Power Method to find dominant eigenvalue
Version 1.0.02 (1.32 KB) by
Dr. Manotosh Mandal
Matlab codes for Power Method to find dominant eigenvalue and the corresponding eigenvector.
Determination of eigenvalues by power method:
Let
be a real symmetric matrix and
be a real n component vector. Let
.
Now ![](data:image/png;base64,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)
Then
is an approximate largest eigenvalues of A and if
where ϵ is the given error of tolerance and the corresponding eigenvector is
.Otherwise continue the process.
For details of the method and also coding watch the lecture: https://youtu.be/rOxgnicTRaA
Example:
A =
1 3 -1
3 2 4
-1 4 10
Enter the tolerance of error 0.001
The greatest eigenvalue is 11.66225
The corresponding eigenvector is:
0.02490
0.42174
1.00000>>
Cite As
Dr. Manotosh Mandal (2024). Power Method to find dominant eigenvalue (https://www.mathworks.com/matlabcentral/fileexchange/72587-power-method-to-find-dominant-eigenvalue), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Created with
R2019a
Compatible with any release
Platform Compatibility
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Version | Published | Release Notes | |
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1.0.02 | For details of the method and also coding watch the lecture: https://youtu.be/rOxgnicTRaA |
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1.0.01 | New and easy version |
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1.0.0 |