6 citations · 22 across the 32 of their papers we have counts for
32 papers
Exact-Penalty Prox-Linear Methods for Bilevel Optimization with Lower-Level Gradient Penalty
Yutong Zheng, Jiani Li, Qingna Li
Bilevel optimization is a fundamental framework for hierarchical decision-making, but its solution is challenging due to the implicit and typically set-valued nature of the lower-l…
Fast Algorithms for Optimal Damping in Mechanical Systems
Qingna Li, Françoise Tisseur
Optimal damping aims at determining a vector of damping coefficients that maximizes the decay rate of a mechanical system's response. This problem can be formulated as the mini…
Bilevel Models for Adversarial Learning and A Case Study
Yutong Zheng, Qingna Li
Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated str…
A Robust EDM Optimization Approach for 3D Single-Source Localization with Angle and Range Measurements
Mingyu Zhao, Qingna Li, Hou-Duo Qi
Accurate source localization in Multi-Platform Radar Networks (MPRNs) benefits from exploiting both range and angle measurements under robust estimation. In this paper, we propose…
On Constraint Qualifications for MPECs with Applications to Bilevel Hyperparameter Optimization for Machine Learning
Jiani Li, Qingna Li, Alain Zemkoho
Constraint qualifications for a Mathematical Program with Equilibrium Constraints (MPEC) are essential for analyzing stationarity properties and establishing convergence results. I…
Modified Block Newton Algorithm for -Regularized Optimization
Yuge Ye, Qingna Li
In this paper, we propose a globally convergent Newton type method to solve regularized sparse optimization problem. In fact, a line search strategy is applied to the Newt…
An Efficient Smoothing Damped Newton Method for Large-Scale Mathematical Programs with Equilibrium Constraints
Yixin Wang, Qingna Li, Liwei Zhang
Bilevel hyperparameter optimization has received growing attention thanks to the fast development of machine learning. Due to the tremendous size of data sets, the scale of bilevel…
Proximal Iterative Hard Thresholding Algorithm for Sparse Group -Regularized Optimization with Box Constraint
Yuge Ye, Qingna Li
This paper investigates a general class of problems in which a lower bounded smooth convex function incorporating and regularization is minimized over a box…
Subspace Newton's Method for -Regularized Optimization Problems with Box Constraint
Yuge Ye, Qingna Li
This paper investigates the box-constrained -regularized sparse optimization problem. We introduce the concept of a -stationary point and establish its connection to the…
Inexact Regularized Quasi-Newton Algorithm for Solving Monotone Variational Inequality Problems
Yuge Ye, Qingna Li, Deren Han
Newton's method has been an important approach for solving variational inequalities, quasi-Newton method is a good alternative choice to save computational cost. In this paper, we…
A Spectral Projected Gradient Method for Computational Protein Design problem
Yukai Zheng, Qingna Li
In this paper, we consider the computational protein design (CPD) problem, which is usually modeled as 0/1 programming and is extremely challenging due to its combinatorial propert…
A Matrix Optimization Method for Blind Extraction of External Equitable Partitions from Low Pass Graph Signals
Wenshun Teng, Qingna Li
Seeking the external equitable partitions (EEPs) of networks under unknown structures is an emerging problem in network analysis. The special structure of EEPs has found widespread…
Triangle Steepest Descent: A Geometry-Based Gradient Algorithm with Guaranteed R-Linear Convergence
Ya Shen, Qing-Na Li, Yu-Hong Dai
Gradient methods are among the simplest yet most widely used algorithms for unconstrained optimization. Motivated by a geometric property of the steepest descent (SD) method that c…
An Efficient Quadratic Penalty Method for a Class of Graph Clustering Problems
Wenshun Teng, Qingna Li
Community-based graph clustering is one of the most popular topics in the analysis of complex social networks. This type of clustering involves grouping vertices that are considere…
Global relaxation-based LP-Newton method for multiple hyperparameter selection in support vector classification with feature selection
Yaru Qian, Qingna Li, Alain Zemkoho
Support vector classification (SVC) is an effective tool for classification tasks in machine learning. Its performance relies on the selection of appropriate hyperparameters. This…
A Fast Smoothing Newton Method for Bilevel Hyperparameter Optimization for SVC with Logistic Loss
Yixin Wang, Qingna Li
Support vector classification (SVC) with logistic loss has excellent theoretical properties in classification problems where the label values are not continuous. In this paper, we…
An Efficient Algorithm for Computational Protein Design Problem
Yukai Zheng, Weikun Chen, Qingna Li
In this paper, we consider the computational protein design (CPD) problem, which is usually modeled as a 0/1 programming and is extremely challenging due to its combinatorial prope…
A Highly Efficient Adaptive-Sieving-Based Algorithm for the High-Dimensional Rank Lasso Problem
Xiaoning Bai, Qingna Li
The high-dimensional rank lasso (hdr lasso) model is an efficient approach to deal with high-dimensional data analysis. It was proposed as a tuning-free robust approach for the hig…
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines
Wen Su, Qingna Li
Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support v…
Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines
Wen Su, Qingna Li, Chunfeng Cui
Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpreted very well. In contrast, lit…
Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method
Qingna Li, Zhen Li, Alain Zemkoho
Support vector classification (SVC) is a classical and well-performed learning method for classification problems. A regularization parameter, which significantly affects the class…
A Facial Reduction Approach for the Single Source Localization Problem
He Shi, Qingna Li
The single source localization problem (SSLP) appears in several fields such as signal processing and global positioning systems. The optimization problem of SSLP is nonconvex and…
A Majorization Penalty Method for SVM with Sparse Constraint
Lu Sitong, Li Qinana
Support vector machine is an important and fundamental technique in machine learning. Soft-margin SVM models have stronger generalization performance compared with the hard-margin…
A Euclidean Distance Matrix Model for Convex Clustering
Zhaowei Wang, Xiaowen Liu, Qingna Li
Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. The recently proposed sum-of-norms (S…