Improving the Bit Complexity of Communication for Distributed Convex Optimization
Author:
Affiliation:
1. Massachusetts Institute of Technology, Cambridge, USA
2. University of Washington, Seattle, USA / Microsoft Research, Redmond, USA
3. Carnegie Mellon University, Pittsburgh, USA
Funder
NSF (National Science Foundation)
Simons Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3618260.3649787
Reference81 articles.
1. Alekh Agarwal and Leon Bottou. 2015. A lower bound for the optimization of finite sums. In International conference on machine learning.
2. Alekh Agarwal and John C Duchi. 2011. Distributed delayed stochastic optimization. Advances in neural information processing systems.
3. Katyusha: The first direct acceleration of stochastic gradient methods;Allen-Zhu Zeyuan;The Journal of Machine Learning Research.,2017
4. Emile Anand Jan van den Brand Mehrdad Ghadiri and Daniel Zhang. 2024. The Bit Complexity of Dynamic Algebraic Formulas and their Determinants. arXiv preprint arXiv:2401.11127.
5. Alexandr Andoni, Jarosł aw Bł asiok, and Arnold Filtser. 2022. Communication Complexity of Inner Product in Symmetric Normed Spaces. arXiv preprint arXiv:2211.13473.
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