# Tree Search Network for Sparse Regression

## TL;DR

The paper introduces TSN, a tree search algorithm enhanced by deep neural networks, which significantly improves sparse signal recovery performance and reduces complexity compared to existing methods.

## Contribution

It presents a novel tree search algorithm driven by deep neural networks for sparse regression, outperforming existing algorithms in accuracy and efficiency.

## Key findings

- TSN achieves lower reconstruction error than baseline methods.
- TSN outperforms existing algorithms across various sensing matrices.
- TSN is effective in both noiseless and noisy scenarios.

## Abstract

We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = \Phi x_0+w$. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sparse regression by performing a tree search with pruning. It is observed in both noiseless and noisy cases, TSN recovers synthetic and real signals with lower complexity than a conventional tree search and is superior to existing algorithms by a large margin for various types of the sensing matrix $\Phi$, widely used in sparse regression.