Iterative thresholding algorithm based on non-convex method for modified \(l_p\)-norm regularization minimization
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Publication:1631426
DOI10.1016/j.cam.2018.08.021zbMath1410.90162arXiv1804.09385OpenAlexW2963438018MaRDI QIDQ1631426
Junxiong Jia, Meng Wen, Angang Cui, Hai-yang Li, Ji-Gen Peng
Publication date: 6 December 2018
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1804.09385
Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Numerical methods of relaxation type (49M20)
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Cites Work
- Iterative thresholding for sparse approximations
- \(S_{1/2}\) regularization methods and fixed point algorithms for affine rank minimization problems
- Complexity of unconstrained \(L_2 - L_p\) minimization
- Optimality Conditions and a Smoothing Trust Region Newton Method for NonLipschitz Optimization
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- The Split Bregman Method for L1-Regularized Problems
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- De-noising by soft-thresholding
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
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