Eigenvector Component Calculation Speedup Over NumPy for High-Performance Computing

Applications related to artificial intelligence, machine learning, and system identification simulations essentially use eigenvectors. Calculating eigenvectors for very large matrices using conventional methods is compute-intensive renders the applications slow. Recently, Eigenvector-Eigenvalue Identity formula promising significant speedup was identified. We study algorithmic implementation of against existing state-of-the-art algorithms their implementations evaluate performance gains. provide a first its kind systematic formula. demonstrate further improvements high-performance computing concepts over native NumPy eigenvector which uses LAPACK BLAS.