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A Study on the Convergence Analysis of the Inexact Simplified Jacobi–Davidson Method
Author(s) -
Jutao Zhao,
Pengfei Guo
Publication year - 2021
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2021/2123897
Subject(s) - rayleigh quotient iteration , mathematics , jacobi method , power iteration , convergence (economics) , eigenvalues and eigenvectors , preconditioner , rayleigh quotient , jacobi eigenvalue algorithm , fixed point iteration , inverse iteration , iterative method , hermitian matrix , rate of convergence , mathematical analysis , mathematical optimization , fixed point , pure mathematics , computer science , physics , quantum mechanics , economics , economic growth , channel (broadcasting) , computer network
The Jacobi–Davidson iteration method is very efficient in solving Hermitian eigenvalue problems. If the correction equation involved in the Jacobi–Davidson iteration is solved accurately, the simplified Jacobi–Davidson iteration is equivalent to the Rayleigh quotient iteration which achieves cubic convergence rate locally. When the involved linear system is solved by an iteration method, these two methods are also equivalent. In this paper, we present the convergence analysis of the simplified Jacobi–Davidson method and present the estimate of iteration numbers of the inner correction equation. Furthermore, based on the convergence factor, we can see how the accuracy of the inner iteration controls the outer iteration.

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