Learning rate decay can exponentially speed SGD on some nonconvex functions
Authors prove a benchmark class where decay uses exponentially fewer gradient queries than any fixed small step size.
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Authors prove a benchmark class where decay uses exponentially fewer gradient queries than any fixed small step size.
New optimization theory paper proves near-optimal lower bounds for gradient descent with predetermined stepsizes, confirming silver-schedule optimality.
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