35 citations · 196 across the 27 of their papers we have counts for
27 papers
Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions
Penghe Zhang, Naihua Xiu, Hou-Duo Qi
The hard margin loss function has been at the core of the support vector machine (SVM) research from the very beginning due to its generalization capability.On the other hand, the…
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…
0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming
Shenglong Zhou, Lili Pan, Naihua Xiu +1
Sample average approximation (SAA) is a tractable approach for dealing with chance constrained programming, a challenging stochastic optimization problem. The constraint of SAA is…
Optimality Analysis and Block Sparse Algorithm for Complex Compressed Sensing
Hui Zhang, Xin Liu, Naihua Xiu
Recently, many new challenges in Compressed Sensing (CS), such as block sparsity, arose. In this paper, we present a new algorithm for solving CS with block sparse constraints (BSC…
0/1 Deep Neural Networks via Block Coordinate Descent
Hui Zhang, Shenglong Zhou, Geoffrey Ye Li +1
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsi…
Global Convergence of Inexact Augmented Lagrangian Method for Zero-One Composite Optimization
Penghe Zhang, Naihua Xiu
We consider the problem of minimizing the sum of a smooth function and a composition of a zero-one loss function with a linear operator, namely zero-one composite optimization prob…