1979
DOI: 10.1090/s0025-5718-1979-0514820-3
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The Lanczos algorithm with selective orthogonalization

Abstract: A new stable and efficient implementation of the Lanczos algorithm is presented.The Lanczos algorithm is a powerful method for finding a few eigenvalues and eigenvectors at one or both ends of the spectrum of a symmetric matrix A. The algorithm is particularly effective if A is large and sparse in that the only way in which A enters the calculation is through a subroutine which computes Av for any vector v. Thus the user is free to take advantage of any sparsity structure in A and A need not even be represente… Show more

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Cited by 329 publications
(75 citation statements)
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“…[23,24,25,26,27] An advantage of this method is that it automatically includes both rotational and vibrational excitations. However, applying this technique to our system brings technical problems.…”
Section: Energies Of Rovibronic Statesmentioning
confidence: 99%
“…[23,24,25,26,27] An advantage of this method is that it automatically includes both rotational and vibrational excitations. However, applying this technique to our system brings technical problems.…”
Section: Energies Of Rovibronic Statesmentioning
confidence: 99%
“…The explicit enforcement of orthogonality implies that the cost increases as the square of the number of eigenfunctions to be determined. Nevertheless, techniques such as the block-Lanczos method with selective orthogonalization 26 can control the linear independence of the Lanczos vectors without the cost of full orthogonalization. This technique has been employed by Braun et al 24 to compute eigenvalues of the examples presented here and some other with very high accuracy.…”
Section: B Higher-energy Eigenfunctionsmentioning
confidence: 99%
“…He also showed that both partial reorthogonalization and selective orthogonalization introduced by Parlett and Scott [21] or himself [24] are semiorthogonalization strategies. According to the result established by Simon, an adapted Gram-Schmidt orthogonalization routine has been included in the Lanczos step in order to keep the semiorthogonality among the Lanczos vectors during the process.…”
Section: Fischermentioning
confidence: 99%