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ABSTRACT
This project investigates numerical methods for approximating eigenvalues of matrices, focusing on accuracy, efficiency, and versatility. Leveraging mathematical frameworks such as the eigenvalue problem 𝐴𝑥 = λ𝑥, methods including power iteration, inverse power iteration, Lanczos algorithm, and QR iteration are evaluated for estimating eigenvalues of symmetric matrices. The project study offers insights into algorithm selection for eigenvalue computations across disciplines.
