The matrix splitting based proximal fixed-point algorithms for quadratically constrained \(\ell_{1}\) minimization and Dantzig selector
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Publication:1686207
DOI10.1016/j.apnum.2017.11.001zbMath1379.65039OpenAlexW2767151063MaRDI QIDQ1686207
Publication date: 21 December 2017
Published in: Applied Numerical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.apnum.2017.11.001
algorithmiterative schemenumerical resultcompressive sensingDantzig selectorsparse recoveryproximity operator\(\ell_1\)-minimizationtotal-variationconvex composite modeltotal-variation minimization problems
Uses Software
Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Smooth minimization of non-smooth functions
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A unified primal-dual algorithm framework based on Bregman iteration
- The restricted isometry property and its implications for compressed sensing
- Fast linearized Bregman iteration for compressive sensing and sparse denoising
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Templates for convex cone problems with applications to sparse signal recovery
- Least angle regression. (With discussion)
- An alternating direction method for finding Dantzig selectors
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Multi-step fixed-point proximity algorithms for solving a class of optimization problems arising from image processing
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Atomic Decomposition by Basis Pursuit
- Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
- Proximity algorithms for image models: denoising
- Analysis and Generalizations of the Linearized Bregman Method
- NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Probing the Pareto Frontier for Basis Pursuit Solutions
- Sparse and Redundant Representations
- Monotone Operators and the Proximal Point Algorithm
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- De-noising by soft-thresholding
- The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
- A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
- On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
- Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Signal Recovery by Proximal Forward-Backward Splitting
- Stable signal recovery from incomplete and inaccurate measurements
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Convex analysis and monotone operator theory in Hilbert spaces
- Compressed sensing
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