Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution-Reference-Cited by-同舟云学术

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

Published:2019-08-05 Issue:3 Volume:20 Page:451-632
ISSN:1615-3375
Container-title:Foundations of Computational Mathematics
language:en
Short-container-title:Found Comput Math

Author:

Ma Cong,Wang Kaizheng,Chi Yuejie,Chen Yuxin

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Analysis

Link

http://link.springer.com/content/pdf/10.1007/s10208-019-09429-9.pdf

Reference133 articles.

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3. Ahmed, A., Recht, B., Romberg, J.: Blind deconvolution using convex programming. IEEE Transactions on Information Theory 60(3), 1711–1732 (2014)

4. Alon, N., Spencer, J.H.: The Probabilistic Method (3rd Edition). Wiley (2008)

5. Bahmani, S., Romberg, J.: Phase retrieval meets statistical learning theory: A flexible convex relaxation. In: Artificial Intelligence and Statistics, pp. 252–260 (2017)

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