11 papers
Bilevel Programming Approach for Image Restoration Problems with Automatically Hyperparameter Selection
Hang Xie, Xuewen Li, Peili Li +1
In optimization-based image restoration models, the correct selection of hyperparameters is crucial for achieving superior performance. However, current research typically involves…
ADMM-based Bilevel Descent Aggregation Algorithm for Sparse Hyperparameter Selection
Yunhai Xiao, Anqi Liu, Peili Li +1
It is widely acknowledged that hyperparameter selection plays a critical role in the effectiveness of sparse optimization problems. The bilevel optimization provides a robust frame…
A Primal Dual Active Set with Continuation Algorithm for -Penalized High-dimensional Accelerated Failure Time Model
Peili Li, Ruoying Hu, Yanyun Ding +1
The accelerated failure time model has garnered attention due to its intuitive linear regression interpretation and has been successfully applied in fields such as biostatistics, c…
Iterative Reweighted Framework Based Algorithms for Sparse Linear Regression with Generalized Elastic Net Penalty
Yanyun Ding, Zhenghua Yao, Peili Li +1
The elastic net penalty is frequently employed in high-dimensional statistics for parameter regression and variable selection. It is particularly beneficial compared to lasso when…
Graph-based Square-Root Estimation for Sparse Linear Regression
Peili Li, Zhuomei Li, Yunhai Xiao +2
Sparse linear regression is one of the classic problems in the field of statistics, which has deep connections and high intersections with optimization, computation, and machine le…
An Efficient Method for Joint Delay-Doppler Estimation of Moving Targets in Passive Radar
Mengjiao Shi, Yunhai Xiao, Peili Li
Passive radar systems can detect and track the moving targets of interest by exploiting non-cooperative illuminators-of-opportunity to transmit orthogonal frequency division multip…
On the Linear Convergence Rate of Generalized ADMM for Convex Composite Programming
Han Wang, Peili Li, Yunhai Xiao
Over the fast few years, the numerical success of the generalized alternating direction method of multipliers (GADMM) proposed by Eckstein \& Bertsekas [Math. Prog., 1992] has insp…
Convergence Analysis of Generalized ADMM with Majorization for Linearly Constrained Composite Convex Optimization
Hongwu Li, Haibin Zhang, Yunhai Xiao
The generalized alternating direction method of multipliers (ADMM) of Xiao et al. [{\tt Math. Prog. Comput., 2018}] aims at the two-block linearly constrained composite convex prog…
A Majorized-Generalized Alternating Direction Method of Multipliers for Convex Composite Programming
Congying Qin, Yunhai Xiao, Peili Li
The linearly constrained convex composite programming problems whose objective function contains two blocks with each block being the form of nonsmooth+smooth arises frequently in…
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
Peili Li, Yuling Jiao, Xiliang Lu +1
In this work, we consider the algorithm to the (nonlinear) regression problems with penalty. The existing algorithms for based optimization problem are often carr…
Semismooth Newton Augmented Lagrangian Algorithm for Adaptive Lasso Penalized Least Squares in Semiparametric Regression
Peili Li, Yunhai Xiao, Meixia Yang +1
This paper is concerned with a partially linear semiparametric regression model containing an unknown regression coefficient, an unknown nonparametric function, and an unobservable…