6 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…
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…
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…
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…