Under a new smoothness assumption linking local curvature to the loss gap, increasing learning rates provably accelerate GD and SGD convergence, with up to Theta(T) speedup in special cases.
Understanding Gradient Clipping In Incremental Gradient Methods
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Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Under a new smoothness assumption linking local curvature to the loss gap, increasing learning rates provably accelerate GD and SGD convergence, with up to Theta(T) speedup in special cases.