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math.OC2026
Optimal Recursive Composition and Dyadic Phase Laws for Gradient Descent with Predetermined Stepsizes
Yu Liu, Kang Chen, Rujun Jiang +1
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…
math.OC2026
The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives
Ruijie Li, Kang Chen, Tianyu Wang
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 noi…
math.OC2025
Revisiting Stochastic Gradient Descent for Strongly Convex Objectives: Tight Uniform-in-Time Bounds
Kang Chen, Yasong Feng, Tianyu Wang
Stochastic optimization via Stochastic Gradient Descent (SGD) is a fundamental problem in statistics and optimization. This paper revisits Stochastic Gradient Descent (SGD) for str…