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Introduction to Eigenvectors

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Eigenvector acts on vector by changing both its magnitude and its direction. A matrix acts on an eigenvector by multiplying its magnitude by a factor. If the direction is unchanged and negative then it will positive and the direction will be reversed. These vectors are the eigenvectors of the matrix.

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To each distinct eigenvalue of a matrix A there will be one eigenvector which are found by solving the appropriate set of homogenous equations. If i is an Eigenvalue then the corresponding eigenvector xi is the solution of Introduction to Eigenvectors

For example:

Find the Eigenvalues of Eigenvectors

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Solution:

Eigenvectors Solutions

two eigenvalues: -1, - 2

Example

Eigenvectors Example

Theorem for Eigenvectors

  • It corresponding to distinct (that is, different) eigenvalues are linearly independent.
  • If is an eigen value of multiplicity k of an n n matrix A then the number of linearly independent eigenvectors of A associated with is given by m = n - r(A- I). Furthermore, 1 m k.
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