$ \newcommand{\e}{{\rm e}} {\alpha\ell_{1}-\beta\ell_{2}}$ regularization for sparse recovery
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Publication:5210419
DOI10.1088/1361-6420/ab34b5zbMath1485.65138OpenAlexW2962969787MaRDI QIDQ5210419
Publication date: 20 January 2020
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1361-6420/ab34b5
Related Items (5)
Generalized conditional gradient method for elastic-net regularization ⋮ Morozov's discrepancy principle for \(\alpha\ell_1-\beta\ell_2\) sparsity regularization ⋮ A projected gradient method for αℓ 1 − βℓ 2 sparsity regularization ** ⋮ Heuristic discrepancy principle for variational regularization of inverse problems ⋮ A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford--Shah Color and Multiphase Image Segmentation
Uses Software
Cites Work
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- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Regularization methods in Banach spaces.
- A generalized conditional gradient method and its connection to an iterative shrinkage method
- Non-convex sparse regularisation
- Variational methods in imaging
- Theoretical foundations and numerical methods for sparse recovery. Papers based on the presentations of the summer school ``Theoretical foundations and numerical methods for sparse recovery, Vienna, Austria, August 31 -- September 4, 2009.
- Global convergence of ADMM in nonconvex nonsmooth optimization
- Well-posedness and convergence rates for sparse regularization with sublinear \(l^q\) penalty term
- Tikhonov regularization with \({\ell^{0}}\)-term complementing a convex penalty: \({\ell^{1}}\)-convergence under sparsity constraints
- Minimization of non-smooth, non-convex functionals by iterative thresholding
- On the minimization of a Tikhonov functional with a non-convex sparsity constraint
- Regularization tools version \(4.0\) for matlab \(7.3\)
- Nonconvex sorted \(\ell_1\) minimization for sparse approximation
- Splitting Methods in Communication, Imaging, Science, and Engineering
- Multi-parameter Tikhonov regularization with the ℓ 0 sparsity constraint
- Description of the Minimizers of Least Squares Regularized with $\ell_0$-norm. Uniqueness of the Global Minimizer
- A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications
- Flexible sparse regularization
- Elastic-net regularization versus ℓ 1 -regularization for linear inverse problems with quasi-sparse solutions
- Generalized Bregman distances and convergence rates for non-convex regularization methods
- Regularization with non-convex separable constraints
- Sparse regularization with l q penalty term
- On Tikhonov regularization with non-convex sparsity constraints
- Elastic-net regularization: error estimates and active set methods
- Variational Analysis
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Convergence rates inℓ1-regularization if the sparsity assumption fails
- Sparsity regularization for parameter identification problems
- L 1/2 regularization
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Morozov's discrepancy principle for Tikhonov-type functionals with nonlinear operators
- A variational approach to sparsity optimization based on Lagrange multiplier theory
- A generalized conditional gradient method for nonlinear operator equations with sparsity constraints
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