  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 and renders the applications slow. Recently,
Eigenvector-Eigenvalue Identity formula promising significant speedup was
identified. We study the algorithmic implementation of the formula against the
existing state-of-the-art algorithms and their implementations to evaluate the
performance gains. We provide a first of its kind systematic study of the
implementation of the formula. We demonstrate further improvements using
high-performance computing concepts over native NumPy eigenvector
implementation which uses LAPACK and BLAS.
