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On the Convergence of
Stochastic Gradient Methods
J. Chen
1 Introduction
Recent advances in stochastic optimization have demonstrated remarkable convergence properties under mild assumptions on the objective function.
In this work, we establish tight bounds for the convergence rate of SGD variants applied to non-convex objectives, extending results from [1, 2].
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