Sparse decomposition by iterating Lipschitzian-type mappings
type: Item
instance of: scholarly article
title: Sparse decomposition by iterating Lipschitzian-type mappings
zbMATH Open document ID: 1361.65013
author: Alessandro Adamo
author: Giuliano Grossi
author: Raffaella Lanzarotti
author: Jianyi Lin
published in: Theoretical Computer Science
publication date: 2017-03-16T00:00:00Z
Mathematics Subject Classification ID: 65F10
Mathematics Subject Classification ID: 65F50
Mathematics Subject Classification ID: 90C27
zbMATH DE Number: 6695320
zbMATH Keywords: underdetermined linear systems
zbMATH Keywords: sparse recovery
zbMATH Keywords: Lipschitzian mappings
zbMATH Keywords: smooth \(\ell_0\)-norm
zbMATH Keywords: smooth \(\ell_1\)-norm
zbMATH Keywords: fixed-point iteration scheme
zbMATH Keywords: combinatorial optimization
zbMATH Keywords: numerical examples
zbMATH Keywords: fast iterative method
zbMATH Keywords: sparse solutions
Wikidata QID: Q59538784
describes a project that uses: PDCO
describes a project that uses: CoSaMP
MaRDI profile type: Publication
full work available at URL: j.tcs.2016.04.025
OpenAlex ID: W2394499918
cites work: For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
cites work: Atomic Decomposition by Basis Pursuit
cites work: Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
cites work: Sparse Approximate Solutions to Linear Systems
cites work: Matching pursuits with time-frequency dictionaries
cites work: CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
cites work: Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
cites work: A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
cites work: Sparse and Redundant Representations
cites work: Least angle regression. (With discussion)
cites work: Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
cites work: Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
cites work: From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
cites work: Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
cites work: Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
cites work: A generalized uncertainty principle and sparse representation in pairs of bases
cites work: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
cites work: Stable signal recovery from incomplete and inaccurate measurements
cites work: Q5491042
cites work: Compressed sensing and best 𝑘-term approximation
cites work: Introduction to Applied Nonlinear Dynamical Systems and Chaos
cites work: Ideal spatial adaptation by wavelet shrinkage
cites work: De-noising by soft-thresholding
cites work: Highly sparse representations from dictionaries are unique and independent of the sparseness measure
cites work: Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
DOI: 10.1016/J.TCS.2016.04.025
description: scientific article; zbMATH DE number 6695320