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Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima

MacDonald, Lachlan Ewen et al.
arXiv (All) · Papers
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dynamical-systemsmachine-learningoptimization-and-control
An important quantity in the theory of gradient descent (GD) is the \emph{sharpness}, defined as the largest eigenvalue of the objective Hessian. Classical analyses typically require the step size to be uniformly smaller than twice the reciprocal of the sharpness, but this condition is frequently vi
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