[Submitted on 30 Oct 2026]
Abstract:This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization. Throughout, we assume that gradient noise is conditionally zero-mean with bounded variance. Among problems with dimension $dgeq N+1$, we provide an exact worst-case analysis of the expected suboptimality of stochastic gradient descent’s final iterate. Our analysis applies to any (potentially nonconstant) schedule of $N$ stepsizes in $[0,1.5]^N$. Here, exact means that we provide a matching objective function and noise distributions where our guarantee is attained. Whenever the variance bound is at least $Omega(1/sqrt{N})$, our theory provides the minimax optimal stepsize schedule for stochastic gradient descent and proves its uniqueness. These optimal stepsizes monotonically decrease, ruling out any accelerations in expected final suboptimality from fixed fractal schedules observed in the deterministic case.
Submission history
From: Benjamin Grimmer [view email]
[v1] Fri, 30 Oct 2026 17:14:59 UTC (109 KB)
