Optimized first-order methods for smooth convex minimization

Abstract

We introduce new optimized first-order methods for smooth unconstrained convex minimization. Drori and Teboulle recently described a numerical method for computing the $N$-iteration optimal step coefficients in a class of first-order algorithms that includes gradient methods, heavy-ball methods, and Nesterov's fast gradient methods. However, Drori and Teboulle's numerical method is computationally expensive for large $N$, and the corresponding numerically optimized first-order algorithm requires impractical memory and computation for large-scale optimization problems. In this paper, we propose optimized first-order algorithms that achieve a convergence bound that is two times smaller than for Nesterov's fast gradient methods; our bound is found analytically and refines the numerical bound. Furthermore, the proposed optimized first-order methods have efficient recursive forms that are remarkably similar to Nesterov's fast gradient methods.