An investigation on semismooth Newton based augmented Lagrangian method for image restoration
From MaRDI portal
Publication:2162237
DOI10.1007/s10915-022-01907-7zbMath1492.65173arXiv1911.10968OpenAlexW2989821678MaRDI QIDQ2162237
Publication date: 5 August 2022
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.10968
augmented Lagrangian methodmetric subregularitysemismooth Newton methodlocal linear convergence rate
Numerical optimization and variational techniques (65K10) Newton-type methods (49M15) Nonsmooth analysis (49J52) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Metric subregularity and the proximal point method
- Nonsmooth equations in optimization. Regularity, calculus, methods and applications
- A semismooth equation approach to the solution of nonlinear complementarity problems
- The Schur complement and its applications
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems
- An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems
- On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming
- Multiplier and gradient methods
- Splitting Methods in Communication, Imaging, Science, and Engineering
- Introduction to Piecewise Differentiable Equations
- A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
- Asymptotic Convergence Analysis of the Proximal Point Algorithm
- Lagrange Multiplier Approach to Variational Problems and Applications
- Some continuity properties of polyhedral multifunctions
- Monotone Operators and the Proximal Point Algorithm
- Semismooth and Semiconvex Functions in Constrained Optimization
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- An active set strategy based on the augmented Lagrangian formulation for image restoration
- Newton and Quasi-Newton Methods for a Class of Nonsmooth Equations and Related Problems
- A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems
- Semismooth Newton and Augmented Lagrangian Methods for a Simplified Friction Problem
- Constrained Minima and Lipschitzian Penalties in Metric Spaces
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Iterative Krylov Methods for Large Linear Systems
- Total Bounded Variation Regularization as a Bilaterally Constrained Optimization Problem
- Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization
- Implicit Functions and Solution Mappings
- An Infeasible Primal-Dual Algorithm for Total Bounded Variation--Based Inf-Convolution-Type Image Restoration
- Convex analysis and monotone operator theory in Hilbert spaces
