# Efficient atom selection strategy for iterative sparse approximations

## TL;DR

This paper introduces a computationally efficient atom selection method for sparse representation algorithms, applicable to both discrete and continuous dictionaries, demonstrated through DOA and Gaussian deconvolution experiments.

## Contribution

It presents a novel low-computational strategy for atom selection that reduces complexity in sparse approximation algorithms.

## Key findings

- Significant computational savings in DOA and Gaussian deconvolution tasks
- Applicable to both discrete and continuous dictionaries
- Improves efficiency without sacrificing accuracy

## Abstract

We propose a low-computational strategy for the efficient implementation of the "atom selection step" in sparse representation algorithms. The proposed procedure is based on simple tests enabling to identify subsets of atoms which cannot be selected. Our procedure applies on both discrete or continuous dictionaries. Experiments performed on DOA and Gaussian deconvolution problems show the computational gain induced by the proposed approach.