Showing that S is an Eigenvalue of a Matrix

In summary: This means that A has a nonzero determinant, and so there are solutions to the equation if and only if the determinant is 0. So, you can solve for S by solving for A-SI, and then substituting in the value of S that you found.In summary, you solved for the eigenvalue of A using the equation ##A-SIx = 0##.
  • #1
Drakkith
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Homework Statement


Consider an n x n matrix A with the property that the row sums all equal the same number S. Show that S is an eigenvalue of A. [Hint: Find an eigenvector.]

Homework Equations


##Ax=λx##

The Attempt at a Solution


S is just lambda here, so I begin solving this just like you would normally.
##Ax=Sx##
##Ax-Sx = 0##
##(A-SI)x = 0##

Subtracting gives me the matrix: ##\begin{bmatrix}
a_{11}-S & a_{12} & a_{13} \\
a_{21} & a_{22}-S & a_{23} \\
a_{31} & a_{32} & a_{33}-S
\end{bmatrix}##
My problem is that I don't know how to find an eigenvector from this matrix. I can't row reduce because I don't know any of the values of the matrix, and I can't recall any other way to find the eigenvector.
 
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  • #2
This one is tricky, but almost simple. I took a lucky guess after looking at it for about 5 minutes and got lucky. One hint: Try a simple eigenvector, but not one that has mostly zeros=in fact, try a very simple eigenvector without any zeros... I may give you an additional hint if you don't see what the eigenvector is that works...
 
  • #3
Charles Link said:
This one is tricky, but almost simple. I took a lucky guess after looking at it for about 5 minutes and got lucky. One hint: Try a simple eigenvector, but not one that has mostly zeros=in fact, try a very simple eigenvector without any zeros... I may give you an additional hint if you don't see what the eigenvector is that works...

Hmm. Well, using a matrix of all 1's, I get S=3 and the eigenvector is ##x_3
\begin{bmatrix}
1\\
1\\
1
\end{bmatrix}##
 
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  • #4
Drakkith said:
Hmm. Well, using a matrix of all 1's, I get S=3 and the eigenvector is ##x_3
\begin{bmatrix}
1\\
1\\
1
\end{bmatrix}##
## S ## will not be equal to 3 and/or n. ## S ## will be equal to what each row sums to. ## S ## is the eigenvalue. And yes, you found the correct eigenvector ! :)
 
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  • #5
Drakkith said:

Homework Statement


Consider an n x n matrix A with the property that the row sums all equal the same number S. Show that S is an eigenvalue of A. [Hint: Find an eigenvector.]

Homework Equations


##Ax=λx##

The Attempt at a Solution


S is just lambda here, so I begin solving this just like you would normally.
##Ax=Sx##
##Ax-Sx = 0##
##(A-SI)x = 0##

Subtracting gives me the matrix: ##\begin{bmatrix}
a_{11}-S & a_{12} & a_{13} \\
a_{21} & a_{22}-S & a_{23} \\
a_{31} & a_{32} & a_{33}-S
\end{bmatrix}##
My problem is that I don't know how to find an eigenvector from this matrix. I can't row reduce because I don't know any of the values of the matrix, and I can't recall any other way to find the eigenvector.
You certainly know that the equation ##(A-SI)x = 0## has nontrivial solutions if the determinant ##|A-SI| = 0##
Remember what operations do not change the value of a determinant. Read the problem. "Consider an n x n matrix A with the property that the row sums all equal the same number S."
 

1. What does it mean for S to be an eigenvalue of a matrix?

When S is an eigenvalue of a matrix, it means that there is a non-zero vector that, when multiplied by the matrix, results in a scalar multiple of itself. In other words, the matrix has a special relationship with this vector, known as an eigenvector, and the scalar multiple is known as the eigenvalue.

2. How can I determine if S is an eigenvalue of a given matrix?

To determine if S is an eigenvalue of a matrix, we can use the characteristic polynomial of the matrix. The characteristic polynomial is a function that is used to find the eigenvalues of a matrix. If we plug in S for the variable in the characteristic polynomial and get a value of 0, then S is an eigenvalue of the matrix.

3. Can a matrix have more than one eigenvalue?

Yes, a matrix can have multiple eigenvalues. In fact, the number of eigenvalues of a matrix is equal to the size of the matrix. However, some eigenvalues may have a multiplicity greater than 1, meaning they are repeated more than once.

4. What is the importance of finding eigenvalues in linear algebra?

Finding eigenvalues is important in linear algebra because it allows us to understand the behavior of a matrix and its corresponding linear transformation. Eigenvectors and eigenvalues also have many practical applications, such as in solving differential equations, optimization problems, and image processing.

5. How can I use eigenvalues to diagonalize a matrix?

To diagonalize a matrix, we need to find a diagonal matrix D and an invertible matrix P such that P-1AP = D, where A is the original matrix. The diagonal elements of D will be the eigenvalues of A, and the columns of P will be the corresponding eigenvectors. This process is known as diagonalization and can be useful in simplifying calculations involving the matrix.

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