Practical error estimates for sparse recovery in linear inverse problems
Numerical Analysis
2010-07-19 v2
Abstract
The effectiveness of using model sparsity as a priori information when solving linear inverse problems is studied. We investigate the reconstruction quality of such a method in the non-idealized case and compute some typical recovery errors (depending on the sparsity of the desired solution, the number of data, the noise level on the data, and various properties of the measurement matrix); they are compared to known theoretical bounds and illustrated on a magnetic tomography example.
Cite
@article{arxiv.0908.3636,
title = {Practical error estimates for sparse recovery in linear inverse problems},
author = {Ignace Loris and Caroline Verhoeven},
journal= {arXiv preprint arXiv:0908.3636},
year = {2010}
}
Comments
11 pages, 5 figures