Finding Eigenvalues for Tridiagonal Matrix - Showing Orthogonality

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In summary: A using the eigenvectors v_k = [ sin(pi*k*x_j] for j = 1:J-1 and k = 1:J-1. He writes A * v_k = L_k * v_k, but gets stuck trying to solve for L_k. He wonders if he's on the right track and if there's a way to simplify the equation. He also needs to show that the eigenvectors v_k are orthogonal and wonders if he should use the dot product or the integral inner product to do so. In summary, Carl is trying to find the eigenvalues of a tridiagonal matrix using a specific set of eigenvectors and needs
  • #1
eckiller
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I have the tridiagonal matrix (which comes from the backward Euler scheme)

A =
[ 1+2M - M 0 ... ]
[ -M 1+2M 0 ... ]
[ ... ]
[ -M 1+2M ]


I am given that the eigenvectors are v_k = [ sin(pi*k*x_j ] for j = 1:J-1 and k = 1:J-1

I am to find the eigenvalues L_k.

I tried just writing A * v_k = L_k * v_k

Then doing the multiplication on the left hand side I can get a relationship from one equation:

-M*sin(sin(pi*k*x_j-1) + (1+2M)sin(pi*k*x_j) -M*sin(pi*k*x_j+1) = L_k * sin(pi*k*x_j)

I get stuck here. Is this the right approach? Is there someway to solve for L_k?


Also: I am to show the v_k are orthogonal. Would I use dot product or the integral inner product to show this?

Thanks for any help.
 
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  • #2
eckiller said:
I get stuck here. Is this the right approach? Is there someway to solve for L_k?

You've got the right answer. If you think the instructor wants you to reduce it to simplest form, then look up the trig identity for sin(A+B) to bring those nasty sines into more equal terms.

eckiller said:
Also: I am to show the v_k are orthogonal. Would I use dot product or the integral inner product to show this?
Thanks for any help.

I would guess that they're wanting you to use the integral inner product. That is, they want you to integrate over x the dot product. But the nature of dot produts should give you that the dot product itself is zero for all x so your integral will be trivial.

Good luck. And if you want your questions answered faster, try learning LaTex and making your questions pretty.

Carl
 

1. How do I find the eigenvalues of a tridiagonal matrix?

To find the eigenvalues of a tridiagonal matrix, you can use the tridiagonalization algorithm, which reduces the matrix to a simpler form that is easier to work with. Then, you can use standard methods such as the QR algorithm or the power method to find the eigenvalues.

2. What is the significance of finding eigenvalues for a tridiagonal matrix?

Finding eigenvalues for a tridiagonal matrix can help in solving a variety of problems in mathematics, physics, and engineering. For example, eigenvalues can be used to solve systems of linear equations, calculate the stability of a dynamic system, and analyze the behavior of quantum mechanical systems.

3. What is the role of orthogonality in finding eigenvalues for a tridiagonal matrix?

Orthogonality is an important concept in finding eigenvalues for a tridiagonal matrix because it allows us to simplify the calculations involved. By using an orthogonal transformation, we can reduce the matrix to a diagonal form, making it easier to determine the eigenvalues.

4. How does the size of a tridiagonal matrix affect the process of finding eigenvalues?

The size of a tridiagonal matrix does not significantly affect the process of finding eigenvalues. However, larger matrices may require more computational power and time to find the eigenvalues. This is why efficient algorithms and methods are important in finding eigenvalues for large matrices.

5. Can I use software or programming languages to find eigenvalues for a tridiagonal matrix?

Yes, there are many software and programming languages that have built-in functions or libraries for finding eigenvalues of matrices. Some popular options include MATLAB, Python's NumPy library, and R's eigen function. These tools can greatly simplify the process of finding eigenvalues for a tridiagonal matrix.

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