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Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent
Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning. For convex $L$-smooth components, recent work establishes convergence guarantees for an averaged iterate with component stepsizes as large as order $1/L$. However, the final iterat
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