# Gaussian Mixtures Based IRLS for Sparse Recovery With Quadratic Convergence

@article{Ravazzi2015GaussianMB, title={Gaussian Mixtures Based IRLS for Sparse Recovery With Quadratic Convergence}, author={Chiara Ravazzi and Enrico Magli}, journal={IEEE Transactions on Signal Processing}, year={2015}, volume={63}, pages={3474-3489} }

- Published 2015 in IEEE Transactions on Signal Processing
DOI:10.1109/TSP.2015.2428216

In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori… CONTINUE READING