Fast and Accurate Algorithms for Re-Weighted $\ell _{1}$-Norm Minimization

DOI10.1109/TSP.2013.2279362zbMath1394.94052arXiv1208.0651OpenAlexW1980079540MaRDI QIDQ4578842

M. Salman Asif, Justin Romberg

Publication date: 22 August 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1208.0651

Mathematics Subject Classification ID

Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (10)

The finite steps of convergence of the fast thresholding algorithms with \(f\)-feedbacks in compressed sensing ⋮ Framework for segmented threshold \(\ell_0\) gradient approximation based network for sparse signal recovery ⋮ A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems ⋮ Effective zero-norm minimization algorithms for noisy compressed sensing ⋮ Smoothed \(\ell_1\)-regularization-based line search for sparse signal recovery ⋮ Constructing New Weighted ℓ₁-Algorithms for the Sparsest Points of Polyhedral Sets ⋮ The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty ⋮ Nonconvex nonsmooth optimization via convex-nonconvex majorization-minimization ⋮ Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems ⋮ A New Computational Method for the Sparsest Solutions to Systems of Linear Equations

This page was built for publication: Fast and Accurate Algorithms for Re-Weighted $\ell _{1}$-Norm Minimization