  We propose a new method for unconstrained optimization of a smooth and
strongly convex function, which attains the optimal rate of convergence of
Nesterov's accelerated gradient descent. The new algorithm has a simple
geometric interpretation, loosely inspired by the ellipsoid method. We provide
some numerical evidence that the new method can be superior to Nesterov's
accelerated gradient descent.
