  The sparse linear regression problem is difficult to handle with usual sparse
optimization models when both predictors and measurements are either quantized
or represented in low-precision, due to non-convexity. In this paper, we
provide a novel linear programming approach, which is effective to tackle this
problem. In particular, we prove theoretical guarantees of robustness, and we
present numerical results that show improved performance with respect to the
state-of-the-art methods.
