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Flatland: The Adventures of Gradient Descent with Large Step Sizes
The training of neural networks often entails objective functions that are not globally L-smooth. For these functions, it is both theroetically and practically difficult to reply to the question: what is the largest possible step size that ensures the convergence of gradient descent (GD)? We address
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