1980
DOI: 10.1090/s0025-5718-1980-0583502-2
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The spectral transformation Lánczos method for the numerical solution of large sparse generalized symmetric eigenvalue problems

Abstract: A new algorithm is developed which computes a specified number of eigenvalues in any part of the spectrum of a generalized symmetric matrix eigenvalue problem. It uses a linear system routine (factorization and solution) as a tool for applying the Lanczos algorithm to a shifted and inverted problem. The algorithm determines a sequence of shifts and checks that all eigenvalues get computed in the intervals between them. It is shown that for each shift several eigenvectors will converge after very few steps of t… Show more

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Cited by 141 publications
(133 citation statements)
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“…The eigenvalues can then be calculated using an efficient diagonalization routine such as the Lanczos algorithm [115][116][117][118].…”
Section: Hamiltonian Basis Sets and Selection Rulesmentioning
confidence: 99%
“…The eigenvalues can then be calculated using an efficient diagonalization routine such as the Lanczos algorithm [115][116][117][118].…”
Section: Hamiltonian Basis Sets and Selection Rulesmentioning
confidence: 99%
“…The basis B is then ordered in order to represent the Schrödinger equation using band matrices as narrow as possible. The eigenvalue problem is then solved using the Lanczos algorithm [23] which makes it possible to compute a few eigenvalues in the range of interest.…”
Section: Numerical Implementationmentioning
confidence: 99%
“…27,[32][33][34][35][36] Whether such an approach is more efficient ͑requires less Hamiltonian operations͒ depends on how many Hamiltonian operations are required to evaluate the action of f (Ĥ ) on a Lanczos vector. Because experience with respect to this issue is mixed, 32,36 in the present work we simply use Ĥ .…”
Section: Introductionmentioning
confidence: 99%