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math.OC2026
Stronger Lower Bounds for (Non-)Anytime Acceleration of Gradient Descent
Minchan Jung, Hanseul Cho, Chulhee Yun
The rate-optimal convergence rate of gradient descent (GD) with a fixed step-size is well known to be for -Lipschitz smooth convex objectives in the prior art in con…
math.OC2024
Fundamental Benefit of Alternating Updates in Minimax Optimization
Jaewook Lee, Hanseul Cho, Chulhee Yun
The Gradient Descent-Ascent (GDA) algorithm, designed to solve minimax optimization problems, takes the descent and ascent steps either simultaneously (Sim-GDA) or alternately (Alt…
math.OC2022
SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization
Hanseul Cho, Chulhee Yun
Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for solving finite-sum minimax optimization problems. Most practical implementations of SGDA randomly reshuf…