  State-of-the-art methods in convex and non-convex optimization employ
higher-order derivative information, either implicitly or explicitly. We
explore the limitations of higher-order optimization and prove that even for
convex optimization, a polynomial dependence on the approximation guarantee and
higher-order smoothness parameters is necessary. As a special case, we show
Nesterov's accelerated cubic regularization method to be nearly tight.
