Iteratively re-weighted least squares minimization for sparse recovery

Understand

We analyze an Iteratively Re-weighted Least Squares (IRLS) algorithm for promoting l1-minimization in sparse and compressible vector recovery.

• We prove its convergence and we estimate its local rate.
• We show how the algorithm can be modified in order to promote lt-minimization for t<1, and how this modification produces superlinear rates of convergence.