The eigenvalues and eigenvectors of T

In summary: The eigenvectors are just the transpose of the eigenvalues.In summary, the lattice Laplacian is a mathematical function that describes the diffusion of energy in a lattice.
  • #1
Schwarzschild90
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1

Homework Statement


The eigenvectors and eigenvalues of T.PNG


Homework Equations


The lattice laplacian is defined as [itex] \Delta^2 = \frac{T}{\tau} [/itex], where T is the transition matrix [tex]
\left[ \begin{array}{cccc}
-2 & 1 & 0 & 0 \\
1 & -2 & 1 & 0 \\
0 & 1 & -2 & 1 \\
0 & 0 & 1 & -2 \end{array} \right]
[/tex]

and [tex]\tau[/tex] is a time constant, which is taken = 1.

The Attempt at a Solution


The eigenvectors and eigenvalues of T solution.PNG
 
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  • #2
remember that the eigenvector is a vector ##v=[O_{1}(j),O_{2}(j),O_{3}(j),O_{4}(j)]## (for the case ##4\times 4##), ##\tau=1##, so is ##v\Delta^{2}=\lambda v## ...
 
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  • #3
Ssnow said:
remember that the eigenvector is a vector ##v=[O_{1}(j),O_{2}(j),O_{3}(j),O_{4}(j)]## (for the case ##4\times 4##), ##\tau=1##, so is ##v\Delta^{2}=\lambda v## ...
Right.

How is the lattice Laplacian commonly defined?
 
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  • #4
Sincerely I searched on the web and I have found only this

https://en.wikipedia.org/wiki/Discrete_Laplace_operator

this is the discrete Laplace operator and yours...

Regarding your problem (in the example that you proposed) is the same to verify the system:
##[O_{1}(j),O_{2}(j),O_{3}(j),O_{4}(j)]\left[\begin{array}{cccc}-2 & 1 & 0&0 \\ 1&-2&1&0\\0&1&-2&1\\0&0&1&-2 \end{array}\right]=[\lambda O_{1}(j),\lambda O_{2}(j),\lambda O_{3}(j),\lambda O_{4}(j)]##

that is

##-2O_{1}+O_{2}=\lambda O_{1}, O_{1}-2O_{2}+O_{3}=\lambda O_{2}, ... ## and so on ...
 
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  • #5
Ssnow said:
Sincerely I searched on the web and I have found only this

https://en.wikipedia.org/wiki/Discrete_Laplace_operator

this is the discrete Laplace operator and yours...

Regarding your problem (in the example that you proposed) is the same to verify the system:
##[O_{1}(j),O_{2}(j),O_{3}(j),O_{4}(j)]\left[\begin{array}{cccc}-2 & 1 & 0&0 \\ 1&-2&1&0\\0&1&-2&1\\0&0&1&-2 \end{array}\right]=[\lambda O_{1}(j),\lambda O_{2}(j),\lambda O_{3}(j),\lambda O_{4}(j)]##

that is

##-2O_{1}+O_{2}=\lambda O_{1}, O_{1}-2O_{2}+O_{3}=\lambda O_{2}, ... ## and so on ...
Okay, I knew that definition of the lattice Laplacian. It's what we used in the course, but it was not defined as such.

Right.

Next step is solving the characteristic equation for the eigenvalues of the system.
 
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  • #6
Hi, this seems a serious article,

http://math.ucdenver.edu/~brysmith/software/Eigenvalues_of_the_discrete_laplacian_bryan_smith.pdf

I think can help you!

Ssnow
 
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  • #7
Ssnow said:
Hi, this seems a serious article,

http://math.ucdenver.edu/~brysmith/software/Eigenvalues_of_the_discrete_laplacian_bryan_smith.pdf

I think can help you!

Ssnow
I read it through, but we haven't worked that much in-depth with eigenvalues and eigenvectors with respect to the lattice Lalplacian or used applied linear algebra sufficiently for me to easily understand that. So it's slightly above my mathematical skills, but I'll talk it through with my professor tomorrow and see what I come up with.

But from what I could garner, the eigenvalues are given simply by two formulas, one for the even-valued k and one for odd-valued k.
 
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FAQ: The eigenvalues and eigenvectors of T

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used to describe linear transformations in linear algebra. Eigenvalues represent the scalar values that are multiplied by the eigenvectors when a linear transformation is applied to them. Eigenvectors are the non-zero vectors that remain unchanged in direction after the transformation.

2. How do you find the eigenvalues and eigenvectors of a linear transformation?

To find the eigenvalues and eigenvectors of a linear transformation T, you must first find the characteristic equation of T by subtracting the identity matrix from T, taking the determinant, and setting it equal to 0. Then, the solutions to the characteristic equation will be the eigenvalues. Once you have the eigenvalues, you can find the eigenvectors by solving the system of equations (T - λI)x = 0, where λ is an eigenvalue and x is an eigenvector.

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

Eigenvalues and eigenvectors are important because they provide information about the behavior of a linear transformation. They can be used to understand the stretching or shrinking effects of a transformation, and to find the direction in which a transformation is most dominant. They are also used in many applications, such as data analysis, image processing, and quantum mechanics.

4. Can a linear transformation have more than one eigenvalue and eigenvector?

Yes, a linear transformation can have multiple eigenvalues and eigenvectors. In fact, most linear transformations have multiple eigenvalues and eigenvectors. The number of eigenvalues and eigenvectors is equal to the dimension of the vector space in which the transformation is taking place.

5. How are eigenvalues and eigenvectors used in diagonalization?

Eigenvalues and eigenvectors are used in diagonalization to simplify the representation of a linear transformation. By finding a basis of eigenvectors, we can transform the original matrix into a diagonal matrix, where the diagonal elements are the eigenvalues. This makes it easier to perform computations and understand the transformation's behavior.

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