54 citations · 76 across the 8 of their papers we have counts for
6 papers · 1 filter
A dual based semismooth Newton method for a class of sparse Tikhonov regularization
Ning Zhang
It is well known that Tikhonov regularization is one of the most commonly used methods for solving ill-posed problems. One of the most widely applied approaches is based on constru…
A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems
Yangjing Zhang, Ning Zhang, Defeng Sun +1
Undirected graphical models have been especially popular for learning the conditional independence structure among a large number of variables where the observations are drawn inde…
An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems
Ning Zhang, Yangjing Zhang, Defeng Sun +1
Nowadays, analysing data from different classes or over a temporal grid has attracted a great deal of interest. As a result, various multiple graphical models for learning a collec…
A Unified Algorithmic Framework of Symmetric Gauss-Seidel Decomposition based Proximal ADMMs for Convex Composite Programming
Liang Chen, Defeng Sun, Kim-Chuan Toh +1
This paper aims to present a fairly accessible generalization of several symmetric Gauss-Seidel decomposition based multi-block proximal alternating direction methods of multiplier…
An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems
Yangjing Zhang, Ning Zhang, Defeng Sun +1
The sparse group Lasso is a widely used statistical model which encourages the sparsity both on a group and within the group level. In this paper, we develop an efficient augmented…
A Linearly Convergent Majorized ADMM with Indefinite Proximal Terms for Convex Composite Programming and Its Applications
Ning Zhang, Jia Wu, Liwei Zhang
This paper aims to study a majorized alternating direction method of multipliers with indefinite proximal terms (iPADMM) for convex composite optimization problems. We show that th…