Negative momentum enables global convergence in convex-concave min-max optimization and accelerated rates in the strongly-convex-strongly-concave setting.
Convergence rate analysis of the gradient descent–ascent method for convex–concave saddle-point problems.Optimization Methods and Software, 39(5):967–989
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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.