I will present 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. In "practice" the new method seems to be superior to Nesterov's accelerated gradient descent. Joint work with Yin-Tat Lee and Mohit Singh.