Finding eigenvalues and eigenvectors

In summary, the conversation discusses finding eigenvalues and eigenvectors for a specific type of matrix. The participants mention the traditional method of solving for eigenvalues and eigenvectors, and also mention the usefulness of shortcuts and intuition. They also suggest using a computer program for efficiency, but acknowledge the value of developing mathematical intuition.
  • #1
fargoth
320
6
is there any trick for finding the eigenvalues and vectors for this kind of matrix?
[tex]
\left(
\begin{array}{ccccc}
0 & 1 & 0 & 0 & 0 \\
1 & 0 & \sqrt{\frac{3}{2} & 0 & 0 \\
0 & \sqrt{\frac{3}{2} & 0 & \sqrt{\frac{3}{2} & 0 \\
0 & 0 & \sqrt{\frac{3}{2} & 0 & 1 \\
0 & 0 & 0 & 1 & 0 \\
\end{array}
\right)
[/tex]

i mean, i can tell the eigenvalues are 2,1,0,-1,-2... and i can tell the eigenvectors would have a=e and b=d... but that's because i know what this matrix is... but if i'll see some matrix with different values then this roaming around... i don't know what i'll do, i don't think trying to solve the standard polynom of it is a good idea... and after knowing the eigenvalues one has to solve the set of equations to find the eigenvectors -yuck!-
 
Last edited:
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  • #2
Well it's a symmetrical matrix so it's surely diagonalizable.
I don't see any special 'trick' although the classical way (add [itex]-\lambda[/itex] on the main diagonal and compute the determinant) shouldn't be too hard thanks to the many 0's... (e.g. expand the determinant to the first row or column).
 
  • #3
yeah, I am just REALLY lazy :tongue2:
i thought there's a trick that would make me see in a sec the right solution...
i only know the trick for block diagonal, and its not useful here...
 
  • #4
Perhaps there is, but I then I don't know it :smile:

If you know the (normal) method and you're only interested in the solution, why not use a computer program?
 
  • #5
well, the advantage of knowing helpful shortcuts is that you develop some intuition, which is pretty useful...

for example, if i got a block diagonal matrix i know the different blocks are orthogonal, which means vectors with parts that belong only to a certain block would stay in that block... and that's why i can find eigenvectors seperately for each block.
 
Last edited:
  • #6
Of course, it was just a suggestion if you were only looking for the answer.
It's by far a better choice to do it yourself, if you wish to develop your mathematical intuition :smile:
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to understand the properties and behavior of linear transformations. Eigenvalues represent the scaling factor of an eigenvector when it is subjected to a linear transformation.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they help us understand the behavior of linear transformations and can be used to simplify complex systems. They also have a wide range of applications in fields such as physics, engineering, and data analysis.

3. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, we need to first find the characteristic polynomial of a given matrix. This polynomial is then solved to find the eigenvalues. The corresponding eigenvectors can then be found by solving a system of linear equations using the eigenvalues.

4. Can matrices have complex eigenvalues and eigenvectors?

Yes, matrices can have complex eigenvalues and eigenvectors. This is because the characteristic polynomial of a matrix can have complex roots, which correspond to complex eigenvalues. The eigenvectors associated with these complex eigenvalues will also be complex.

5. What is the significance of the eigendecomposition of a matrix?

The eigendecomposition of a matrix is a way to express a matrix as a product of its eigenvalues and eigenvectors. This can help us understand the behavior of the matrix and simplify calculations involving the matrix. It is also an important tool in solving systems of linear differential equations.

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