Abstract
Iterative soft thresholding algorithm (ISTA) has a simple formulation and it can easily be implemented. Nevertheless, ISTA is limited to well-conditioned problems, e.g. compressive sensing. In this paper, we present an ISTA type algorithm based on the generalized conditional gradient method (GCGM) to solve elastic-net regularization which is commonly adopted in ill-conditioned problems. Furthermore, we propose a projected gradient (PG) method to accelerate the ISTA type algorithm. In addition, we discuss the existence of the radius R and we give a strategy to determine the radius R of the l1-ball constraint in the PG method by Morozov's discrepancy principle (MDP). Numerical results are reported to illustrate the efficiency of the proposed approach. (C) 2021 Elsevier B.V. All rights reserved.
NSTL
Elsevier