For nonsmooth convex finite-sum optimization, random reshuffling and single shuffle achieve last-iterate rates up to n^{1/4} and n^{1/2} faster than proximal gradient descent, with random reshuffling suffix average matching a known lower bound.
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Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
For nonsmooth convex finite-sum optimization, random reshuffling and single shuffle achieve last-iterate rates up to n^{1/4} and n^{1/2} faster than proximal gradient descent, with random reshuffling suffix average matching a known lower bound.