Linear Algebra Matrix Eigenvalues

In summary, lina29 was trying to solve the Homework Equations A=7x2-8x+0 and found that she made a mistake. She found that her characteristic equation was incorrect and that her eigenvalues were off by a minus sign. She found that to find v1 she needed to solve the equation A v1 = t1 v1.
  • #1
lina29
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0

Homework Statement



The matrix A has 3 distinct eigenvalues t1< t2< t3. Let vi be the unique eigenvector associated to ti with a 1 as its first nonzero component. Let

D= [t1 0 0
0 t2 0
0 0 t3]

and P= [v1|v2|v3] so that the ith column of P is the eigenvector vi associated to ti

A=
7 2 -8
0 1 0
4 2 -5

a) Find D
b) Find P
c) Find P-1

Homework Equations





The Attempt at a Solution


My thought to find D was to find the characteristic equation of A which I found to be (t-3)(t+1)=0 so the eigenvalues would be t=-3, t=1 I then plugged in these values into the matrix D so D became
3 0 0
0 -1 0
0 0 0

but it was counting it wrong. What did I mess up on?
 
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  • #2
Hi lina29 :smile:

I'm afraid your characteristic equation is a little off.
How did you get it?

And your eigenvalues are not the solution of the equation you have.
They are off by a minus sign.

You're supposed to get 3 distinct eigenvalues.
(They should be 3, -1, 1.)

Oh, and according to your problem statement your eigenvalues should be sorted low to high on your diagonal.
 
  • #3
To find the determinant I did aei+bfg+cdh-ceg-bdi-afh. So I got (t-7)(t-1)(t+5)+0+0-(8)(t-1)(-4)-0-0. I simplified that to (t-7)(t-1)(t+5)-(-32)(t-1). My mistake was that I accidently crossed out the t-1 on both sides. From there how would I get to P?
 
  • #4
lina29 said:
To find the determinant I did aei+bfg+cdh-ceg-bdi-afh. So I got (t-7)(t-1)(t+5)+0+0-(8)(t-1)(-4)-0-0. I simplified that to (t-7)(t-1)(t+5)-(-32)(t-1). My mistake was that I accidently crossed out the t-1 on both sides. From there how would I get to P?

Good! :smile:Find the eigenvectors v1, v2, v3.
P is the matrix with these eigenvectors as columns.
 
  • #5
So the eigenvectors would be -1, 1, and 3. Then the matrix P would be
-1
1
3
is that it?
 
  • #6
or I guess that wouldn't be right since I need to find P inverse so P would have to be a square matrix
 
  • #7
lina29 said:
So the eigenvectors would be -1, 1, and 3. Then the matrix P would be
-1
1
3
is that it?

No. There's a difference between eigenvalues and eigenvectors.
t1=-1 is your first eigenvalue.
v1 is the vector belonging to this eigenvalue.

To find v1 you need to solve A v1 = t1 v1.

Note that any scalar multiple of v1 also satisfies the equation, but its direction is unique.Edit: I just saw that your problem statement says that v1 should have "1 as its first nonzero component".
 
Last edited:
  • #8
ohh I got it. Thank you so much!
 
  • #9
Good! :smile:
 

FAQ: Linear Algebra Matrix Eigenvalues

1. What is a matrix in linear algebra?

A matrix is a rectangular array of numbers or variables arranged in rows and columns. It is a fundamental concept in linear algebra and is used to represent and manipulate linear equations and transformations.

2. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are properties of a square matrix. Eigenvalues are scalars that represent the scaling factor of the eigenvectors when the matrix is multiplied by them. Eigenvectors are non-zero vectors that remain in the same direction when multiplied by the matrix.

3. How do you calculate eigenvalues and eigenvectors?

To calculate eigenvalues and eigenvectors, we first find the characteristic polynomial of the matrix. The eigenvalues are the roots of the characteristic polynomial. Then, we use the eigenvalues to find the corresponding eigenvectors by solving a system of linear equations.

4. What is the importance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are crucial in many applications of linear algebra, such as solving systems of differential equations, finding principal components in data analysis, and understanding the behavior of linear transformations. They also provide important insight into the properties of a matrix.

5. Can a matrix have complex eigenvalues and eigenvectors?

Yes, a matrix can have complex eigenvalues and eigenvectors. This is because the characteristic polynomial may have complex roots. In this case, the eigenvectors will also be complex and can be represented as a combination of real and imaginary components.

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