Iterative Hard Thresholding for Compressed Sensing

Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper we present a theoretical analysis of the iterative hard thresholding algorithm when applied to the compressed sensing recovery problem. We show that the algorithm has the following properties (made more precise in the main text of the paper)

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Thomas Blumensath, Mike E. Davies
• Keywords: Compressed Sensing, CORR 2008, Education, Near-optimal Error Guarantees, Sensing Recovery Problem