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...
Saved in:
| Main Author: | |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2019
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/8679 https://ink.library.smu.edu.sg/context/sis_research/article/9682/viewcontent/NeurIPS_2019_ssrgd_simple_stochastic_recursive_gradient_descent_for_escaping_saddle_points_Paper.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |