HiQR: An efficient algorithm for high-dimensional quadratic regression with penalties

被引：0

作者：

Wang, Cheng ^{[1
]}

Chen, Haozhe ^{[1
]}

Jiang, Binyan ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Math Sci, MOE LSC, Shanghai 200240, Peoples R China

[2] Hong Kong Polytech Univ, Dept Appl Math, Hung Hom, Kowloon, Hong Kong, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2024年 / 192卷

基金：

中国国家自然科学基金;

关键词：

ADMM; LASSO; Quadratic regression; Ridge regression; VARIABLE SELECTION; STRONG RULES; LASSO;

D O I：

10.1016/j.csda.2023.107904

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper investigates the efficient solution of penalized quadratic regressions in high -dimensional settings. A novel and efficient algorithm for ridge-penalized quadratic regression is proposed, leveraging the matrix structures of the regression with interactions. Additionally, an alternating direction method of multipliers (ADMM) framework is developed for penalized quadratic regression with general penalties, including both single and hybrid penalty functions. The approach simplifies the calculations to basic matrix-based operations, making it appealing in terms of both memory storage and computational complexity for solving penalized quadratic regressions in high-dimensional settings.

引用

页数：10