翻訳と辞書
Words near each other
・ Inverse element
・ Inverse exchange-traded fund
・ Inverse Faraday effect
・ Inverse filter
・ Inverse filter (disambiguation)
・ Inverse floating rate note
・ Inverse function
・ Inverse function theorem
・ Inverse functions and differentiation
・ Inverse Galois problem
・ Inverse gambler's fallacy
・ Inverse gas chromatography
・ Inverse Gaussian distribution
・ Inverse hyperbolic function
・ Inverse image functor
Inverse iteration
・ Inverse kinematics
・ Inverse Laplace transform
・ Inverse limit
・ Inverse magnetostrictive effect
・ Inverse mapping theorem
・ Inverse matrix gamma distribution
・ Inverse mean curvature flow
・ Inverse method
・ Inverse Mills ratio
・ Inverse multiplexer
・ Inverse Multiplexing for ATM
・ Inverse number
・ Inverse parser
・ Inverse Phase


Dictionary Lists
翻訳と辞書 辞書検索 [ 開発暫定版 ]
スポンサード リンク

Inverse iteration : ウィキペディア英語版
Inverse iteration
In numerical analysis, inverse iteration is an iterative eigenvalue algorithm. It allows one to find an approximate
eigenvector when an approximation to a corresponding eigenvalue is already known.
The method is conceptually similar to the power method and is also known as the inverse power method.
It appears to have originally been developed to compute resonance frequencies in the field of structural mechanics.
〔Ernst Pohlhausen, ''Berechnung der Eigenschwingungen statisch-bestimmter Fachwerke'', ZAMM - Zeitschrift für Angewandte
Mathematik und Mechanik 1, 28-42 (1921).〕
The inverse power iteration algorithm starts with an approximation \mu for the eigenvalue corresponding to the desired eigenvector and a vector ''b''0, either a randomly selected vector or an approximation to the eigenvector. The method is described by the iteration
: b_ = \frac,
where ''Ck'' are some constants usually chosen as C_k= \|(A - \mu I)^b_k \|. Since eigenvectors are defined up to multiplication by constant, the choice of ''Ck'' can be arbitrary in theory; practical aspects of the choice of C_k are discussed below.
At every iteration, the vector ''b''''k'' is multiplied by the inverse of the matrix (A - \mu I) and normalized.
It is exactly the same formula as in the power method, except replacing the matrix ''A'' by (A - \mu I)^.
The closer the approximation \mu to the eigenvalue is chosen, the faster the algorithm converges; however, incorrect choice of \mu can lead to slow convergence or to the convergence to an eigenvector other than the one desired. In practice, the method is used when a good approximation for the eigenvalue is known, and hence one needs only few (quite often just one) iterations.
== Theory and convergence ==

The basic idea of the power iteration is choosing an initial vector ''b'' (either an eigenvector approximation or a random vector) and iteratively calculating Ab, A^b, A^b,.... Except for a set of zero measure, for any initial vector, the result will converge to an eigenvector corresponding to the dominant eigenvalue.
The inverse iteration does the same for the matrix (A - \mu I)^, so it converges to eigenvector corresponding to the dominant eigenvalue of the matrix (A - \mu I)^.
Eigenvalues of this matrix are (\lambda_1 - \mu)^,...,(\lambda_n - \mu)^, where \lambda_i are eigenvalues of ''A''.
The largest of these numbers corresponds to the smallest of (\lambda_1 - \mu),...,(\lambda_n - \mu). It is obvious that the eigenvectors of ''A'' and of (A - \mu I)^ are the same.
Conclusion: The method converges to the eigenvector of the matrix ''A'' corresponding to the closest eigenvalue to \mu .
In particular taking \mu=0 we see that (A)^ b
converges to the eigenvector corresponding to the eigenvalue of ''A'' with the smallest absolute value.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
ウィキペディアで「Inverse iteration」の詳細全文を読む



スポンサード リンク
翻訳と辞書 : 翻訳のためのインターネットリソース

Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.