Understanding Lookahead Dynamics Through Laplace Transform

TL;DR

This paper develops a frequency-domain framework using Laplace transforms and HRDEs to analyze and improve hyperparameter convergence in game optimization algorithms, especially Lookahead.

Contribution

It introduces a novel frequency-domain approach for analyzing hyperparameter dynamics in game optimization, providing tighter convergence criteria and practical tuning guidance.

Findings

Higher-precision HRDE models yield tighter convergence criteria.

Empirical validation confirms the effectiveness of the frequency-domain approach.

Framework extends to locally linear operators for scalable hyperparameter selection.

Abstract

We introduce a frequency-domain framework for convergence analysis of hyperparameters in game optimization, leveraging High-Resolution Differential Equations (HRDEs) and Laplace transforms. Focusing on the Lookahead algorithm--characterized by gradient steps $k$ and averaging coefficient $\alpha$--we transform the discrete-time oscillatory dynamics of bilinear games into the frequency domain to derive precise convergence criteria. Our higher-precision $O(\gamma^2)$-HRDE models yield tighter criteria, while our first-order $O(\gamma)$-HRDE models offer practical guidance by prioritizing actionable hyperparameter tuning over complex closed-form solutions. Empirical validation in discrete-time settings demonstrates the effectiveness of our approach, which may further extend to locally linear operators, offering a scalable framework for selecting hyperparameters for learning in games.