Orthogonal Matching Pursuit Algorithm (OMP)

Orthogonal Matching Pursuit Algorithm (OMP) is a greedy compressed sensing recovery algorithm.

Orthogonal Matching Pursuit Algorithm (OMP) is a greedy compressed sensing recovery algorithm which selects the best fitting column of the sensing matrix in each iteration. A least squares (LS) optimization is then performed in the subspace spanned by all previously picked columns. This method is less accurate than the Basis pursuit algorithms but has a lower computational complexity. The Matlab function has three inputs: Sparsity K, measurements vector y and sensing matrix A. The output of this function is the recovered sparse vector x.