# Non-convex Fraction Function Penalty: Sparse Signals Recovered from Quasi-linear Systems

## TL;DR

This paper introduces a non-convex fraction function penalty for quasi-linear compressed sensing, enabling effective sparse signal recovery in nonlinear systems, with an iterative thresholding algorithm outperforming existing methods.

## Contribution

The paper proposes a novel non-convex fraction function penalty and an iterative thresholding algorithm tailored for quasi-linear compressed sensing problems.

## Key findings

- The method effectively recovers sparse signals in nonlinear systems.
- Numerical experiments show superior performance over state-of-the-art methods.
- The algorithm's adaptability via parameter $a$ enhances recovery results.

## Abstract

The goal of compressed sensing is to reconstruct a sparse signal under a few linear measurements far less than the dimension of the ambient space of the signal. However, many real-life applications in physics and biomedical sciences carry some strongly nonlinear structures, and the linear model is no longer suitable. Compared with the compressed sensing under the linear circumstance, this nonlinear compressed sensing is much more difficult, in fact also NP-hard, combinatorial problem, because of the discrete and discontinuous nature of the $\ell_{0}$-norm and the nonlinearity. In order to get a convenience for sparse signal recovery, we set most of the nonlinear models have a smooth quasi-linear nature in this paper, and study a non-convex fraction function $\rho_{a}$ in this quasi-linear compressed sensing. We propose an iterative fraction thresholding algorithm to solve the regularization problem $(QP_{a}^{\lambda})$ for all $a>0$. With the change of parameter $a>0$, our algorithm could get a promising result, which is one of the advantages for our algorithm compared with other algorithms. Numerical experiments show that our method performs much better compared with some state-of-art methods.