Sharp RIP bound for sparse signal and low-rank matrix recovery

2012/08/09 by T. Tony Cai, Tommaso Cai, Anru R. Zhang +1 · 4 citations
Engineering · Mathematics · #Mathematical Analysis and Transform Methods #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.1016/j.acha.2012.07.010

openalex publication_date 2012/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Citations

• Nearly unbiased variable selection under minimax concave penalty
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Ideal spatial adaptation by wavelet shrinkage
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

Cited by

• Efficient iterative thresholding algorithms with functional feedbacks and convergence analysis
• Global and Local Analyses of Nonlinear Low-Rank Matrix Recovery Problems
• Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence
• Reconstruction Error Bounds for Compressed Sensing under Poisson Noise using the Square Root of the Jensen-Shannon Divergence