Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Łojasiewicz Condition
Author:
Affiliation:
1. University of Colorado,Department of Applied Mathematics,Boulder
2. University of Colorado,Department of Electrical, Computer and Energy Engineering and with the Department of Applied Mathematics,Boulder
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9992315/9992317/09993166.pdf?arnumber=9993166
Reference38 articles.
1. Optimization algorithms as robust feedback controllers;hauswirth,2021
2. Characterizations of Łojasiewicz inequalities: Subgradient flows, talweg, convexity
3. Regret minimization in stochastic non-convex learning via a proximal-gradient approach;hallak,2020
4. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods
5. Linear convergence of gradient and proximal-gradient methods under the Polyak-?ojasiewicz condition;karimi;Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases,2016
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