A Scale-Invariant Approach for Sparse Signal Recovery
DOI10.1137/18M123147XzbMath1427.94050arXiv1812.08852OpenAlexW2990337753WikidataQ126748193 ScholiaQ126748193MaRDI QIDQ5204007
Chao Wang, Yifei Lou, Hongbo Dong, Yaghoub Rahimi
Publication date: 9 December 2019
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1812.08852
sparsityalternating direction method of multipliersMRI reconstructionnull space property\(L_0\)\(L_1\)
Applications of mathematical programming (90C90) Numerical optimization and variational techniques (65K10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical methods of relaxation type (49M20) Inverse problems in optimal control (49N45)
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Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A unified approach to model selection and sparse recovery using regularized least squares
- Nonlinear total variation based noise removal algorithms
- Theory of compressive sensing via \(\ell_1\)-minimization: a non-RIP analysis and extensions
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Computing sparse representation in a highly coherent dictionary based on difference of \(L_1\) and \(L_2\)
- Global convergence of ADMM in nonconvex nonsmooth optimization
- Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Ratio and difference of \(l_1\) and \(l_2\) norms and sparse representation with coherent dictionaries
- Minimization of transformed \(l_1\) penalty: closed form representation and iterative thresholding algorithms
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- A Generalized Forward-Backward Splitting
- A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications
- Coherence Pattern–Guided Compressive Sensing with Unresolved Grids
- Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
- Certifying the Restricted Isometry Property is Hard
- Compressed sensing and best 𝑘-term approximation
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- The Split Bregman Method for L1-Regularized Problems
- A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
- Global Convergence of Splitting Methods for Nonconvex Composite Optimization
- Sparse representations in unions of bases
- Uncertainty principles and ideal atomic decomposition
- First-Order Methods in Optimization
- Decomposition Methods for Computing Directional Stationary Solutions of a Class of Nonsmooth Nonconvex Optimization Problems
- Sparse Approximate Solutions to Linear Systems
- Edge Guided Reconstruction for Compressive Imaging
- Likelihood-Based Selection and Sharp Parameter Estimation
- Comparing Measures of Sparsity
- Convergence of alternating direction method for minimizing sum of two nonconvex functions with linear constraints
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing
- Computational Aspects of Constrained L 1-L 2 Minimization for Compressive Sensing
- Towards a Mathematical Theory of Super‐resolution
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- Stable signal recovery from incomplete and inaccurate measurements