Negative momentum enables global convergence in convex-concave min-max optimization and accelerated rates in the strongly-convex-strongly-concave setting.
Distributed optimization and statistical learning via the alternating direction method of multipliers.Foundations and Trends® in Machine Learning, 3(1):1–122
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Negative Momentum for Convex-Concave Optimization
Negative momentum enables global convergence in convex-concave min-max optimization and accelerated rates in the strongly-convex-strongly-concave setting.