Asymptotic convergence of spectral inverse iterations for stochastic eigenvalue problems

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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We consider and analyze applying a spectral inverse iteration algorithm and its subspace iteration variant for computing eigenpairs of an elliptic operator with random coefficients. With these iterative algorithms the solution is sought from a finite dimensional space formed as the tensor product of the approximation space for the underlying stochastic function space, and the approximation space for the underlying spatial function space. Sparse polynomial approximation is employed to obtain the first one, while classical finite elements are employed to obtain the latter. An error analysis is presented for the asymptotic convergence of the spectral inverse iteration to the smallest eigenvalue and the associated eigenvector of the problem. A series of detailed numerical experiments supports the conclusions of this analysis.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut577-609
Sivumäärä33
JulkaisuNumerische Mathematik
Vuosikerta142
Numero3
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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