Orthogonal Matching Pursuit

Orthogonal Matching Pursuit (OMP) is a greedy algorithm used for sparse signal recovery and approximation, aiming to identify the most relevant features from a large dataset to reconstruct a signal or estimate a model. Current research focuses on improving OMP's efficiency and accuracy, particularly through faster projection methods, novel selection criteria, and extensions to handle high-dimensional data, distributed computing, and privacy constraints. These advancements are impacting diverse fields, including signal processing, machine learning, and channel estimation, by enabling more efficient and robust solutions for problems involving sparse representations.