Fetching the paper…
Reading the bibliography…
In this paper, we develop a variant of the well-known Gauss-Newton (GN) method to solve a class of nonconvex optimization problems involving low-rank matrix variables.
A singular value thresholding algorithm for matrix completion
J.-F. Cai, E. J. Candes, and Z. Shen · 1956
Earlier work this paper cites.
Multiplier and gradient methods
M. R. Hestenes · 1969
Earlier work this paper cites.
The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate
G. H. Golub and V. Pereyra · 1973
Earlier work this paper cites.
Matrix completion problems: a survey
Charles R Johnson · 1990
Earlier work this paper cites.
Sparse approximate solutions to linear systems
Balas Kausik Natarajan · 1995
Earlier work this paper cites.
Constrained Optimization and Lagrange Multiplier Methods
Dimitri P. Bertsekas · 1996
Earlier work this paper cites.
Numerical Methods for Least Squares Problems
A. Björck · 1996
Earlier work this paper cites.
Matrix Computations
G.H. Golub and C.F. van Loan · 1996
Earlier work this paper cites.
Matrix rank minimization with applications
Maryam Fazel · 2002
Earlier work this paper cites.
A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
S. Burer and R. DC. Monteiro · 2003
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2004
Earlier work this paper cites.
Matrix mathematics
D.S. Bernstein · 2005
Earlier work this paper cites.
Accelerating the Lee-Seung algorithm for non-negative matrix factorization
Edward F Gonzalez and Yin Zhang · 2005
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
E. Candes, J. Romberg, and T. Tao · 2006
Earlier work this paper cites.
Newton Methods for Nonlinear Problems – Affine Invariance and Adaptative Algorithms
P. Deuflhard · 2006
Earlier work this paper cites.
Numerical Optimization
J. Nocedal and S.J. Wright · 2006
Earlier work this paper cites.
Generalized inverses of linear transformations
Stephen L Campbell and Carl D Meyer · 2009
Cited alongside, same era.
A gradient descent algorithm on the Grassman manifold for matrix completion
Raghunandan H Keshavan and Sewoong Oh · 2009
Cited alongside, same era.
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
Z. Lin, M. Chen, L. Wu, and Y. Ma · 2009
Cited alongside, same era.
On the local quadratic convergence of the primal–dual augmented lagrangian method
R. A. Polyak · 2009
Cited alongside, same era.
Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
Cited alongside, same era.
Primal-dual algorithm for convex models and applications to image restoration, registration and nonlocal inpainting
J. E. Esser · 2010
Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
Y. Shen, Z. Wen, and Y. Zhang · 2012
Later among the works it cites.
Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
Z. Wen, W. Yin, and Y. Zhang · 2012
Later among the works it cites.
A literature survey of low-rank tensor approximation techniques
Lars Grasedyck, Daniel Kressner, and Christine Tobler · 2013
Later among the works it cites.
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
M. Jaggi · 2013
Later among the works it cites.
Low-rank matrix completion by Riemannian optimization
Bart Vandereycken · 2013
Later among the works it cites.
Matrix recipes for hard thresholding methods
A. Kyrillidis and V. Cevher · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Quantum state tomography via compressed sensing
D. Gross, Y.-K. Liu, S. Flammia, S. Becker, and J. Eisert · 2010
Cited alongside, same era.
Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
B. Recht, M. Fazel, and P.A. Parrilo · 2010
Cited alongside, same era.
An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems
K.-C. Toh and S. Yun · 2010
Cited alongside, same era.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
Cited alongside, same era.
Phase retrieval via matrix completion
E. J. Candes, Y. Eldar, T. Strohmer, and V. Voroninski · 2011
Cited alongside, same era.
Robust principal component analysis?
E.J. Candés, X. Li, Y. Ma, and J. Wright · 2011
Cited alongside, same era.
Later among the works it cites.
Learning with Tensors: a framework based on convex optimization and spectral regularization
M. Signoretto, Q. Tran-Dinh, L. De-Lathauwer, and J.A.K. Suykens · 2014
Later among the works it cites.
Parallel matrix factorization for recommender systems
Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, and Inderjit S Dhillon · 2014
Later among the works it cites.
Fast gradient algorithms for structured sparsity
Y. Yu · 2014
Later among the works it cites.
Dropping convexity for faster semi-definite optimization
Srinadh Bhojanapalli, Anastasios Kyrillidis, and Sujay Sanghavi · 2015
Later among the works it cites.
Structured sparsity: Discrete and convex approaches
A. Kyrillidis, L. Baldassarre, M. El-Halabi, Q. Tran-Dinh, and V. Cevher · 2015
Later among the works it cites.
Global convergence of splitting methods for nonconvex composite optimization
G. Li and T.-K. Pong · 2015
Later among the works it cites.
An efficient Gauss-Newton algorithm for symmetric low-rank product matrix approximations
X. Liu, Z. Wen, and Y. Zhang · 2015
Later among the works it cites.
Global convergence of ADMM in nonconvex nonsmooth optimization
Y. Wang, W. Yin, and J. Zeng · 2015
Later among the works it cites.
Low-rank solutions of linear matrix equations via procrustes flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Benjamin Recht · 2016
Closest in time.