Stochastic Subgradient Method Converges on Tame Functions

This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic subgradient method, on any semialgebraic locally Lipschitz function, produces limit points that are all first-order stati...

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Bibliographic Details
Published in:	Foundations of computational mathematics Vol. 20; no. 1; pp. 119 - 154
Main Authors:	Davis, Damek, Drusvyatskiy, Dmitriy, Kakade, Sham, Lee, Jason D.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2020 Springer Nature B.V
Subjects:	Applications of Mathematics Computer Science Convergence Convexity Economics Linear and Multilinear Algebras Machine learning Math Applications in Computer Science Mathematics Mathematics and Statistics Matrix Theory Numerical Analysis Queuing theory Smoothness Proximal Subgradient Tame 65K05 Semialgebraic Stochastic subgradient method 65K10 90C15 Differential inclusion 34A60 Lyapunov function
ISSN:	1615-3375, 1615-3383
Online Access:	Get full text
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