Fetching the paper…
Reading the bibliography…
This paper considers the global geometry of general low-rank minimization problems via the Burer-Monterio factorization approach.
A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
Samuel Burer and Renato DC Monteiro · 2003
Earlier work this paper cites.
Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
Earlier work this paper cites.
The power of convex relaxation: Near-optimal matrix completion
Emmanuel J Candès and Terence Tao · 2010
Earlier work this paper cites.
Guaranteed rank minimization via singular value projection
Prateek Jain, Raghu Meka, and Inderjit Dhillon · 2010
Earlier work this paper cites.
Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
Benjamin Recht, Maryam Fazel, and Pablo A Parrilo · 2010
Earlier work this paper cites.
Angular synchronization by eigenvectors and semidefinite programming
Amit Singer · 2011
Earlier work this paper cites.
Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods
Hedy Attouch, Jérôme Bolte, and Benar Fux Svaiter · 2013
Earlier work this paper cites.
1-bit matrix completion
Mark A Davenport, Yaniv Plan, Ewout Van Den Berg, and Mary Wootters · 2014
Earlier work this paper cites.
Phase retrieval with application to optical imaging: a contemporary overview
Yoav Shechtman, Yonina C Eldar, Oren Cohen, Henry Nicholas Chapman, Jianwei Miao, and Mordechai Segev · 2015
Earlier work this paper cites.
Nonconvex phase synchronization
Nicolas Boumal · 2016
Earlier work this paper cites.
Matrix completion has no spurious local minimum
Rong Ge, Jason D Lee, and Tengyu Ma · 2016
Cited alongside, same era.
Gradient descent only converges to minimizers
Jason D Lee, Max Simchowitz, Michael I Jordan, and Benjamin Recht · 2016
Cited alongside, same era.
Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions
Ioannis Panageas and Georgios Piliouras · 2016
Cited alongside, same era.
Low-rank solutions of linear matrix equations via Procrustes flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Ben Recht · 2016
Cited alongside, same era.
No spurious local minima in nonconvex low rank problems: A unified geometric analysis
Rong Ge, Chi Jin, and Yi Zheng · 2017
Cited alongside, same era.
How to escape saddle points efficiently
A geometric analysis of phase retrieval
Ju Sun, Qing Qu, and John Wright · 2018
Later among the works it cites.
How much restricted isometry is needed in nonconvex matrix recovery?
Richard Y Zhang, Cédric Josz, Somayeh Sojoudi, and Javad Lavaei · 2018
Later among the works it cites.
Global optimality in low-rank matrix optimization
Zhihui Zhu, Qiuwei Li, Gongguo Tang, and Michael B Wakin · 2018
Later among the works it cites.
Nonconvex optimization meets low-rank matrix factorization: An overview
Yuejie Chi, Yue M Lu, and Yuxin Chen · 2019
Later among the works it cites.
Perturbed proximal descent to escape saddle points for non-convex and non-smooth objective functions
Zhishen Huang and Stephen Becker · 2019
Later among the works it cites.
The non-convex geometry of low-rank matrix optimization
Qiuwei Li, Zhihui Zhu, and Gongguo Tang · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017
Cited alongside, same era.
A unified computational and statistical framework for nonconvex low-rank matrix estimation
Lingxiao Wang, Xiao Zhang, and Quanquan Gu · 2017
Cited alongside, same era.
Harnessing structures in big data via guaranteed low-rank matrix estimation: Recent theory and fast algorithms via convex and nonconvex optimization
Yudong Chen and Yuejie Chi · 2018
Cited alongside, same era.
Accelerated gradient descent escapes saddle points faster than gradient descent
Chi Jin, Praneeth Netrapalli, and Michael I Jordan · 2018
Cited alongside, same era.
Finding low-rank solutions via nonconvex matrix factorization, efficiently and provably
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, and Sujay Sanghavi · 2018
Cited alongside, same era.
Dropping convexity for faster semi-definite optimization
Srinadh Bhojanapalli, Anastasios Kyrillidis, and Sujay Sanghavi
Cited in the paper.
Global optimality of local search for low rank matrix recovery
Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro
Cited in the paper.
Later among the works it cites.
Sharp restricted isometry bounds for the inexistence of spurious local minima in nonconvex matrix recovery
Richard Y Zhang, Somayeh Sojoudi, and Javad Lavaei · 2019
Later among the works it cites.
An equivalence between critical points for rank constraints versus low-rank factorizations
Wooseok Ha, Haoyang Liu, and Rina Foygel Barber · 2020
Later among the works it cites.
On the absence of spurious local minima in nonlinear low-rank matrix recovery problems
Yingjie Bi and Javad Lavaei · 2021
Closest in time.
The global optimization geometry of low-rank matrix optimization
Zhihui Zhu, Qiuwei Li, Gongguo Tang, and Michael B Wakin · 2021
Closest in time.