Difference between revisions of "Eigenvectors and Eigenvalues"

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{{Jargon}}
 
{{Jargon}}
  
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An '''eigenvector''' of a square [[matrix]] <math>A</math> is a vector <math>\boldsymbol{x}\in\mathbb{R}</math> such that,
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'''Eigenvectors''' represent directions that are preserved by linear transformations of a [[vector space]].
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An '''eigenvector''' of an ''n x n'' square [[matrix]] <math>A</math> is a vector <math>\boldsymbol{x}\in\mathbb{C}^n</math> such that,
  
 
:<math>A\boldsymbol{x}=\lambda\boldsymbol{x}</math>
 
:<math>A\boldsymbol{x}=\lambda\boldsymbol{x}</math>

Revision as of 19:43, April 25, 2010

This article or section needs to be written in plain English, using plain English that most of our readers can understand. Articles that depend excessively on technical terms accessible only to specialists are useless for our purposes, so writers are admonished to avoid jargon

Eigenvectors represent directions that are preserved by linear transformations of a vector space.

An eigenvector of an n x n square matrix is a vector such that,

where is complex number, known as a eigenvalue. The eigenvalue is the zero of a matrix's charateristic polynomial.

All the eigenvectors of a particular eigenvalue span a vector space called the eigenspace.