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• W2980452942 abstract "We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don't increase the stepsize too fast and 2) don't overstep the local curvature. No need for functional values, no line search, no information about the function except for the gradients. By following these rules, you get a method adaptive to the local geometry, with convergence guarantees depending only on the smoothness in a neighborhood of a solution. Given that the problem is convex, our method converges even if the global smoothness constant is infinity. As an illustration, it can minimize arbitrary continuously twice-differentiable convex function. We examine its performance on a range of convex and nonconvex problems, including logistic regression and matrix factorization." @default.
• W2980452942 created "2019-10-25" @default.
• W2980452942 creator A5008101394 @default.
• W2980452942 creator A5015808280 @default.
• W2980452942 date "2019-10-21" @default.
• W2980452942 modified "2023-09-27" @default.
• W2980452942 title "Adaptive Gradient Descent without Descent" @default.
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• W2980452942 hasPublicationYear "2019" @default.
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