On support sizes of restricted isometry constants
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Publication:711053
DOI10.1016/j.acha.2010.05.001zbMath1197.94027OpenAlexW2164717810MaRDI QIDQ711053
Andrew Thompson, Jeffrey D. Blanchard
Publication date: 25 October 2010
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2010.05.001
sparse approximationcompressed sensingrestricted isometry propertysparse signal recoveryrestricted isometry constants
Linear programming (90C05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Decoding (94B35)
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Cites Work
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- Phase transitions for greedy sparse approximation algorithms
- Iterative hard thresholding for compressed sensing
- The restricted isometry property and its implications for compressed sensing
- Sparse recovery by non-convex optimization - instance optimality
- A note on guaranteed sparse recovery via \(\ell_1\)-minimization
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- High-dimensional centrally symmetric polytopes with neighborliness proportional to dimension
- Compressed Sensing: How Sharp Is the Restricted Isometry Property?
- Decoding by Linear Programming
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse
- Shifting Inequality and Recovery of Sparse Signals
- New Bounds for Restricted Isometry Constants
- Sparse nonnegative solution of underdetermined linear equations by linear programming
- For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing