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math.OC2026
Negative Stepsizes Make Gradient-Descent-Ascent Converge
Henry Shugart, Jason M. Altschuler
Efficient computation of min-max problems is a central question in optimization, learning, games, and control. Arguably the most natural algorithm is gradient-descent-ascent (GDA).…
math.OC2026
Negative Momentum for Convex-Concave Optimization
Henry Shugart, Shuyi Wang, Jason M. Altschuler
This paper revisits momentum in the context of min-max optimization. Momentum is a celebrated mechanism for accelerating gradient dynamics in settings like convex minimization, but…
math.OC2025
Min-Max Optimization Is Strictly Easier Than Variational Inequalities
Henry Shugart, Jason M. Altschuler
Classically, a mainstream approach for solving a convex-concave min-max problem is to instead solve the variational inequality problem arising from its first-order optimality condi…