A feedback-feedforward adaptive stepsize law for gradient descent is shown via Lyapunov analysis to achieve O(1/k) last-iterate convergence for convex locally smooth objectives with robustness to inexact gradients.
Convex optimization for shallow neural networks,
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Adaptive control mechanisms in gradient descent algorithms
A feedback-feedforward adaptive stepsize law for gradient descent is shown via Lyapunov analysis to achieve O(1/k) last-iterate convergence for convex locally smooth objectives with robustness to inexact gradients.