Iterative Hard Thresholding for Compressed Sensing

Understand

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) - It gives near-optimal error guarantees.
• - It is robust to observation noise.