Iteratively re-weighted least squares minimization for sparse recovery
Numerical Analysis
2008-07-04 v1
Abstract
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.
Cite
@article{arxiv.0807.0575,
title = {Iteratively re-weighted least squares minimization for sparse recovery},
author = {Ingrid Daubechies and Ronald DeVore and Massimo Fornasier and C. Sinan Gunturk},
journal= {arXiv preprint arXiv:0807.0575},
year = {2008}
}
Comments
35 pages, 4 figures