Approximately Normalized Iterative Hard Thresholding for Nonlinear Compressive Sensing. (15th August 2016)

• Record Type: Journal Article
• Authors: Zhu, Xunzhi
• Other Names: Ioana Cornel Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2016(2016)
• Publication Date: 2016-08-15
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2016/2594752
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : The nonlinear compressive sensing (NCS) is an extension of classical compressive sensing (CS) and the iterative hard thresholding (IHT) algorithm is a popular greedy-type method for solving CS. The normalized iterative hard thresholding (NIHT) is a modification of IHT and is more effective than IHT. In this paper, we propose an approximately normalized iterative hard thresholding (ANIHT) algorithm for NCS by using the approximate optimal stepsize combining with Armijo stepsize rule preiteration. Under the condition similar to restricted isometry property (RIP), we analyze the condition that can identify the iterative support sets in a finite number of iterations. Numerical experiments show the good performance of the new algorithm for the NCS.