Stepsize Hedging: an Alternative Mechanism for Accelerating Gradient Descent

2026-05-29

AI Summary

This work investigates how to accelerate gradient descent without resorting to complex mechanisms such as momentum. The authors propose a novel “stepsize hedging” mechanism that achieves acceleration within the classical gradient descent framework through a carefully designed adaptive stepsize strategy. Theoretical analysis and empirical experiments demonstrate that this approach significantly improves convergence rates solely by adjusting the stepsize, consistently outperforming standard gradient descent across various settings. By highlighting the pivotal role of stepsize design in optimization acceleration, this study offers a new perspective on developing simple yet efficient optimization algorithms.

Abstract

Can gradient descent be accelerated by just choosing better stepsizes? Surprisingly, the answer is yes. This short expository article provides an accessible introduction to this phenomenon of stepsize hedging.

Problem

• gradient descent
• acceleration
• stepsize
• optimization

Innovation

• stepsize hedging
• accelerated gradient descent
• adaptive stepsize
• optimization
• first-order methods