Transcribed
Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods
Mar 2, 2026 ·
14m 53s
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Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods
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Description
Explores various matrix computations, with a particular focus on semiseparable matrices and their applications. It defines semiseparable matrices, details their properties, and discusses different representations, such as generator and Givens-vector...
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Explores various matrix computations, with a particular focus on semiseparable matrices and their applications. It defines semiseparable matrices, details their properties, and discusses different representations, such as generator and Givens-vector forms. The text covers algorithms for reducing matrices into semiseparable, tridiagonal, and Hessenberg forms, utilizing transformations like Givens and Householder rotations. Furthermore, it examines the convergence properties of these reduction algorithms, relates them to Lanczos and Arnoldi-Ritz values, and discusses the implementation and numerical experiments of QR-algorithms for eigenvalue and singular value computations of structured matrices, including techniques for deflation and eigenvector calculation. Finally, the source touches upon inverse eigenvalue problems and orthogonal polynomials in the context of structured matrix computations.
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You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/book_shelter
Get the Book now from Amazon:
https://www.amazon.com/Matrix-Computations-Semiseparable-Matrices-Eigenvalue-ebook/dp/B07DFMHY49?&linkCode=ll2&tag=cvthunderx-20&linkId=cd9bdb1855d862771135945f6a1bd9eb&language=en_US&ref_=as_li_ss_tl
Produced by Podcai Studio:
https://www.podcaistudio.com/
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