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Stronger Lower Bounds for (Non-)Anytime Acceleration of Gradient Descent
The rate-optimal convergence rate of gradient descent (GD) with a fixed step-size is well known to be $Θ(N^{-1})$ for $L$-Lipschitz smooth convex objectives in the prior art in convex optimization. Surprisingly, several recent works show that we can accelerate vanilla GD by applying a nonconstant, n
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