Fast Global Convergence for Low-rank Matrix Recovery via Riemannian
Gradient Descent with Random Initialization

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Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization

31 December 2020

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Papers citing "Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization"

19 / 19 papers shown

Title
Escaping from saddle points on Riemannian manifolds Yue Sun Nicolas Flammarion Maryam Fazel 50 72 0 18 Jun 2019
Efficiently escaping saddle points on manifolds Christopher Criscitiello Nicolas Boumal 69 63 0 10 Jun 2019
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery Richard Y. Zhang Somayeh Sojoudi Javad Lavaei 53 51 0 07 Jan 2019
Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis Salar Fattahi Somayeh Sojoudi 64 38 0 30 Dec 2018
Subgradient Descent Learns Orthogonal Dictionaries Yu Bai Qijia Jiang Ju Sun 129 51 0 25 Oct 2018
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview Yuejie Chi Yue M. Lu Yuxin Chen 65 426 0 25 Sep 2018
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma 71 237 0 21 Mar 2018
Gradient Descent Can Take Exponential Time to Escape Saddle Points S. Du Chi Jin Jason D. Lee Michael I. Jordan Barnabás Póczós Aarti Singh 56 244 0 29 May 2017
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis Rong Ge Chi Jin Yi Zheng 129 436 0 03 Apr 2017
How to Escape Saddle Points Efficiently Chi Jin Rong Ge Praneeth Netrapalli Sham Kakade Michael I. Jordan ODL 229 836 0 02 Mar 2017
Matrix Completion has No Spurious Local Minimum Rong Ge Jason D. Lee Tengyu Ma 111 599 0 24 May 2016
Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro ODL 109 388 0 23 May 2016
Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions Ioannis Panageas Georgios Piliouras 54 142 0 02 May 2016
A Geometric Analysis of Phase Retrieval Ju Sun Qing Qu John N. Wright 129 526 0 22 Feb 2016
Gradient Descent Converges to Minimizers Jason D. Lee Max Simchowitz Michael I. Jordan Benjamin Recht 71 212 0 16 Feb 2016
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen Emmanuel J. Candes 111 590 0 19 May 2015
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition Rong Ge Furong Huang Chi Jin Yang Yuan 140 1,059 0 06 Mar 2015
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality R. Schneider André Uschmajew 75 133 0 21 Feb 2014
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization Benjamin Recht Maryam Fazel P. Parrilo 419 3,771 0 28 Jun 2007

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