A Generalized Formulation for Group Selection via ADMM

This paper studies a statistical learning model where the model coefficients have a pre-determined non-overlapping group sparsity structure. We consider a combination of a loss function and a regularizer to recover the desired group sparsity patterns, which can embrace many existing works. We analyze the directional stationary solution of the proposed formulation, obtaining a sufficient … Read more