@article{Ahmed2025, 
author = {Kabiru Ahmed and Mohammed Yusuf Waziri and Mohammed A. Saleh and Abdulgader Z. Almaymuni and Abubakar Sani Halilu and Mohamad Afendee Mohamed and Sulaiman M. Ibrahim and Salisu Murtala and Habibu Abdullahi},
title = {Sparse signal recovery through a modified Dai-Yuan algorithm},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {8},
pages = {19675-19692},
keywords = {compressed sensing, conjugate gradient method, sparse signals, Lipschitz condition, global convergence, signaling, algorithms},
url = {https://www.sciopen.com/article/10.3934/math.2025877},
doi = {10.3934/math.2025877},
abstract = {Sparse signal recovery is a concept that is not only central to compressed sensing problems, but also apparent in magnetic resonance imaging (MRI) problems, machine learning, as well as statistical inference. In each of these fields, the target is finding sparse solutions to linear systems of equations that are underdetermined or ill-conditioned. In this paper, an efficient modified Dai-Yuan conjugate gradient method that is globally convergent irrespective of the line search procedure employed was developed to reconstruct sparse signals in compressed sensing. Results of the experiments conducted show that the method is promising.}
}