Numerical algorithms for finding an eigenvector

In summary, there are different algorithms available for finding eigenvectors, one of which is using the fact that when z is an eigenvector, the quantity (1) holds true. However, there may be precision problems when using this method. Another approach is using the Householder transformations to obtain a Hessenberg form, which can then be converted to a Schur form. The NR3 book provides more information on these algorithms, but the older editions are free to view while the newer ones are locked. It may also be helpful to post the question in the NR forum for further assistance.
  • #1
jostpuur
2,116
19
All matrices [itex]A\in\mathbb{C}^{n\times n}[/itex] have at least one eigenvector [itex]z\in\mathbb{C}^n[/itex]. I'm interested to know what kind of algorithms there are for the purpose of finding an eigenvector.

I noticed that

[tex]
\frac{|z^{\dagger} A z|}{\|Az\|} = 1\quad\quad\quad\quad (1)
[/tex]

holds only when [itex]z[/itex] is an eigenvector, so I succeeded writing one algorithm using this fact. I let [itex]z[/itex] move on the sphere [itex]\|z\|=1[/itex] when the program runs iterations so that the quantity (1) is maximized. Unfortunately I run into some precision problems. For some reason my function seems to give only 3 or 4 decimals right for the components of the eigenvector, even though floating point numbers in C-language could contain more accuracy. I believe that the inaccuracy problem rises from the fact that the quantity (1) is approximately a paraboloid in the environment of the eigenvector. If the peak was sharp, then its location would be easier to find with iterations, but paraboloid is not so sharp peak, which makes its location less precise.

Anyway, are there other kind of algorithms out there too?
 
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  • #2
  • #3
Those pdf files are locked somehow. My pdf viewer software asks for a password.

update:

I succeeded getting the book as a pdf file in an alternative way, and it's working now. So thank's for mentioning the book, anyway.
 
Last edited:
  • #4
Actually I did not succeed in learning how to find an eigenvector from that book. For my purposes finding an eigenvector is approximately the same as finding a Schur form, so tried to look for that. I see that the book explains how the Householder transformations can be used to obtain a Hessenberg form, but I don't see how to proceed from Hessenberg form to a Schur form.
 
  • #5
For clarity,

The pdf files for the NR3 book are locked. However, the older editions are free to view.

Also, you might want to post the question in the NR forum.
 

Related to Numerical algorithms for finding an eigenvector

What is an eigenvector?

An eigenvector is a vector that, when multiplied by a square matrix, results in a scalar multiple of itself. In other words, it is a special vector that remains in the same direction after being transformed by a matrix.

Why are eigenvectors important in numerical algorithms?

Eigenvectors are important in numerical algorithms because they allow us to find the most important directions or patterns in a dataset. This can be useful in various applications such as data compression, pattern recognition, and machine learning.

What is the difference between an eigenvalue and an eigenvector?

An eigenvalue is the scalar multiple that an eigenvector is scaled by when multiplied by a matrix. In other words, it is the "stretching factor" of the eigenvector. An eigenvector, on the other hand, is the vector that remains in the same direction after being transformed by a matrix.

How do numerical algorithms find eigenvectors?

Numerical algorithms for finding eigenvectors typically use iterative methods, such as the power method or the QR algorithm. These methods involve repeatedly multiplying a starting vector by a matrix and normalizing it until it converges to an eigenvector.

What are some applications of numerical algorithms for finding eigenvectors?

Numerical algorithms for finding eigenvectors have various applications in fields such as physics, engineering, finance, and data analysis. Some specific examples include principal component analysis, image processing, and solving differential equations.

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