Computing an Entire Solution Path of a Nonconvexly Regularized Convex Sparse Model

Yi Zhang; Isao Yamada

ICASSP 2024accepted0 citations

Computing an Entire Solution Path of a Nonconvexly Regularized Convex Sparse Model

Abstract

The generalized minimax concave (GMC) penalty is a nonconvex sparse regularizer which can preserve the overall-convexity of the sparse least squares problem. In this paper, we study the solution path of a special but important instance of the GMC model termed the scaled GMC (sGMC) model. We show that despite the nonconvexity of the regularizer, there exists a solution path of the sGMC model which is piecewise linear as a function of the regularization parameter, and we propose an efficient algorithm for computing a solution path of this type. Our algorithm is an extension of the well-known least angle regression (LARS) algorithm for LASSO, hence we term the proposed algorithm LARS-sGMC. The proposed algorithm is provably correct and finitely terminating under suitable assumptions. Numerical experiments verify the correctness of LARS-sGMC, and demonstrate the usefulness of LARS-sGMC (with proper model selection criterion) for finding the optimal regularization parameter of the sGMC model.

BibTeX

@inproceedings{icassp2024_computinganentir,
  title = {Computing an Entire Solution Path of a Nonconvexly Regularized Convex Sparse Model},
  author = {Yi Zhang and Isao Yamada},
  booktitle = {ICASSP 2024},
  year = {2024}
}