  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.
