An efficient semismooth Newton method for adaptive sparse signal recovery problems. (4th March 2023)

• Record Type: Journal Article
• Authors: Ding, Yanyun; Zhang, Haibin; Li, Peili; Xiao, Yunhai
• Is Part Of: Optimization methods and software. Volume 38:Number 2(2023)
• Page Start: 262
• Page End: 288
• Publication Date: 2023-03-04
• Subjects: Compressive sensing -- ℓp-ℓ1−2 minimization -- proximal majorization-minimization -- Clarke subdifferential -- semismooth newton method; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2022.2120983
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between ℓ 1 - and ℓ 2 -norm as a regularization always has superior performance. In this paper, we consider an adaptive ℓ p - ℓ 1 − 2 model where the ℓ p -norm with p ≥ 1 measures the data fidelity and the ℓ 1 − 2 -term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the non-convex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm.