Work

ADMM for least square problems with pairwise-difference penalties for coefficient grouping

Year: 2022

Type: article

Abstract: In the era of bigdata, scalability is a crucial issue in learning models.Among many others, the Alternating Direction of Multipliers (ADMM, Boyd et al., 2011) algorithm has gained great popularity in solving large-scale problems efficiently.In this article, we propose applying the ADMM algorithm to solve the least square problem penalized by the pairwise-difference penalty, frequently used to identify group structures among coefficients.ADMM algorithm enables us to solve the high-dimensional problem efficiently in a unified fashion and thus allows us to employ several different types of penalty functions such as LASSO, Elastic Net, SCAD, and MCP for the penalized problem.Additionally, the ADMM algorithm naturally extends the algorithm to distributed computation and real-time updates, both desirable when dealing with large amounts of data.

Source: Communications for Statistical Applications and Methods

DOI: 10.29220/csam.2022.29.4.441

Authors: Soohee Parka, Seung Jun Shin

Institution: Korea University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea

Awards: G6230222439, G7676710523