Sublinear Regret with Barzilai-Borwein Step Sizes

TL;DR

This paper demonstrates that the Barzilai-Borwein gradient method achieves sublinear regret in online optimization, making it effective for large-scale problems without requiring strong convexity.

Contribution

The study provides a rigorous regret analysis of the Barzilai-Borwein method in online settings, showing sublinear regret and zero average regret without strong convexity assumptions.

Findings

Regret of BB algorithm is sublinear in time.

Average regret approaches zero without strong convexity.

BB method is suitable for large-scale online optimization.

Abstract

This paper considers the online scenario using the Barzilai-Borwein Quasi-Newton Method. In an online optimization problem, an online agent uses a certain algorithm to decide on an objective at each time step after which a possible loss is encountered. Even though the online player will ideally try to make the best decisions possible at each time step, there is a notion of regret associated with the player's decisions. This study examines the regret of an online player using optimization methods like the Quasi-Newton methods, due to their fast convergent properties. The Barzilai-Borwein (BB) gradient method is chosen in this paper over other Quasi-Newton methods such as the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm because of its less computational complexities. In addition, the BB gradient method is suitable for large-scale optimization problems including the online optimization scenario presented in this paper. To corroborate the effectiveness of the Barzilai-Borwein (BB) gradient algorithm, a greedy online gradient algorithm is used in this study based on the two BB step sizes. Through a rigorous regret analysis on the BB step sizes, it is established that the regret obtained from the BB algorithm is sublinear in time. Moreover, this paper shows that the average regret using the online BB algorithm approaches zero without assuming strong convexity on the cost function to be minimized.