Negative momentum enables global convergence in convex-concave min-max optimization and accelerated rates in the strongly-convex-strongly-concave setting.
Min-max optimization is strictly easier than variational in- equalities.Preprint at
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This expository article introduces stepsize hedging as a way to accelerate gradient descent without additional terms like momentum.
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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.
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Stepsize Hedging: an Alternative Mechanism for Accelerating Gradient Descent
This expository article introduces stepsize hedging as a way to accelerate gradient descent without additional terms like momentum.