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 speed up 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 it's 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.
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.
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compute intensive
compute-intensive
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speed up
speedup
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high performance
high-performance