Optimality conditions for locally Lipschitz optimization with \(l_0\)-regularization
From MaRDI portal
Publication:1996752
DOI10.1007/s11590-020-01579-yzbMath1470.90135OpenAlexW3020910274MaRDI QIDQ1996752
Hui Zhang, Nai-Hua Xiu, Li-Li Pan
Publication date: 26 February 2021
Published in: Optimization Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11590-020-01579-y
applicationlocally Lipschitz optimization\(l_0\)-regularizationproximal-stationary pointsubdifferential-stationary point
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Relationship between the optimal solutions of least squares regularized with \(\ell_{0}\)-norm and constrained by \(k\)-sparsity
- Iterative reweighted minimization methods for \(l_p\) regularized unconstrained nonlinear programming
- Global solutions of non-Lipschitz \(S_{2}\)-\(S_{p}\) minimization over the positive semidefinite cone
- Iterative hard thresholding for compressed sensing
- Iterative thresholding for sparse approximations
- Least absolute deviations estimation for the censored regression model
- Bayes inference in the Tobit censored regression model
- Generalized subdifferentials of the rank function
- Automatic speech recognition. A deep learning approach
- Necessary optimality conditions and exact penalization for non-Lipschitz nonlinear programs
- Complexity of unconstrained \(L_2 - L_p\) minimization
- On optimal solutions of the constrained ℓ 0 regularization and its penalty problem
- Compressed Sensing With Nonlinear Observations and Related Nonlinear Optimization Problems
- Decoding by Linear Programming
- Optimization and nonsmooth analysis
- Variational Analysis
- Composite Difference-Max Programs for Modern Statistical Estimation Problems
- Variational Analysis and Applications
- Proximal Mapping for Symmetric Penalty and Sparsity
- Robust Linear Regression via $\ell_0$ Regularization
- Sparse Approximate Solutions to Linear Systems
- Sparse Approximation via Penalty Decomposition Methods
- Compressed sensing
This page was built for publication: Optimality conditions for locally Lipschitz optimization with \(l_0\)-regularization