Large sparse signal recovery by conjugate gradient algorithm based on smoothing technique
From MaRDI portal
Publication:2629501
DOI10.1016/j.camwa.2013.04.022zbMath1391.94481OpenAlexW2007353157MaRDI QIDQ2629501
Hong Zhu, Soon-Yi Wu, Yun-hai Xiao
Publication date: 6 July 2016
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.camwa.2013.04.022
conjugate gradient methodnon-smooth optimizationcompressive sensingsparse solution\(\ell_1\)-norm regularization
Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical optimization and variational techniques (65K10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13)
Related Items
Two sufficient descent three-term conjugate gradient methods for unconstrained optimization problems with applications in compressive sensing ⋮ An efficient solution of Hamiltonian boundary value problems by combined Gauss pseudospectral method with differential continuation approach ⋮ Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model ⋮ A hybrid quasi-Newton method with application in sparse recovery ⋮ Smoothing strategy along with conjugate gradient algorithm for signal reconstruction ⋮ Nonmonotone quasi-Newton-based conjugate gradient methods with application to signal processing ⋮ Group sparse recovery in impulsive noise via alternating direction method of multipliers ⋮ A perturbation analysis of nonconvex block-sparse compressed sensing ⋮ Smoothed \(\ell_1\)-regularization-based line search for sparse signal recovery ⋮ Analysis of the maximum magnification by the scaled memoryless DFP updating formula with application to compressive sensing ⋮ Signal reconstruction by conjugate gradient algorithm based on smoothing \(l_1\)-norm ⋮ An augmented memoryless BFGS method based on a modified secant equation with application to compressed sensing ⋮ Dai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Smooth minimization of non-smooth functions
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A coordinate gradient descent method for \(\ell_{1}\)-regularized convex minimization
- A box constrained gradient projection algorithm for compressed sensing
- Wavelets and curvelets for image deconvolution: a combined approach
- Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization
- Primal and dual alternating direction algorithms for \(\ell _{1}\)-\(\ell _{1}\)-norm minimization problems in compressive sensing
- Atomic Decomposition by Basis Pursuit
- Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization
- Fixed-Point Continuation Applied to Compressed Sensing: Implementation and Numerical Experiments
- NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence
- An EM algorithm for wavelet-based image restoration
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Why Simple Shrinkage Is Still Relevant for Redundant Representations?
- Probing the Pareto Frontier for Basis Pursuit Solutions
- Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse
- Two-Point Step Size Gradient Methods
- Atomic Decomposition by Basis Pursuit
- A new approach to variable selection in least squares problems
- Nonmonotone Spectral Projected Gradient Methods on Convex Sets
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
This page was built for publication: Large sparse signal recovery by conjugate gradient algorithm based on smoothing technique
