# A linear programming approach to sparse linear regression with quantized data

## TL;DR

This paper introduces a linear programming method for sparse linear regression with quantized data, providing theoretical robustness guarantees and demonstrating improved performance over existing methods.

## Contribution

It presents a novel linear programming approach specifically designed for sparse regression with low-precision data, addressing non-convexity issues.

## Key findings

- Proves robustness guarantees for the proposed method.
- Shows improved numerical performance compared to state-of-the-art techniques.
- Effectively handles quantized and low-precision data in sparse regression.

## Abstract

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.