Author

Tian-Yu Wang

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Preprint Sep 2026

Optimal Recursive Composition and Dyadic Phase Laws for Gradient Descent with Predetermined Stepsizes

Predetermined stepsize schedules featuring carefully chosen long steps have recently been shown to accelerate gradient descent (GD) on smooth convex functions. A prominent class of such schedules is built through recursive composition. In this paper, we characterize the convergence of these optimized recursive schedule...

Yu Liu, Kang Chen, Ru-Jun Jiang et al. · 1 citation

#machine learning Preprint Sep 2026

The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives

We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives. We prove that, under standard noise assumptions, the time-uniform convergence rate gets arbitrarily close to $\sqrt{\log n / n}$ but never reaches it. More specifically, we...

Rui-Jie Li, Kang Chen, Tian-Yu Wang · 1 citation