Stochastic proximal quasi-Newton methods for non-convex composite optimization-Reference-Cited by-同舟云学术

Stochastic proximal quasi-Newton methods for non-convex composite optimization

Published:2018-05-18 Issue:5 Volume:34 Page:922-948
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Wang Xiaoyu¹²,Wang Xiao²,Yuan Ya-xiang¹

Affiliation:

1. LSEC, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences, Beijing, People's Republic of China

2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2018.1471141

Reference60 articles.

1. Z. Allen-Zhu, Natasha 2: Faster non-convex optimization than SGD, preprint (2017). Available at arXiv:1708.08694v2.

2. Z. Allen-Zhu, Natasha: Faster stochastic non-convex optimization via strongly non-convex parameter, preprint (2017). Available at arXiv:1702.00763.

3. Z. Allen-Zhu and E. Hazan, Variance reduction for faster non-convex optimization, International Conference on Machine Learning, New York, NY, 2016, pp. 699–707.

4. S. Becker and J. Fadili, A quasi-Newton proximal splitting method, Advances in Neural Information Processing Systems, Lake Tahoe, 2012, pp. 2618–2626.

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