Nesterov accelerated gradient method

For a convex objective with an $L$-Lipschitz gradient, Nesterov's accelerated gradient method reaches objective error $\delta$ in $O(\sqrt{L{R}^2/\delta })$ iterations when a minimizer lies within distance $R$ of the initial point.

This is an accelerated variant of gradient descent; it should not be identified with every first-order or proximal gradient method.