# Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

## TL;DR

This paper introduces a novel polynomial optimization approach for recovering binary sparse signals from limited linear measurements, offering a new perspective and comparison to existing methods in binary compressed sensing.

## Contribution

It presents a new polynomial optimization formulation specifically for binary sparse signal recovery and analyzes its performance relative to current state-of-the-art methods.

## Key findings

- The polynomial optimization approach effectively recovers binary sparse signals.
- Compared to existing methods, it offers competitive or improved recovery performance.
- The approach provides a new mathematical framework for binary compressed sensing.

## Abstract

The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse signals and we propose a novel formulation, based on polynomial optimization. This approach is analyzed and compared to the state-of-the-art binary compressed sensing methods.