Are Similar Matrices' Eigenvalues the Same? Solving for Symmetric Matrices

In summary, the homework equations state that B has eigenvalues of 0 and 5, and that x = P*y are eigenvectors of A. The attempt at a solution found that x2*[0 1] and x2*[-2.14 1] are eigenvectors of A.
  • #1
shreddinglicks
212
6

Homework Statement


Consider matrices A = [1 2;2 4] and P = [1 3;3 6]. Using B = P^-1*A*P, verify that similar matrices have the same eigenvalues. Find the eigenvectors y for B and show that x = P*y are eigenvectors of A.

Homework Equations


B = P^-1*A*P,
x = P*y

The Attempt at a Solution


I have

P^-1 = [-2 1;1 -.333]

B = [0 0;2.333 4.999]

eigenvalues for matrices A and B are 0 and 5.

eigenvectors, y are

x2*[0 1]
x2*[-2.14 1]

eigenvectors for matrix A are

x2*[-2 1]
x2*[.5 1]

P*y = [1 3;3 6]*[0 1 ; -2.14 1]

I get x = [3 .86;6 -.42]

What am I doing wrong?
 
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  • #2
similar matrices represent the same operator on a space but with different coordinates. since eigenvalues do not depend on coordinates they have the same eigenvalues.
 
  • #3
Right, which is why I have 0 and 5 as eigenvalues for both matrices.

How do I show that x = P*y are eigenvectors of A? I feel like I'm getting the wrong answer.
 
  • #4
shreddinglicks said:

Homework Statement


Consider matrices A = [1 2;2 4] and P = [1 3;3 6]. Using B = P^-1*A*P, verify that similar matrices have the same eigenvalues. Find the eigenvectors y for B and show that x = P*y are eigenvectors of A.

Homework Equations


B = P^-1*A*P,
x = P*y

The Attempt at a Solution


I have

P^-1 = [-2 1;1 -.333]

B = [0 0;2.333 4.999]
You should leave the numbers as fractions, not as rounded decimal values.
##P^{-1} =\begin{bmatrix} -2 & 1 \\ 1 & -\frac 1 3 \end{bmatrix}## and ##B =\begin{bmatrix} 0 & 0 \\ \frac 7 3 & 5 \end{bmatrix}##
shreddinglicks said:
eigenvalues for matrices A and B are 0 and 5.

eigenvectors, y are

x2*[0 1]
x2*[-2.14 1]
The latter is really ##\begin{bmatrix} -\frac{15} 7 \\ 1\end{bmatrix}##
shreddinglicks said:
eigenvectors for matrix A are

x2*[-2 1]
x2*[.5 1]

P*y = [1 3;3 6]*[0 1 ; -2.14 1]

I get x = [3 .86;6 -.42]
Hard to tell what you did here. When I multiply Py, I get a matrix whose columns are vectors that are scalar multiples of ##\begin{bmatrix} -2 \\ 1\end{bmatrix}## and ##\begin{bmatrix} \frac 1 2 \\ 1\end{bmatrix}##
shreddinglicks said:
What am I doing wrong?
 
  • #5
shreddinglicks said:
P*y = [1 3;3 6]*[0 1 ; -2.14 1]
Your matrix of y’s on the right appears to be transposed. Might have been better off leaving them as two vectors. That would avoid this error anyway
 
  • #6
shreddinglicks said:
How do I show that x = P*y are eigenvectors of A? I feel like I'm getting the wrong answer.

This might not be an issue once you construct the x’s properly, but on the off chance you are asking the more basic question of “how”, you prove the x’s are eigenvectors of A by using the definition of an eigenvector
 
  • #7
Mark44 said:
You should leave the numbers as fractions, not as rounded decimal values.
##P^{-1} =\begin{bmatrix} -2 & 1 \\ 1 & -\frac 1 3 \end{bmatrix}## and ##B =\begin{bmatrix} 0 & 0 \\ \frac 7 3 & 5 \end{bmatrix}##
The latter is really ##\begin{bmatrix} -\frac{15} 7 \\ 1\end{bmatrix}##
Hard to tell what you did here. When I multiply Py, I get a matrix whose columns are vectors that are scalar multiples of ##\begin{bmatrix} -2 \\ 1\end{bmatrix}## and ##\begin{bmatrix} \frac 1 2 \\ 1\end{bmatrix}##

I made x2 = 1

Then I calculated P*y

This gave me

P*y = [1 3; 3 6] * [0 -15/7; 1 1] = [3 6/7; 6 -3/7]
 
  • #8
The columns of your last matrix, Py, are multiples of ##\begin{bmatrix} \frac 1 2 \\ 1\end{bmatrix}## and ##\begin{bmatrix} -2 1\end{bmatrix}##, so they are also eigenvectors of A.
 
  • #9
Yes, you're right. I didn't even notice that.

6*[.5 1]'
and
(3/7)*[-2 1]
 
  • #10
Thanks! You have been a great help.
 

What is a symmetric matrix?

A symmetric matrix is a square matrix in which the values above and below the main diagonal are equal. In other words, if the matrix is denoted as A, then A[i,j] = A[j,i]. This means that the matrix is the same when reflected along its main diagonal.

What is the significance of symmetric matrices?

Symmetric matrices have several important properties that make them useful in various mathematical applications. For example, they are easier to manipulate and solve than non-symmetric matrices, and they have real eigenvalues that can be used to analyze the matrix and its properties.

How can you tell if a matrix is symmetric?

To determine if a matrix is symmetric, you can check if A[i,j] = A[j,i] for all values of i and j. Another way is to transpose the matrix and see if it remains the same. If both of these conditions are satisfied, then the matrix is symmetric.

What is the difference between a symmetric and a skew-symmetric matrix?

A symmetric matrix is a square matrix in which the values above and below the main diagonal are equal, while a skew-symmetric matrix is a square matrix in which the values above and below the main diagonal are equal in magnitude but opposite in sign. In other words, if the matrix is denoted as A, then A[i,j] = -A[j,i].

How are symmetric matrices used in real-world applications?

Symmetric matrices have many practical applications, such as in physics, engineering, and computer science. They are commonly used to represent systems that have symmetry, such as in structural analysis, signal processing, and data compression. They are also used in optimization problems and in machine learning algorithms.

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