Preprint
Jul 2026
Sharp Dimension Dependence for the Last Iterate of the SubGradient Method
We study the last iterate of the projected subGradient Method (sGM) for convex Lipschitz objectives defined on $\mathbb{R}^d$. We prove that, for a finite horizon $n$ and a constant stepsize $\eta=\Theta(1/\sqrt n)$, the last iterate achieves an optimization error of order $d/\sqrt n$, showing that the extra $\log n$ f...
Guglielmo Beretta, Tommaso Cesari, Roberto Colomboni et al.
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