A pseudo-heuristic parameter selection rule for l1-regularized minimization problems

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:333

Article

Li, Chong-Jun¹  Zhong, Yi-Jun¹ 

[1] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China

关键词: Regularization parameter;    l(1)-regularized;    Sparse recovery;    De Boor's rule;   

DOI  :  10.1016/j.cam.2017.10.006

来源: Elsevier

【 摘 要 】

This paper considers the regularization parameter determination of l(1)-regularized minimization problem. We solve the l(1)-regularized problem using iterative reweighted least squares (IRIS) which involves solving a linear system whose coefficient matrix has the form alpha M + (1 - alpha)N (alpha is an element of (0, 1)). The aim of this paper is to find an efficient and cornputationally inexpensive algorithm to both choose the regularization parameter and solve the l(1)-regularized problem. In order to achieve this, we propose an IRLS algorithm with adaptive regularization parameter selection based on a heuristic parameter determination rule de Boor's parameter selection criterion. Compared with some of the state-of-the-art algorithms and parameter selection rules, the numerical experiments show the efficiency and robustness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free