SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points

We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points. We show that a simple perturbed version of...

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Bibliographische Detailangaben

1. Verfasser:	Li, Zhize
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Data Structures and Algorithms Computer Science - Learning Mathematics - Optimization and Control Statistics - Machine Learning
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