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Augmented Lagrangian method (also called as method of multipliers) is an important and powerful optimization method for lots of smooth or nonsmooth variational problems in modern signal processing, imaging, optimal control and so on. However, one usually needs to solve the coupled and nonlinear system together and simultaneously, which is very challenging. In this paper, we proposed several semismooth Newton methods to solve the nonlinear subproblems arising in image restoration, which leads to several highly efficient and competitive algorithms for imaging processing. With the analysis of the metric subregularities of the corresponding functions, we give both the global convergence and local linear convergence rate for the proposed augmented Lagrangian methods with semismooth Newton solvers.
Total generalization variation (TGV) is a very powerful and important regularization for various inverse problems and computer vision tasks. In this paper, we proposed a semismooth Newton based augmented Lagrangian method to solve this problem. The a
Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications. Previous work has demonstrated the superiority of the SVMs in dealing with the high dimensional, low sample size problems. However, the numeri
The octagonal shrinkage and clustering algorithm for regression (OSCAR), equipped with the $ell_1$-norm and a pair-wise $ell_{infty}$-norm regularizer, is a useful tool for feature selection and grouping in high-dimensional data analysis. The computa
This paper is devoted to studying an inexact augmented Lagrangian method for solving a class of manifold optimization problems, which have non-smooth objective functions and non-negative constraints. Under the constant positive linear dependence cond
Support vector machine (SVM) has proved to be a successful approach for machine learning. Two typical SVM models are the L1-loss model for support vector classification (SVC) and $epsilon$-L1-loss model for support vector regression (SVR). Due to the