Scaled proximal gradient methods for sparse optimization problems
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Publication:6184263
DOI10.1007/s10915-023-02393-1zbMath1530.65063OpenAlexW4388909717MaRDI QIDQ6184263
Guiyun Xiao, Zheng-Jian Bai, Shu-Qin Zhang
Publication date: 5 January 2024
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-023-02393-1
Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Inverse problems in linear algebra (15A29) Iterative numerical methods for linear systems (65F10)
Cites Work
- Unnamed Item
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- Best subset selection via a modern optimization lens
- A mathematical introduction to compressive sensing
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Iterative hard thresholding for compressed sensing
- Iterative thresholding for sparse approximations
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- A family of variable metric proximal methods
- A geometric proximal gradient method for sparse least squares regression with probabilistic simplex constraint
- Coordinate descent algorithms
- Constructing New Weighted ℓ1-Algorithms for the Sparsest Points of Polyhedral Sets
- Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Why Simple Shrinkage Is Still Relevant for Redundant Representations?
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Sparse and Redundant Representations
- Atomic Decomposition by Basis Pursuit
- Variational Analysis
- Sparse Optimization Theory and Methods
- First-Order Methods in Optimization
- Stochastic Geometry for Wireless Networks
- Sparse Approximate Solutions to Linear Systems
- De-noising by soft-thresholding
- Matching pursuits with time-frequency dictionaries
- Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
- Subspace Pursuit for Compressive Sensing Signal Reconstruction
- Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery
- Optimal $k$-Thresholding Algorithms for Sparse Optimization Problems
- Compressed sensing
- Sparse Image and Signal Processing
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