DC algorithms for a class of sparse group $\ell_0$ regularized optimization problems
Abstract
In this paper, we consider a class of sparse group regularized optimization problems. Firstly, we give a continuous relaxation model of the considered problem and establish the equivalence of these two problems in the sense of global minimizers. Then, we define a class of stationary points of the relaxation problem, and prove that any defined stationary point is a local minimizer of the considered sparse group regularized problem and satisfies a desirable property of its global minimizers. Further, based on the difference-of-convex (DC) structure of the relaxation problem, we design two DC algorithms to solve the relaxation problem. We prove that any accumulation point of the iterates generated by them is a stationary point of the relaxation problem. In particular, all accumulation points have a common support set and a unified lower bound for the nonzero entries, and their zero entries can be attained within finite iterations. Moreover, we prove the convergence of the entire iterates generated by the proposed algorithms. Finally, we give some numerical experiments to show the efficiency of the proposed algorithms.
Cite
@article{arxiv.2109.05251,
title = {DC algorithms for a class of sparse group $\ell_0$ regularized optimization problems},
author = {Wenjing Li and Wei Bian and Kim-Chuan Toh},
journal= {arXiv preprint arXiv:2109.05251},
year = {2022}
}