Sparse decomposition by iterating Lipschitzian-type mappings
From MaRDI portal
Publication:517031
DOI10.1016/j.tcs.2016.04.025zbMath1361.65013OpenAlexW2394499918WikidataQ59538784 ScholiaQ59538784MaRDI QIDQ517031
Giuliano Grossi, Alessandro Adamo, Raffaella Lanzarotti, Jianyi Lin
Publication date: 16 March 2017
Published in: Theoretical Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.tcs.2016.04.025
Lipschitzian mappingsnumerical examplescombinatorial optimizationsparse recoverysparse solutionsfast iterative methodfixed-point iteration schemesmooth \(\ell_0\)-normsmooth \(\ell_1\)-normunderdetermined linear systems
Computational methods for sparse matrices (65F50) Combinatorial optimization (90C27) Iterative numerical methods for linear systems (65F10)
Uses Software
Cites Work
- Unnamed Item
- Highly sparse representations from dictionaries are unique and independent of the sparseness measure
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Least angle regression. (With discussion)
- Compressed sensing and best 𝑘-term approximation
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
- Sparse and Redundant Representations
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Atomic Decomposition by Basis Pursuit
- Ideal spatial adaptation by wavelet shrinkage
- A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
- A generalized uncertainty principle and sparse representation in pairs of bases
- Introduction to Applied Nonlinear Dynamical Systems and Chaos
- Sparse Approximate Solutions to Linear Systems
- De-noising by soft-thresholding
- Matching pursuits with time-frequency dictionaries
- Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Stable signal recovery from incomplete and inaccurate measurements
