Revisiting Stochastic Gradient Descent for Strongly Convex Objectives: Tight Uniform-in-Time Bounds

Stochastic optimization via Stochastic Gradient Descent (SGD) is a fundamental problem in statistics and optimization. This paper revisits Stochastic Gradient Descent (SGD) for strongly convex objectives, establishing tight, uniform-in-time convergence bounds. We prove that, with probability at least $1-\beta$, a convergence rate of order $\frac{\log \log k+\log (1\mathrm{/}\beta )}{k}$ simultaneously holds for all $k\in {\mathbb{N}}_{+}$, and demonstrate this bound is tight up to constant factors. We also provide an improved last-iterate convergence rate for such objectives. While focused on strongly convex objectives, our results generalize to the Polyak-Łojasiewicz functions and indicate an $\mathcal{O}(k^{-1}\log \log k)$ convergence rate for contractive stochastic approximation with additive noise.