12 citations · 16 across the 6 of their papers we have counts for
5 papers · 1 filter
iNALM: An inexact Newton Augmented Lagrangian Method for Zero-One Composite Optimization
Penghe Zhang, Naihua Xiu, Hou-Duo Qi
Zero-One Composite Optimization (0/1-COP) is a prototype of nonsmooth, nonconvex optimization problems and it has attracted much attention recently. The augmented Lagrangian Method…
Proximal Operator and Optimality Conditions for Ramp Loss SVM
Huajun Wang, Yuanhai Shao, Naihua Xiu
Support vector machines with ramp loss (dubbed as -SVM) have attracted wide attention due to the boundedness of ramp loss. However, the corresponding optimization problem is n…
Support Vector Machine Classifier via Soft-Margin Loss
Huajun Wang, Yuanhai Shao, Shenglong Zhou +2
Support vector machine (SVM) has attracted great attentions for the last two decades due to its extensive applications, and thus numerous optimization models have been proposed. To…
A Lagrangian Dual Based Approach to Sparse Linear Programming
Chen Zhao, Ziyan Luo, Weiyue Li +2
A sparse linear programming (SLP) problem is a linear programming problem equipped with a sparsity (or cardinality) constraint, which is nonconvex and discontinuous theoretically a…
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method
Ziyan Luo, Defeng Sun, Kim-Chuan Toh +1
The octagonal shrinkage and clustering algorithm for regression (OSCAR), equipped with the -norm and a pair-wise -norm regularizer, is a useful tool for feat…