Lecture
Matrix Computations: Eigenvalues and Eigenvectors
Description
This lecture covers the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices. It discusses algorithms like QR and tridiagonal reduction, emphasizing backward error analysis and the impact of near-multiplicity on accuracy. The instructor explores the challenges in computing eigenvectors, including polytime algorithms, precision issues, and the worst-case complexity of computing all eigenvectors.
Official source
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