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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…
math.OC2025
Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
Yasong Feng, Yifan Jiang, Tianyu Wang +1
This work provides a novel convergence analysis for stochastic optimization in terms of stopping times, addressing the practical reality that algorithms are often terminated adapti…