Minimal eigenvalue of large sparse matrices by an efficient reverse power-conjugate gradient scheme

DOI10.1016/0045-7825(83)90049-XzbMath0505.73057OpenAlexW1964267036MaRDI QIDQ1836519

Giuseppe Gambolati, Anna Maria Perdon

Publication date: 1983

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0045-7825(83)90049-x

zbMATH Keywords

minimal eigenvalueaccurate estimatedouble iterative schemeexternal and internal iterationslarge sparse positive definite matricesmodified conjugate gradientsreverse power

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Numerical and other methods in solid mechanics (74S99)

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LEFTMOST EIGENVALUE OF REAL AND COMPLEX SPARSE MATRICES ON PARALLEL COMPUTER USING APPROXIMATE INVERSE PRECONDITIONING, An improved iterative optimization technique for the leftmost eigenpairs of large symmetric matrices, Computation of minimum eigenvalue through minimization of rayleigh's quotient for large sparse matrices using vector computer:, Extreme eigenvalues of large sparse matrices by Rayleigh quotient and modified conjugate gradients

Cites Work

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• Numerical solution of nonlinear elliptic partial differential equations by a generalized conjugate gradient method
• Fast solution to finite element flow equations by newton iteration and modified conjugate gradient method