Jointly low-rank and bisparse recovery: Questions and partial answers
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Publication:5220065
DOI10.1142/S0219530519410094zbMath1434.65050arXiv1902.04731WikidataQ126831598 ScholiaQ126831598MaRDI QIDQ5220065
Holger Rauhut, Laurent Jacques, Simon Foucart, Rémi Gribonval
Publication date: 10 March 2020
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1902.04731
compressive sensingsample complexityrestricted isometry propertiesiterative thresholding algorithmshead and tail projectionsrank-one measurementssimultaneity of structures
Computational methods for sparse matrices (65F50) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
Related Items
Robust sensing of low-rank matrices with non-orthogonal sparse decomposition, Riemannian thresholding methods for row-sparse and low-rank matrix recovery, Sampling schemes and recovery algorithms for functions of few coordinate variables, Hierarchical compressed sensing
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