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.

翻译：本文关注于一种简单的方法，即从（Gorbunov等人，2020）的强凸光滑优化问题中统一分析SGD类型方法。讨论了不同随机一阶方法的分析中的相似性，以及现有框架的扩展。同时提到了分析的局限性和几种替代方法。