Eigenvalues of Laplacian Matrix

In summary, the conversation discusses a formula related to graphs and matrices, specifically the Laplacian matrix. The conversation also mentions orthogonal eigenvectors and their corresponding eigenvalues. The author explains that the eigenvalues are listed in increasing order, and provides an example to clarify the understanding.
  • #1
member 428835
hi pf!

I am reading a text and am stuck at a part. this is what is being said:

If ##g## is a graph we have ##L(g) + L(\bar{g}) = nI - J## where ##J## is the matrix of ones. Let ##f^1,...f^n## be an orthogonal system of eigenvectors to ##L(g) : f^1 = \mathbb{1}## and ##L(g)f^i = \lambda_i f^i##. Evidently ##L(g) + L(\bar{g}) = nI - J## implies ##L(\bar{g}) f^1 = 0##.

Now for ##2 \leq i \leq n## we have ##L(\bar{g}) f^i = (n-\lambda_i)f^i##

The text continues by saying ##\lambda_1(\bar{g}) = 0##

Thus we have that ##\lambda_i(\bar{g}) = n - \lambda_{n-i+2}(g) : i \geq 2##. This is the part I don't understand. If eigenvalues of ##L(g)## are eigenvalues to ##L(\bar{g})## then I see that ##\lambda_i (\bar{g})f^i = (n-\lambda_i)f^i \implies \lambda_i (\bar{g}) = n-\lambda_i(g)## but the index is now wrong...please help me out!

Thanks so much!

Josh
 
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  • #2
Can anyone help me out? Am I in the wrong section?
 
  • #3
That is because the author puts eigenvalues in the increasing (non-decreasing, to be precise) order. You are correct that ##L(\overline g) f^i = (n-\lambda_i) f^i## for ##i\ge2##, so ##n-\lambda_i##, ##i\ge 2## are eigenvectors of ##L(\overline g)##. Putting them into non-decreasing order you get exactly the formula you quoted.
 
  • #4
Hawkeye18 said:
That is because the author puts eigenvalues in the increasing (non-decreasing, to be precise) order. You are correct that ##L(\overline g) f^i = (n-\lambda_i) f^i## for ##i\ge2##, so ##n-\lambda_i##, ##i\ge 2## are eigenvectors of ##L(\overline g)##. Putting them into non-decreasing order you get exactly the formula you quoted.
Could you elaborate a little? I'm a little confused on what you mean by "putting" them in non-decreasing order.
 
  • #5
The eigenvalues of ##L(g)## are ##0=\lambda_1\le \lambda_2\le\lambda_3 \le \ldots \le \lambda_n##. The eigenvalues of ##L(\overline g)## are ##0, n-\lambda_1, n-\lambda_2, \ldots, n-\lambda_n##. From the above inequalities for ##\lambda_i## we can write ##n-\lambda_n\le n-\lambda_{n-1}\le\ldots\le n-\lambda_2##. A Laplacian is always positive semidefinite, so all eigenvalues are non-negative. So, ##0=\lambda_1\le n-\lambda_n\le n-\lambda_{n-1}\le\ldots\le n-\lambda_2## gives us all eigenvalues of ##L(\overline g)## in non-decreasing order. And by ##\lambda_k(\overline g)## the author means item # ##k## on this list.
 
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  • #6
Thanks so much! I really appreciate this Hawkeye!
 

1. What is the Laplacian matrix?

The Laplacian matrix, also known as the Kirchhoff matrix, is a square matrix that represents the structure of a graph. It is commonly used in mathematics and computer science to study the properties of networks and other graph structures.

2. What are eigenvalues of a Laplacian matrix?

The eigenvalues of a Laplacian matrix are the values that, when multiplied by a vector, give back the same vector multiplied by a scalar. In other words, they represent the "stretching" or "squishing" factor of a vector when it is multiplied by the matrix.

3. How are eigenvalues of a Laplacian matrix related to the graph structure?

The eigenvalues of a Laplacian matrix are closely related to the graph structure, as they can provide information about the connectivity and properties of the graph. For example, the number of eigenvalues that are equal to zero is equal to the number of connected components in the graph.

4. Why are eigenvalues of a Laplacian matrix important?

Eigenvalues of a Laplacian matrix are important because they can provide insights into the behavior and dynamics of a system represented by a graph. They can also be used to solve a variety of mathematical problems, such as finding the minimum cut in a graph or determining the stability of a network.

5. How are eigenvalues of a Laplacian matrix calculated?

The eigenvalues of a Laplacian matrix can be calculated using various methods, such as the power method or the Jacobi method. These methods involve repeated matrix multiplication and iteration to approximate the eigenvalues. In some cases, the eigenvalues can also be calculated analytically using the properties of the graph.

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