Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4944360 | Information Sciences | 2017 | 15 Pages |
Abstract
As a natural extension of Compressive Sensing and the requirement of some practical problems, Phaseless Compressive Sensing (PCS) has been introduced and studied recently. Many theoretical results have been obtained for PCS with the aid of its convex relaxation. Motivated by successful applications of nonconvex relaxed methods for solving Compressive Sensing, in this paper, we try to investigate PCS via its nonconvex relaxation. Specifically, we relax PCS in the real context by the corresponding âp-minimization with p â (0, 1). We show that there exists a constant p* â (0, 1] such that for any fixed p â (0, p*), every optimal solution to the âp-minimization also solves the concerned problem; and derive an expression of such a constant p* by making use of the known data and the sparsity level of the concerned problem. These provide a theoretical basis for solving this class of problems via the corresponding âp-minimization.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Guowei You, Zheng-Hai Huang, Yong Wang,