A new smoothed L0 regularization approach for sparse signal recovery
From MaRDI portal
Publication:2298142
DOI10.1155/2019/1978154zbMath1435.94093OpenAlexW2960155404MaRDI QIDQ2298142
Jianhong Xiang, Linyu Wang, Xiangjun Yin, Huihui Yue
Publication date: 20 February 2020
Published in: Mathematical Problems in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2019/1978154
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Uses Software
Cites Work
- A spectral PRP conjugate gradient methods for nonconvex optimization problem based on modified line search
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- The restricted isometry property and its implications for compressed sensing
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- Convergence property of the Fletcher-Reeves conjugate gradient method with errors
- Atomic Decomposition by Basis Pursuit
- Robust principal component analysis?
- Practical Optimization
- Decoding by Linear Programming
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Greedy Basis Pursuit
- A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
- Generalized Orthogonal Matching Pursuit
- Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
- Subspace Pursuit for Compressive Sensing Signal Reconstruction
- Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit
- Compressed sensing
