Linear Algebra - Eigenvalues/Eigenvectors?

In summary: Since you can only have eigenvalues of +1 or -1, you should be able to write down what the diagonal matrix looks like. So you have something like:A=\left(\begin{array}{cc}1 & 0 \\0 & -1 \end{array}\right)Then you need to find a basis in which A is diagonal, and write (3,-1) in that basis.
  • #1
KIDRoach
4
0

Homework Statement


a.) If A2 = I, what are the possible eigenvalues of A?
b.) If this A is 2 by 2, and not I or -I, find its trace and determinant.
c.) If the first row is (3,-1), what is the second row?

Homework Equations


None was given, but I think:

1. det(A) = 1 for A2 = I or A-1 = A

We're studying about Matrix diagonalization and the topic is called "Diagonalization of a Matrix". The equation S[tex]\Lambda[/tex]S-1 = A is supposed to be relevant.

The Attempt at a Solution


a.) I got the possible eigenvalues to be: [tex]\lambda[/tex]1 x [tex]\lambda[/tex] 2 x ... x [tex]\lambda[/tex]n = 1
b.) tr(A) = [tex]\lambda[/tex] + [tex]1/\lambda[/tex]
det(A) = 1
c.) This is where I'm stuck... I know I'm supposed to use the equation det(A) = 1 but there are two unknowns and I only know one equation. I was going to use the equation S[tex]\Lambda[/tex]S-1 = A but then I realized that I need the eigenvectors and I can't find the eigenvectors since I don't know the whole matrix.
 
Last edited:
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  • #2
Welcome to PF!

Hi KIDRoach! Welcome to PF! :smile:

You're making this very complicated. :redface:

Hint: if det(A) = a, and det(B) = b, what is det(AB)? :wink:

(alternatively, just write A relative to a basis in which A is diagonal)
 
  • #3
Hi Tim!

Thanks for answering my question. I tried that too I think, but I don't think it's right, or at least either that's wrong or one of the earlier answer is wrong. :(

I know that det(AB) = det(A) x det(B) = ab

When I tried it to solve the equation:
[tex]

\[ \left( \begin{array}{ccc}
3 & -1 \\
0 & x \end{array} \right)\]
[/tex]

I get x = 1/3, which, when I square the A matrix, I don't get the I matrix, so the equation is inconsistent/ something is wrong... But I can't figure out which part I'm wrong at... :(
 
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  • #4
KIDRoach said:
I know that det(AB) = det(A) x det(B) = ab

ok, so if A2 = I, and if A ≠ I or -I, then det(A) = … ? :smile:
 
  • #5
The eigenvalue equation is [tex]Ax=\lambda x[/tex]. Multiply by A to obtain
[tex]
A^{2}x=\lambda Ax=>x=\lambda^{2}x
[/tex]
So for this to hold [tex]\lambda =\pm 1[/tex]
In order to compute the third part, you have two equations, one coming from the determinant equation and the other one coming from the trace equation.
 
  • #6
Just wondering, so my answers in part a and b is wrong then? No one has mentioned it yet so I'm not sure... I know that part b is wrong now, that det(A) = -1 instead of 1 ?

Tim,

If A is 2x2 and not I or -I, then det(A) = -1 right? I still don't get how to get from that point to the third though. I still get 1/3 when I use det(A) = -1 to solve the diagonal matrix equation:
Sorry for being so dumb... :frown:

hunt_mat
Wouldn't it be A2x = [tex]\lambda^{2}[/tex]x instead?
I tried using the trace equation. The thing is, the trace equation brings in the two [tex]\lambda[/tex] into the equation and adds significant complexity when I'm computing the eigenvectors.
 
  • #7
From the question, we know that A^2 =I. One thing which you may not know is that you can diagonalise a matrix via the eigenvectors. So the determinant is just the product of the eigenvalues, hence det(A)=(+1)(-1)=1. Off the top of my head, I think that the tr(A) is given by the sum of the eigenvalues, so tr(A)=0. Write you matrix as:
[tex]
A=\left(\begin{array}{cc}
-3 & 1 \\
x & y
\end{array}\right)
[/tex]
Use the two equations which I mentioned to obtain your solution.
 
  • #8
KIDRoach said:
Just wondering, so my answers in part a and b is wrong then? No one has mentioned it yet so I'm not sure... I know that part b is wrong now, that det(A) = -1 instead of 1 ?
Yes, they're wrong. Hunt_mat showed you that the eigenvalues have to be +1 or -1. So think about how A looks in the basis where it's been diagonalized. There are only four possible matrices.

Wouldn't it be A2x = [tex]\lambda^{2}[/tex]x instead?
I tried using the trace equation. The thing is, the trace equation brings in the two [tex]\lambda[/tex] into the equation and adds significant complexity when I'm computing the eigenvectors.
Remember that A2=I, so no more A's on the lefthand side.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to analyze and understand linear transformations. Eigenvalues are scalar values that represent the scaling factor of the eigenvectors, which are vectors that are not affected in direction by the transformation.

2. How are eigenvalues and eigenvectors calculated?

To find the eigenvalues and eigenvectors of a matrix, you need to solve the characteristic equation for the matrix. This involves finding the determinant of the matrix and setting it equal to zero. The solutions to the characteristic equation are the eigenvalues, and the corresponding eigenvectors can be found by plugging in each eigenvalue into the equation (A-λI)x = 0, where A is the original matrix and λ is the eigenvalue.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, including physics, engineering, and data analysis. They help us understand how a linear transformation affects a vector and can be used to simplify calculations and solve complex problems.

4. Can a matrix have complex eigenvalues and eigenvectors?

Yes, a matrix can have complex eigenvalues and eigenvectors. In fact, complex eigenvalues and eigenvectors often arise when dealing with higher dimensional matrices and are important in quantum mechanics and other areas of physics.

5. How are eigenvalues and eigenvectors used in data analysis?

In data analysis, eigenvalues and eigenvectors are used in techniques such as principal component analysis (PCA) to reduce the dimensionality of data and identify patterns and relationships between variables. They can also be used in machine learning algorithms to make predictions and classify data.

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