Implementation of Orthogonal Matching Pursuit (OMP) Algorithm

This document provides a simplified implementation of the Orthogonal Matching Pursuit (OMP) algorithm, faithfully reproducing the approach outlined in "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit." The code structure demonstrates key OMP components including iterative atom selection from the sensing matrix, least-squares solution updates, and residual computation - making it ideal for students beginning their compressed sensing (CS) journey to understand OMP's core mechanics.

Despite its simplicity, this implementation maintains algorithmic integrity with practical value. Users can examine how the code handles sparse signal reconstruction through greedy iterations, observes convergence behavior, and manages stopping criteria. Supplementary resources such as tutorial notes and case studies are included to facilitate deeper exploration of CS concepts including measurement matrices, sparsity constraints, and recovery guarantees.

We anticipate this material will significantly accelerate the learning curve for OMP and compressed sensing. For technical inquiries or improvement suggestions regarding the code implementation's structure or parameter configurations, please feel free to contact our team.