The Jacobi-Davidson (JD) method has been recently proposed for the evaluation of the partial eigenspectrum of large sparse matrices. In this work we report a set of numerical experiments that compare this method with other previously proposed techniques; DACG (Deflation Accelerated Conjugate Gradient) and Lanczos (ARPACK), on large sparse symmetric matrices. The results obtained by JD and DACG are benchmarked against those obtained with ARPACK in terms ofcomputational time for the evaluation of a number of the leftomost eigenpairs of large and sparse matrices
Numerical comparison of iterative eigensolvers for large sparse symmetric matrices
BERGAMASCHI, LUCA;PUTTI, MARIO
2002
Abstract
The Jacobi-Davidson (JD) method has been recently proposed for the evaluation of the partial eigenspectrum of large sparse matrices. In this work we report a set of numerical experiments that compare this method with other previously proposed techniques; DACG (Deflation Accelerated Conjugate Gradient) and Lanczos (ARPACK), on large sparse symmetric matrices. The results obtained by JD and DACG are benchmarked against those obtained with ARPACK in terms ofcomputational time for the evaluation of a number of the leftomost eigenpairs of large and sparse matrices
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