Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent

TL;DR

This paper provides the first rigorous convergence analysis of hypergradient descent, demonstrating its ability to adapt stepsizes effectively, achieve superlinear convergence, and outperform existing adaptive methods in convex optimization tasks.

Contribution

It offers a theoretical convergence analysis of HDM, introduces new variants with momentum, and empirically shows superior performance over other adaptive methods and quasi-Newton algorithms.

Findings

HDM automatically finds optimal local stepsizes.

HDM with heavy-ball momentum outperforms other adaptive methods.

HDM-HB matches L-BFGS performance with less memory and computation.

Abstract

This paper investigates the convergence properties of the hypergradient descent method (HDM), a 25-year-old heuristic originally proposed for adaptive stepsize selection in stochastic first-order methods. We provide the first rigorous convergence analysis of HDM using the online learning framework of [Gao24] and apply this analysis to develop new state-of-the-art adaptive gradient methods with empirical and theoretical support. Notably, HDM automatically identifies the optimal stepsize for the local optimization landscape and achieves local superlinear convergence. Our analysis explains the instability of HDM reported in the literature and proposes efficient strategies to address it. We also develop two HDM variants with heavy-ball and Nesterov momentum. Experiments on deterministic convex problems show HDM with heavy-ball momentum (HDM-HB) exhibits robust performance and significantly outperforms other adaptive first-order methods. Moreover, HDM-HB often matches the performance of L-BFGS, an efficient and practical quasi-Newton method, using less memory and cheaper iterations.