Analysis of convergence for the alternating direction method applied to joint sparse recovery
From MaRDI portal
Publication:668704
DOI10.1016/j.amc.2015.07.104zbMath1410.94021OpenAlexW1242890019MaRDI QIDQ668704
Jiaxin Xie, Xiaobo Yang, Yuan Lei, An-Ping Liao
Publication date: 19 March 2019
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2015.07.104
alternating direction methodsparse recoverycompressed sensingjoint sparsitymultiple measurement vectorsrow sparse matrices
Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Inverse problems in linear algebra (15A29) Random matrices (algebraic aspects) (15B52) Numerical solution to inverse problems in abstract spaces (65J22)
Related Items
General parameterized proximal point algorithm with applications in statistical learning ⋮ Exact recovery of sparse multiple measurement vectors by \(l_{2,p}\)-minimization ⋮ Some results on OMP algorithm for MMV problem ⋮ A remark on joint sparse recovery with OMP algorithm under restricted isometry property
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- The null space property for sparse recovery from multiple measurement vectors
- The restricted isometry property and its implications for compressed sensing
- Algorithms for simultaneous sparse approximation. II: Convex relaxation
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Atomic Decomposition by Basis Pursuit
- A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
- Decoding by Linear Programming
- Stable recovery of sparse overcomplete representations in the presence of noise
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Uncertainty principles and ideal atomic decomposition
- An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
- High-Resolution Radar via Compressed Sensing
- Adaptive Compressed Sensing Radar Oriented Toward Cognitive Detection in Dynamic Sparse Target Scene
- The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range
- Greedy Algorithms for Joint Sparse Recovery
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Sparse Bayesian Learning for Basis Selection
- Sparse solutions to linear inverse problems with multiple measurement vectors
- A sparse signal reconstruction perspective for source localization with sensor arrays
