Optimality conditions for the constrainedLp-regularization

DOI10.1080/02331934.2014.929678zbMath1332.65079OpenAlexW2060734305MaRDI QIDQ3454863

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Publication date: 27 November 2015

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2014.929678

zbMATH Keywords

constrained optimizationoptimality conditionsnonconvexnonsmoothKarush-Kuhn-Tucker pointconstrained \(L_p\)-regularization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30)

Related Items (2)

Relating \(\ell_p\) regularization and reweighted \(\ell_1\) regularization ⋮ Nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods

Cites Work

• 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
• Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization
• Restricted isometry properties and nonconvex compressive sensing
• Digital filters as absolute norm regularizers
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A new approach to variable selection in least squares problems
• Non-Lipschitz $\ell_{p}$-Regularization and Box Constrained Model for Image Restoration
• Nonlinear Programming
• Analysis of the Recovery of Edges in Images and Signals by Minimizing Nonconvex Regularized Least-Squares
• Compressed sensing

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