IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy

Compressed sensing is a central topic in signal processing with myriad applications, where the goal is to recover a signal from as few observations as possible. Iterative re-weighting is one of the fundamental tools to achieve this goal. This paper re-examines the iteratively reweighted least squares (IRLS) algorithm for sparse recovery proposed by Daubechies, Devore, Fornasier, and G\"unt\"urk in \emph{Iteratively reweighted least squares minimization for sparse recovery}, {\sf Communications on Pure and Applied Mathematics}, {\bf 63}(2010) 1--38. Under the null space property of order $K$, the authors show that their algorithm converges to the unique $k$-sparse solution for $k$ strictly bounded above by a value strictly less than $K$, and this $k$-sparse solution coincides with the unique $\ell_1$ solution. On the other hand, it is known that, for $k$ less than or equal to $K$, the $k$-sparse and $\ell_1$ solutions are unique and coincide. The authors emphasize that their proof method does not apply for $k$ sufficiently close to $K$, and remark that they were unsuccessful in finding an example where the algorithm fails for these values of $k$. In this note we construct a family of examples where the Daubechies-Devore-Fornasier-G\"unt\"urk IRLS algorithm fails for $k=K$, and provide a modification to their algorithm that provably converges to the unique $k$-sparse solution for $k$ less than or equal to $K$ while preserving the local linear rate. The paper includes numerical studies of this family as well as the modified IRLS algorithm, testing their robustness under perturbations and to parameter selection.