Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative...
Main Authors: | Recht, Benjamin, Fazel, Maryam, Parrilo, Pablo A. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
Society of Industrial and Applied Mathematics (SIAM)
2011
|
Online Access: | http://hdl.handle.net/1721.1/60575 https://orcid.org/0000-0003-1132-8477 |
Similar Items
-
Lower bounds on nonnegative rank via nonnegative nuclear norms
by: Fawzi, Hamza, et al.
Published: (2016) -
The effect of perturbation and noise folding on the recovery performance of low-rank matrix via the nuclear norm minimization
by: Zahia Aidene, et al.
Published: (2022-01-01) -
Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
by: Yaru Su, et al.
Published: (2019-01-01) -
Nuclear norm penalized LAD estimator for low rank matrix recovery
by: Wei, Wenzhe
Published: (2015) -
Rank-Sparsity Incoherence for Matrix Decomposition
by: Chandrasekaran, Venkat, et al.
Published: (2011)