Approximately normalized iterative hard thresholding for nonlinear compressive sensing
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Publication:1792868
DOI10.1155/2016/2594752zbMath1400.94081OpenAlexW2509516755WikidataQ59131168 ScholiaQ59131168MaRDI QIDQ1792868
Publication date: 12 October 2018
Published in: Mathematical Problems in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2016/2594752
Convex programming (90C25) Applications of mathematical programming (90C90) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)
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Cites Work
- A mathematical introduction to compressive sensing
- On performance of greedy algorithms
- Iterative hard thresholding for compressed sensing
- Iterative thresholding for sparse approximations
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms
- On the Evaluation Complexity of Cubic Regularization Methods for Potentially Rank-Deficient Nonlinear Least-Squares Problems and Its Relevance to Constrained Nonlinear Optimization
- A New and Improved Quantitative Recovery Analysis for Iterative Hard Thresholding Algorithms in Compressed Sensing
- Compressed Sensing With Nonlinear Observations and Related Nonlinear Optimization Problems
- Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
- Decoding by Linear Programming
- GESPAR: Efficient Phase Retrieval of Sparse Signals
- CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion
- Matching pursuits with time-frequency dictionaries
- Subspace Pursuit for Compressive Sensing Signal Reconstruction
- Compressed sensing
- Adaptive greedy approximations