  The problem of sparse linear regression is relevant in the context of linear
system identification from large datasets. When data are collected from
real-world experiments, measurements are always affected by perturbations or
low-precision representations. However, the problem of sparse linear regression
from fully-perturbed data is scarcely studied in the literature, due to its
mathematical complexity. In this paper, we show that, by assuming bounded
perturbations, this problem can be tackled by solving low-complex l2 and l1
minimization problems. Both theoretical guarantees and numerical results are
illustrated in the paper.
