The \(\ell_{2,q}\) regularized group sparse optimization: lower bound theory, recovery bound and algorithms
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Publication:778013
DOI10.1016/j.acha.2020.04.002zbMath1448.94058OpenAlexW3021333344MaRDI QIDQ778013
Publication date: 30 June 2020
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2020.04.002
Ridge regression; shrinkage estimators (Lasso) (62J07) Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items (3)
A General Non-Lipschitz Infimal Convolution Regularized Model: Lower Bound Theory and Algorithm ⋮ Convergence and stability analysis of iteratively reweighted least squares for noisy block sparse recovery ⋮ The \(\ell_{2,p}\) regularized total variation with overlapping group sparsity prior for image restoration with impulse noise
Uses Software
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