Unified analysis of SGD-type methods
Optimization and Control
2023-03-30 v1 Machine Learning
Abstract
This note focuses on a simple approach to the unified analysis of SGD-type methods from (Gorbunov et al., 2020) for strongly convex smooth optimization problems. The similarities in the analyses of different stochastic first-order methods are discussed along with the existing extensions of the framework. The limitations of the analysis and several alternative approaches are mentioned as well.
Cite
@article{arxiv.2303.16502,
title = {Unified analysis of SGD-type methods},
author = {Eduard Gorbunov},
journal= {arXiv preprint arXiv:2303.16502},
year = {2023}
}
Comments
Part of the Encyclopedia of Optimization. 8 pages