Papers › Accelerated Gradient Descent via Long Steps
Accelerated Gradient Descent via Long Steps
Benjamin Grimmer, Kevin Shu, Alex L. Wang
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Recently Grimmer [1] showed for smooth convex optimization by utilizing longer steps periodically, gradient descent's textbook LD²/2T convergence guarantees can be improved by constant factors, conjecturing an accelerated rate strictly faster than O(1/T) could be possible. Here we prove such a big-O gain, establishing gradient descent's first accelerated convergence rate in this setting. Namely, we prove a O(1/T^(1.0564)) rate for smooth convex minimization by utilizing a nonconstant nonperiodic sequence of increasingly large stepsizes. It remains open if one can achieve the O(1/T^(1.178)) rate conjectured by Das Gupta et. al. [2] or the optimal gradient method rate of O(1/T²). Big-O convergence rate accelerations from long steps follow from our theory for strongly convex optimization, similar to but somewhat weaker than those concurrently developed by Altschuler and Parrilo [3].
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