Gradient-based method with active set strategy for $\ell _1$ optimization
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Publication:4605700
DOI10.1090/mcom/3238zbMath1392.90079OpenAlexW2606445905MaRDI QIDQ4605700
Publication date: 27 February 2018
Published in: Mathematics of Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1090/mcom/3238
Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Complexity and performance of numerical algorithms (65Y20)
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Sparse solutions to an underdetermined system of linear equations via penalized Huber loss, A truncated Newton algorithm for nonconvex sparse recovery, An active set Newton-CG method for \(\ell_1\) optimization, An inexact quasi-Newton algorithm for large-scale \(\ell_1\) optimization with box constraints, A preconditioned conjugate gradient method with active set strategy for \(\ell_1\)-regularized least squares, Improved SVRG for finite sum structure optimization with application to binary classification, An active set Barzilar-Borwein algorithm for \(l_0\) regularized optimization
Uses Software
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