109 citations · 565 across the 64 of their papers we have counts for
5 papers · 2 filters
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…
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems
Peipei Tang, Chengjing Wang, Defeng Sun +1
In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for these problems. Ou…
An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming
Xudong Li, Defeng Sun, Kim-Chuan Toh
Powerful interior-point methods (IPM) based commercial solvers, such as Gurobi and Mosek, have been hugely successful in solving large-scale linear programming (LP) problems. The h…
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 dual Newton based preconditioned proximal point algorithm for exclusive lasso models
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regulari…