While the ℓ1 minimization technique plays an important role in designing computationally tractable recovery methods in compressed sensing, its complexity is still impractical for many applications. An attractive alternative to the ℓ1 minimization is a family of greedy algorithms. We will discuss several greedy algorithms from the point of view of their practical applicability and theoretical performance.