A superlinearly convergent \(R\)-regularized Newton scheme for variational models with concave sparsity-promoting priors
From MaRDI portal
Publication:2436684
DOI10.1007/s10589-013-9583-2zbMath1286.90116OpenAlexW2105140177WikidataQ115155141 ScholiaQ115155141MaRDI QIDQ2436684
Publication date: 25 February 2014
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-013-9583-2
Related Items (8)
The jump set under geometric regularisation. II: Higher-order approaches ⋮ Linearly Constrained Non-Lipschitz Optimization for Image Restoration ⋮ Limiting Aspects of Nonconvex ${TV}^{\phi}$ Models ⋮ Inertial alternating generalized forward-backward splitting for image colorization ⋮ A unified primal dual active set algorithm for nonconvex sparse recovery ⋮ Smoothing Newton method for \(\ell^0\)-\(\ell^2\) regularized linear inverse problem ⋮ The Jump Set under Geometric Regularization. Part 1: Basic Technique and First-Order Denoising ⋮ Bilevel optimization for calibrating point spread functions in blind deconvolution
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A measure space approach to optimal source placement
- Smoothing methods for nonsmooth, nonconvex minimization
- Elliptic optimal control problems with \(L^1\)-control cost and applications for the placement of control devices
- Solution of monotone complementarity problems with locally Lipschitzian functions
- Asymptotics for Lasso-type estimators.
- A nonsmooth version of Newton's method
- Asymptotic properties of bridge estimators in sparse high-dimensional regression models
- On the minimization of a Tikhonov functional with a non-convex sparsity constraint
- Atomic Decomposition by Basis Pursuit
- A Bilevel Optimization Approach for Parameter Learning in Variational Models
- Nonconvex TV$^q$-Models in Image Restoration: Analysis and a Trust-Region Regularization--Based Superlinearly Convergent Solver
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
- Variational Analysis in Sobolev andBVSpaces
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Split Bregman Methods and Frame Based Image Restoration
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Iteratively reweighted least squares minimization for sparse recovery
- On the Convergence of the Lagged Diffusivity Fixed Point Method in Total Variation Image Restoration
- The Primal-Dual Active Set Strategy as a Semismooth Newton Method
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Trust Region Methods
- Iterative Methods for Total Variation Denoising
- Training a Support Vector Machine in the Primal
- A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
- Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction
- Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
- Robust Estimation of a Location Parameter
- Analysis of the Recovery of Edges in Images and Signals by Minimizing Nonconvex Regularized Least-Squares
- Mathematical problems in image processing. Partial differential equations and the calculus of variations. Foreword by Olivier Faugeras
This page was built for publication: A superlinearly convergent \(R\)-regularized Newton scheme for variational models with concave sparsity-promoting priors
