Linear algebra - eigenvalues and eigenvectors and hermitian

In summary, the conversation discussed a problem involving the spectral decomposition of a Hermitian matrix. The values of lambda and x were found, but when multiplied together and added, the resulting matrix did not match the original matrix. It was discovered that normalizing the eigenvectors would resolve the issue.
  • #1
SpiffyEh
194
0

Homework Statement



I attached the problem in a picture so its easier to see.

Homework Equations





The Attempt at a Solution



These are the values i got
[tex]\lambda[/tex]_ 1 = 1
[tex]\lambda[/tex]_ 2 = -1

x_1 = [-i; 1] (x_1)^H = [i 1]
x_2 = [ i; 1] (x_2)^H = [-i 1]
* where x_1 and x_2 are 2x1 matricies, and their hermitians are 1x2

after each multiplication I got
[tex]\lambda[/tex]_ 1 x_1 (x_1)^H =
[1 -i
i 1]

[tex]\lambda[/tex]_ 2 x_2 (x_2)^H =
[-1 -i
i -1]

When I add these together I get
[0 -2i
2i 0]

which is not the original A_5. I can't figure out where I went wrong in this process. If someone could look over it and let me know that would be great. Thank you
 

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  • #2
No picture?
 
  • #3
Did you forget the picture, I can't see any.
 
  • #4
I think I forgot to press the upload button after trying to attach it. Sorry about that. I edited it and it's there now
 
  • #5
The spectral decomposition allows you to write a Hermitian matrix as a linear combination of projections onto the orthonormal basis of eigenvectors. So you need to normalize your eigenvectors, and then the 2 will nicely vanish.
 
  • #6
oh! that worked perfectly. Thank you, I would've never gotten that
 

1. What is the significance of eigenvalues and eigenvectors in linear algebra?

The eigenvalues and eigenvectors of a matrix are important because they provide a way to decompose a matrix into simpler forms, making it easier to understand and manipulate. They also play a key role in solving systems of linear equations and finding the optimal solutions to certain problems in data analysis and engineering.

2. How do you find the eigenvalues of a matrix?

To find the eigenvalues of a matrix, you first need to set up and solve the characteristic equation for the matrix. This equation is formed by taking the determinant of the matrix minus an identity matrix multiplied by a variable lambda. The solutions to this equation are the eigenvalues of the original matrix. In some cases, the eigenvalues can also be found by inspection.

3. What is the difference between an eigenvalue and an eigenvector?

An eigenvalue is a scalar that represents the amount by which an eigenvector is stretched or compressed when multiplied by a matrix. An eigenvector, on the other hand, is a vector that remains in the same direction (or opposite direction) when multiplied by a matrix. In other words, an eigenvector is a vector that is only scaled by the matrix, while an eigenvalue represents that scale factor.

4. How are eigenvalues and eigenvectors used in Hermitian matrices?

In Hermitian matrices, eigenvalues and eigenvectors have special properties. The eigenvalues are always real numbers, and the eigenvectors are orthogonal (perpendicular) to each other. This makes it easier to solve problems involving Hermitian matrices, as these properties can be used to simplify calculations and find optimal solutions.

5. Can a matrix have more than one eigenvector for the same eigenvalue?

Yes, a matrix can have multiple eigenvectors for the same eigenvalue. In fact, this is often the case for larger matrices. Each eigenvalue can have a corresponding set of eigenvectors, and these eigenvectors can be scaled by any non-zero scalar and still be valid. This can be useful in solving systems of linear equations and finding the optimal solutions to certain problems.

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