Optimality condition and iterative thresholding algorithm for $$l_p$$ l p -regularization problems
نویسندگان
چکیده
منابع مشابه
Regularization: Convergence of Iterative Half Thresholding Algorithm
In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, Lq regularization with q ∈ (0, 1)) have been demonstrated to possess capability of gaining much benefit in sparsity-inducing and efficiency. As compared with the convex regularization approaches (say, L1 regularization), however, the convergence issue of the corresponding algorithms are more difficult t...
متن کاملOptimality condition and iterative thresholding algorithm for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_p$$\end{document}lp-regularization problems
where A ∈ Rm×n, b ∈ R, ∈ (0,∞), �s�p = ∑n i=1 |si|, p ∈ (0, 1). The problem (1) has a broad applications in compressive sensing, variable selection problems and sparse least squares fitting for high dimensional data (see Chartrand and Staneva 2008; Fan and Li 2001; Foucart and Lai 2009; Frank and Freidman 1993; Ge et al. 2011; Huang et al. 2008; Knight and Wu 2000; Lai and Wang 2011; Natarajan ...
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ژورنال
عنوان ژورنال: SpringerPlus
سال: 2016
ISSN: 2193-1801
DOI: 10.1186/s40064-016-3516-3