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

Soohee Park, Seung Jun Shin

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish
    Pages (from-to)441-451
    Number of pages11
    JournalCommunications for Statistical Applications and Methods
    Volume29
    Issue number4
    DOIs
    Publication statusPublished - 2022

    Bibliographical note

    Publisher Copyright:
    © 2022. The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.

    Keywords

    • Alternating direction of multipliers
    • Grouping coefficients
    • High-dimensional data
    • Real-time update

    ASJC Scopus subject areas

    • Statistics and Probability
    • Modelling and Simulation
    • Finance
    • Statistics, Probability and Uncertainty
    • Applied Mathematics

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