COSAMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples This paper introduces CoSaMP, an iterative algorithm for signal recovery from incomplete and inaccurate samples. CoSaMP is designed to reconstruct compressible signals with high accuracy and efficiency. The algorithm is based on the concept of orthogonal matching pursuit (OMP) but incorporates additional ideas to ensure robustness and efficiency. CoSaMP guarantees that the reconstructed signal is close to the original signal, even in the presence of noise. The algorithm has a running time of O(N log² N), where N is the length of the signal. CoSaMP is particularly effective for compressible signals, which are well-approximated by sparse signals. The algorithm is efficient because it only requires matrix-vector multiplications with the sampling matrix. The paper also discusses the theoretical foundations of compressive sampling, including the restricted isometry property, which ensures that the sampling matrix preserves the distances between sparse signals. The algorithm is analyzed in detail, and its performance is evaluated in terms of error bounds and computational complexity. The paper concludes that CoSaMP is a promising approach for signal recovery from incomplete and inaccurate samples.COSAMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples This paper introduces CoSaMP, an iterative algorithm for signal recovery from incomplete and inaccurate samples. CoSaMP is designed to reconstruct compressible signals with high accuracy and efficiency. The algorithm is based on the concept of orthogonal matching pursuit (OMP) but incorporates additional ideas to ensure robustness and efficiency. CoSaMP guarantees that the reconstructed signal is close to the original signal, even in the presence of noise. The algorithm has a running time of O(N log² N), where N is the length of the signal. CoSaMP is particularly effective for compressible signals, which are well-approximated by sparse signals. The algorithm is efficient because it only requires matrix-vector multiplications with the sampling matrix. The paper also discusses the theoretical foundations of compressive sampling, including the restricted isometry property, which ensures that the sampling matrix preserves the distances between sparse signals. The algorithm is analyzed in detail, and its performance is evaluated in terms of error bounds and computational complexity. The paper concludes that CoSaMP is a promising approach for signal recovery from incomplete and inaccurate samples.