Inexact tensor methods and their application to stochastic convex optimization-Reference-Cited by-同舟云学术

Inexact tensor methods and their application to stochastic convex optimization

Published:2023-11-17 Issue: Volume: Page:1-42
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Agafonov Artem¹²,Kamzolov Dmitry¹²^ORCID,Dvurechensky Pavel³^ORCID,Gasnikov Alexander¹⁴⁵,Takáč Martin²^ORCID

Affiliation:

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

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

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

4. Institute for Information Transmission Problems, Moscow, Russia

5. HSE University, Moscow, Russia

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2023.2261604

Reference58 articles.

1. A. Agafonov D. Kamzolov P. Dvurechensky and A. Gasnikov Inexact tensor methods and their application to stochastic convex optimization preprint 2020. arXiv:2012.15636.

2. N. Agarwal and E. Hazan Lower bounds for higher-order convex optimization preprint 2017. arXiv:1710.10329.

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

4. M. Baes Estimate sequence methods: extensions and approximations . Institute for Operations Research ETH Zürich Switzerland 2009.

5. Stochastic analysis of an adaptive cubic regularization method under inexact gradient evaluations and dynamic Hessian accuracy

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