Estimating a largest eigenvector by Lanczos and polynomial algorithms with a random start

DOI<147::AID-NLA128>3.0.CO;2-2 10.1002/(SICI)1099-1506(199805/06)5:3<147::AID-NLA128>3.0.CO;2-2zbMath0937.65046OpenAlexW2080510946MaRDI QIDQ4940818

Zbigniew Leyk, Henryk Woźniakowski

Publication date: 27 February 2000

Full work available at URL: https://doi.org/10.1002/(sici)1099-1506(199805/06)5:3<147::aid-nla128>3.0.co;2-2

zbMATH Keywords

convergenceeigenvectorLanczos methoderror analysislargest eigenvalueKrylov subspace methodpower method

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical computation of eigenvalues and eigenvectors of matrices (65F15)

Related Items (4)

Randomized numerical linear algebra: Foundations and algorithms ⋮ Finding Sparse Solutions for Packing and Covering Semidefinite Programs ⋮ Oracle-Based Primal-Dual Algorithms for Packing and Covering Semidefinite Programs ⋮ Randomized block Krylov methods for approximating extreme eigenvalues

Cites Work

• Statistical complexity of dominant eigenvector calculation
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