations;Baes M.;Inst. Oper. Res. ETH Zür. Switzerland,2009