A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations
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Publication:2211058
DOI10.1016/j.rinam.2019.100011zbMath1458.94133arXiv1710.07348OpenAlexW2977234408WikidataQ127205026 ScholiaQ127205026MaRDI QIDQ2211058
Publication date: 10 November 2020
Published in: Results in Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1710.07348
Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Information theory (general) (94A15)
Related Items (5)
Distribution agnostic Bayesian compressive sensing with incremental support estimation ⋮ A novel regularization based on the error function for sparse recovery ⋮ A non-convex piecewise quadratic approximation of \(\ell_0\) regularization: theory and accelerated algorithm ⋮ Towards optimal sampling for learning sparse approximation in high dimensions ⋮ Analysis of the ratio of \(\ell_1\) and \(\ell_2\) norms in compressed sensing
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