Primal and dual alternating direction algorithms for \(\ell _{1}\)-\(\ell _{1}\)-norm minimization problems in compressive sensing
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Publication:1946621
DOI10.1007/s10589-012-9475-xzbMath1269.90081OpenAlexW2079654589MaRDI QIDQ1946621
Soon-Yi Wu, Hong Zhu, Yun-hai Xiao
Publication date: 15 April 2013
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-012-9475-x
dual problemalternating direction methodaugmented Lagrangian functioncompressive sensingsparse optimization
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Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Operator splittings, Bregman methods and frame shrinkage in image processing
- A coordinate gradient descent method for \(\ell_{1}\)-regularized convex minimization
- A unified primal-dual algorithm framework based on Bregman iteration
- Removing multiplicative noise by Douglas-Rachford splitting methods
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- Some convergence properties of a method of multipliers for linearly constrained monotone variational inequalities
- Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities
- A new inexact alternating directions method for monotone variational inequalities
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Quantitative robust uncertainty principles and optimally sparse decompositions
- Atomic Decomposition by Basis Pursuit
- Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
- NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- The Split Bregman Method for L1-Regularized Problems
- 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?
- Probing the Pareto Frontier for Basis Pursuit Solutions
- Numerical Optimization
- Sparse Reconstruction by Separable Approximation
- Dense Error Correction Via $\ell^1$-Minimization
- 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
- Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
- A descent method for structured monotone variational inequalities
- Compressed sensing
- Benchmarking optimization software with performance profiles.
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