An Accelerated Second-Order Method for Distributed Stochastic Optimization-Reference-Cited by-同舟云学术

An Accelerated Second-Order Method for Distributed Stochastic Optimization

Published:2021-12-14 Issue: Volume: Page:
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Container-title:2021 60th IEEE Conference on Decision and Control (CDC)
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Author:

Agafonov Artem¹,Dvurechensky Pavel²,Scutari Gesualdo³,Gasnikov Alexander⁴,Kamzolov Dmitry¹,Lukashevich Aleksandr⁵,Daneshmand Amir³

Affiliation:

1. Moscow Institute of Physics and Technology,Russia

2. Weierstrass Institute for Applied Analysis and Stochastics,Germany

3. Purdue University,School of Industrial Engineering,USA

4. Institute for Information Transmission Problems RAS, HSE University,Moscow Institute of Physics and Technology,Russian Federation

5. Skolkovo Institute of Science and Technology,Russia

Funder

Ministry of Science and Higher Education of the Russian Federation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9682670/9682776/09683400.pdf?arnumber=9683400

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3. Statistically preconditioned accelerated gradient method for distributed optimization;hendrikx;Proceedings of the 37th International Conference on Machine Learning,2020

4. On convergence of distributed approximate newton methods: Globalization, sharper bounds and beyond;yuan;Journal of Machine Learning Research,2020

5. Aide: Fast and communication efficient distributed optimization;reddi,2016

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