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Restart and Adaptive Acceleration in Stochastic Gradient Methods
We study restart schemes in stochastic optimization problems for non-smooth and weakly convex functions that satisfy a Kurdyka-Łojasiewicz (KŁ) inequality. Using restarts allows us to leverage KŁ inequalities to achieve optimal convergence rates, with acceleration depending explicitly on the KŁ expo
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