Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models.

ABSTRACT: This paper proposes an adaptively bounded gradient descent (ABGD) algorithm for group elastic net penalized regression. Unlike previously proposed algorithms, the proposed algorithm adaptively bounds the Fisher information matrix, which results in a flexible and stable computational framework. In particular, the proposed algorithm (i) does not require orthogonalization of the predictors, and (ii) can be easily applied to any combination of exponential family response distribution and link function. The proposed algorithm is implemented in the grpnet R package (available from CRAN), which implements the approach for common response distributions (Gaussian, binomial, and Poisson), as well as several response distributions not previously considered in the group penalization literature (i.e.,

SUBMITTER: Helwig NE 

PROVIDER: S-EPMC11864759 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature