4 papers
Sharp Dimension Dependence for the Last Iterate of the SubGradient Method
Guglielmo Beretta, Tommaso Cesari, Roberto Colomboni +1
We study the last iterate of the projected subGradient Method (sGM) for convex Lipschitz objectives defined on . We prove that, for a finite horizon and a constan…
New Bounds for the Last Iterate of the Stochastic subGradient Method
Guglielmo Beretta, Tommaso Cesari, Roberto Colomboni +1
We study the last iterate of the stochastic subgradient method for one-dimensional convex Lipschitz objectives. For a fixed horizon , we consider the standard fixed stepsizes $η…
An Improved Analysis of the Clipped Stochastic subGradient Method under Heavy-Tailed Noise
Daniela Angela Parletta, Andrea Paudice, Saverio Salzo
In this paper, we provide novel optimal (or near optimal) convergence rates for a clipped version of the stochastic subgradient method. We consider nonsmooth convex problems over p…
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
Khaled Eldowa, Andrea Paudice
In this paper, we provide novel tail bounds on the optimization error of Stochastic Mirror Descent for convex and Lipschitz objectives. Our analysis extends the existing tail bound…