54 citations · 76 across the 8 of their papers we have counts for
8 papers
Robust and Sparse Portfolio Selection: Quantitative Insights and Efficient Algorithms
J. Chen, S. D. Ahipaşaoğlu, N. Zhang +1
We extend the classical mean-variance (MV) framework and propose a robust and sparse portfolio selection model incorporating an ellipsoidal uncertainty set to reduce the impact of…
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…
Sparse learning of maximum likelihood model for optimization of complex loss function
Ning Zhang, Prathamesh Chandrasekar
Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction perfo…