A conjugate subgradient algorithm with adaptive preconditioning for the least absolute shrinkage and selection operator minimization
DOI10.1134/S0965542517040066zbMath1378.65091arXiv1506.07730MaRDI QIDQ1682919
Publication date: 6 December 2017
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1506.07730
regularizationcomputed tomographynumerical examplesleast absolute shrinkageadaptive preaondibioningconjugate subgradient algorithmill-conditioned linear problems
Numerical solutions to overdetermined systems, pseudoinverses (65F20) Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical mathematical programming methods (65K05) Convex programming (90C25) Biomedical imaging and signal processing (92C55) Preconditioners for iterative methods (65F08)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nonlinear total variation based noise removal algorithms
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Proximal Splitting Methods in Signal Processing
- Optimization with Sparsity-Inducing Penalties
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
- Stable signal recovery from incomplete and inaccurate measurements
This page was built for publication: A conjugate subgradient algorithm with adaptive preconditioning for the least absolute shrinkage and selection operator minimization
