eigenvector

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ei·gen·vec·tor

 (ī′gən-vĕk′tər)
n.
A vector whose direction is unchanged by a given transformation and whose magnitude is changed by a factor corresponding to that vector's eigenvalue. In quantum mechanics, the transformations involved are operators corresponding to a physical system's observables. The eigenvectors correspond to possible states of the system, and the eigenvalues to possible observed values.

[Partial translation of German Eigenvektor : eigen-, characteristic; see eigenvalue + Vektor, vector.]

eigenvector

(ˈaɪɡənˌvɛktə)
n
(Mathematics) maths physics a vector x satisfying an equation Ax = λx, where A is a square matrix and λ is a constant
Translations
egenvektor
omavektor
ominaisvektori
autovettore
eigenvector
egenvektor
egenvektor
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References in periodicals archive ?
The other is an ambitious conjecture of Harangi and Virag concerning eigenvectors of random regular graphs, stating that these eigenvectors converge to Gaussian wave functions.
2010), proposed the Power Iteration Clustering (PIC) algorithm which finds only one pseudo-eigenvector, a linear combination of the eigenvectors in linear time.
The sample points are the eigenvalues of a certain unitary matrix, and the weights are the squares of the absolute values of the first components of the eigenvectors.
Gaussian elimination and its applications are covered next, followed by eigenvalues, eigenvectors, and determinants.
2) The eigenvalues and the left eigenvectors of P = {[P.
According to the singular value decomposition principle, solve the eigenvalues and eigenvectors of [A.
In this paper we investigate the so-called extended eigenvalues and extended eigenvectors and cyclicity problems for some convolution operators acting on the space of analytic functions defined on the starlike domain D of the complex plane.
Diagnostic tests for collinearity are: VIF (variance inflation factor), correlation matrix of variables, eigenvalues and eigenvectors, condition indices, variance proportions.
I presume that baryons and mesons could be the fundamental and excited eigenvectors of the Dynamic bi-Laplacian Equation obtained in the Introduction of Reference 1.
The DATA matrix is then decomposed using Singular Value Decomposition (aka eigenvector decomposition), which yields the eigenvectors of the covariance matrix.
For determining the eigenvectors of the aggregate matrices the following method was applied:
For this analysis, data were first standardized and a correlation matrix, eigenvalues, and eigenvectors were calculated using NTSYS-pc verson 2.