Greedy algorithms for compressed sensing

Compressed Sensing (CS) is often synonymous with l1 based optimization. How- ever, when choosing an algorithm for a particular application, there are a range of different properties that have to be considered and weighed against each other. Important algorithm properties, such as speed and storage requirements, ease of implementation, flexibility and recovery performance have to be compared. In this chapter we will therefore present a range of alternative algorithms that can be used to solve the CS recovery problem and which outperform convex optimization based methods in some of these areas. These methods therefore add important versatility to any CS recovery toolbox