Accelerating Gradient Descent for Over-Parameterized Asymmetric Low-Rank Matrix Sensing via Preconditioning
Author:
Affiliation:
1. ShanghaiTech University,School of Information Science and Technology,Shanghai,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446187.pdf?arnumber=10446187
Reference22 articles.
1. The Power of Convex Relaxation: Near-Optimal Matrix Completion
2. Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization
3. Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
4. Local Minima and Convergence in Low-Rank Semidefinite Programming
5. Low-rank matrix completion using alternating minimization
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