A new variant of Arnoldi method for approximation of eigenpairs

Ravibabu, Mashetti; Singh, Arindama

doi:10.1016/j.cam.2018.05.047

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And M Is a Solution Vector Of A Least Squares Problem (22)1

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Cited by 4 publications

(2 citation statements)

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“…For the proof of the theorem; See Theorem-4 in [5]. Using the above theorem the LLS method computes a vector (I−yy * )z (I−yy * )z without computing (I − yy * )z explicitly.…”

Section: And M Is a Solution Vector Of A Least Squares Problem (22)mentioning

confidence: 99%

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On Residual norms in the Rayleigh-Ritz and refined Projection methods

Ravibabu

2019

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“…For the proof of the theorem; See Theorem-4 in [5]. Using the above theorem the LLS method computes a vector (I−yy * )z (I−yy * )z without computing (I − yy * )z explicitly.…”

Section: And M Is a Solution Vector Of A Least Squares Problem (22)mentioning

confidence: 99%

“…The LLS technique procures an approximate eigenpair from Rayleigh-Ritz projection. Then, it improves an eigenvector approximation in Rayleigh-Ritz projection by using least squares heuristics and line search technique [5,6].…”

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confidence: 99%