The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery Without Regularization-Reference-Cited by-同舟云学术

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The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery Without Regularization

Published:2020 Issue: Volume:27 Page:1400-1404
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Li Shuang^ORCID,Li Qiuwei^ORCID,Zhu Zhihui^ORCID,Tang Gongguo^ORCID,Wakin Michael B.^ORCID

Funder

NSF

Defense Advanced Research Projects Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/97/8966529/9141202-aam.pdf

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accelerating Gradient Descent for Over-Parameterized Asymmetric Low-Rank Matrix Sensing via Preconditioning;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Adaptive Rank-One Matrix Completion Using Sum of Outer Products;IEEE Transactions on Circuits and Systems for Video Technology;2023-09

3. Certifying the Absence of Spurious Local Minima at Infinity;SIAM Journal on Optimization;2023-07-13

4. From the simplex to the sphere: faster constrained optimization using the Hadamard parametrization;Information and Inference: A Journal of the IMA;2023-04-27

5. Global convergence of the gradient method for functions definable in o-minimal structures;Mathematical Programming;2023-02-23

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