Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence

TL;DR

This paper introduces a globally convergent quasi-Newton method with explicit non-asymptotic superlinear convergence, achieved through an online learning framework for Hessian approximation, filling a gap in existing convergence guarantees.

Contribution

The paper presents the first quasi-Newton algorithm with global convergence and explicit superlinear rate, using an online convex optimization approach for Hessian updates.

Findings

Achieves global convergence with explicit superlinear rate.

Introduces an online learning framework for Hessian approximation.

Provides theoretical analysis linking regret bounds to convergence rate.

Abstract

Quasi-Newton algorithms are among the most popular iterative methods for solving unconstrained minimization problems, largely due to their favorable superlinear convergence property. However, existing results for these algorithms are limited as they provide either (i) a global convergence guarantee with an asymptotic superlinear convergence rate, or (ii) a local non-asymptotic superlinear rate for the case that the initial point and the initial Hessian approximation are chosen properly. In particular, no current analysis for quasi-Newton methods guarantees global convergence with an explicit superlinear convergence rate. In this paper, we close this gap and present the first globally convergent quasi-Newton method with an explicit non-asymptotic superlinear convergence rate. Unlike classical quasi-Newton methods, we build our algorithm upon the hybrid proximal extragradient method and propose a novel online learning framework for updating the Hessian approximation matrices. Specifically, guided by the convergence analysis, we formulate the Hessian approximation update as an online convex optimization problem in the space of matrices, and we relate the bounded regret of the online problem to the superlinear convergence of our method.