Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms
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Publication:6041666
DOI10.1007/s11425-021-1987-2zbMath1518.90111arXiv2007.01451OpenAlexW4309775521MaRDI QIDQ6041666
Publication date: 12 May 2023
Published in: Science China. Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.01451
compressed sensingiterative hard thresholdingrestricted isometry propertyguaranteed performancecompressive sampling matching pursuit
Nonlinear programming (90C30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Inverse problems in linear algebra (15A29) Iterative numerical methods for linear systems (65F10)
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Cites Work
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- Best subset selection via a modern optimization lens
- A mathematical introduction to compressive sensing
- Iterative hard thresholding for compressed sensing
- Iterative thresholding for sparse approximations
- Hard thresholding pursuit algorithms: number of iterations
- Iterative thresholding algorithms
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit
- A simple proof of the restricted isometry property for random matrices
- Uniform uncertainty principle for Bernoulli and subgaussian ensembles
- Iterative hard thresholding for low-rank recovery from rank-one projections
- Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms
- A Generalized Class of Hard Thresholding Algorithms for Sparse Signal Recovery
- Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
- Decoding by Linear Programming
- Why Simple Shrinkage Is Still Relevant for Redundant Representations?
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Sparse and Redundant Representations
- Ideal spatial adaptation by wavelet shrinkage
- A Tight Bound of Hard Thresholding
- Linear Convergence of Stochastic Iterative Greedy Algorithms With Sparse Constraints
- Sparse Optimization Theory and Methods
- RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT
- RSP-Based Analysis for Sparsest and Least $\ell_1$-Norm Solutions to Underdetermined Linear Systems
- Subspace Pursuit for Compressive Sensing Signal Reconstruction
- Between hard and soft thresholding: optimal iterative thresholding algorithms
- Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery
- Optimal Variable Selection and Adaptive Noisy Compressed Sensing
- Optimal $k$-Thresholding Algorithms for Sparse Optimization Problems
- Weak Stability of ℓ1-Minimization Methods in Sparse Data Reconstruction
- Compressed Sensing and Its Applications
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Oblique Pursuits for Compressed Sensing
- Compressed sensing