  There is a recent surge of interest in developing algorithms for finding
sparse solutions of underdetermined systems of linear equations $y = \Phi x$.
In many applications, extremely large problem sizes are envisioned, with at
least tens of thousands of equations and hundreds of thousands of unknowns. For
such problem sizes, low computational complexity is paramount. The best studied
$\ell_1$ minimization algorithm is not fast enough to fulfill this need.
Iterative thresholding algorithms have been proposed to address this problem.
In this paper we want to analyze two of these algorithms theoretically, and
give sufficient conditions under which they recover the sparsest solution.
