  Recent results show that vanilla gradient descent can be accelerated for
smooth convex objectives, merely by changing the stepsize sequence. We show
that this can lead to surprisingly large errors indefinitely, and therefore
ask: Is there any stepsize schedule for gradient descent that accelerates the
classic $\mathcal{O}(1/T)$ convergence rate, at \emph{any} stopping time $T$?
