The Dantzig selector: recovery of signal via ℓ 1 − αℓ 2 minimization
From MaRDI portal
Publication:5014489
DOI10.1088/1361-6420/ac39f8zbMath1479.94063arXiv2105.14229OpenAlexW4200182950MaRDI QIDQ5014489
Publication date: 8 December 2021
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2105.14229
Dantzig selectorrestricted isometry propertysparse signal recovery\(\ell_1-\alpha\ell_2\) minimization
Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Point source super-resolution via non-convex \(L_1\) based methods
- A mathematical introduction to compressive sensing
- Computing sparse representation in a highly coherent dictionary based on difference of \(L_1\) and \(L_2\)
- On support sizes of restricted isometry constants
- MUSIC for single-snapshot spectral estimation: stability and super-resolution
- Global convergence of ADMM in nonconvex nonsmooth optimization
- Fast L1-L2 minimization via a proximal operator
- An alternating direction method for finding Dantzig selectors
- Unconstrained \(\ell_1\)-\(\ell_2\) minimization for sparse recovery via mutual coherence
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization
- ROP: matrix recovery via rank-one projections
- PhaseLiftOff: an accurate and stable phase retrieval method based on difference of trace and Frobenius norms
- Further properties of the forward-backward envelope with applications to difference-of-convex programming
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- High-dimensional graphs and variable selection with the Lasso
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- Coherence Pattern–Guided Compressive Sensing with Unresolved Grids
- Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
- Restricted isometry properties and nonconvex compressive sensing
- Decoding by Linear Programming
- Stable recovery of sparse overcomplete representations in the presence of noise
- Sparse and Redundant Representations
- Uncertainty principles and ideal atomic decomposition
- On $l_q$ Optimization and Matrix Completion
- The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
- ℓ 1 − αℓ 2 minimization methods for signal and image reconstruction with impulsive noise removal
- Uniform RIP Conditions for Recovery of Sparse Signals by $\ell _p\,(0< p\leq 1)$ Minimization
- Sparse Phase Retrieval Via PhaseLiftOff
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Compressive radar with off-grid targets: a perturbation approach
- Stable signal recovery from incomplete and inaccurate measurements
- New Restricted Isometry Property Analysis for $\ell_1-\ell_2$ Minimization Methods
