A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast

Abstract

Since Nesterov's seminal 1983 work, many accelerated first-order optimization methods have been proposed, but their analyses lacks a common unifying structure. In this work, we identify a geometric structure satisfied by a wide range of first-order accelerated methods. Using this geometric insight, we present several novel generalizations of accelerated methods. Most interesting among them is a method that reduces the squared gradient norm with $\mathcal{O}(1/K^4)$ rate in the prox-grad setup, faster than the $\mathcal{O}(1/K^3)$ rates of Nesterov's FGM or Kim and Fessler's FPGM-m.

References in corpus (2)

• From Nesterov's Estimate Sequence to Riemannian Acceleration
• Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization