Proving Existence of Positive Eigenvector in 2x2 Matrix with Positive Elements

In summary: I think these are the eigenvalues. OK, so I can use lambda2 as the eigenvalue for the eigenvector associated with that eigenvalue.In summary, the student is trying to find the characteristic polynomial for a 2x2 matrix using the quadratic equation and eigenvalues. He is getting lost and needs help from his professor. After some further work, he finds that lambda2 is the eigenvalue for the eigenvector associated with the first eigenvalue (a+d +/- sqrt((a-d)^2+4bc))/2. He then uses this eigenvector to find the eigenvalue for the second eigenvalue (a+d -
  • #1
Wildcat
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Homework Statement



Let A = matrix [a b] row 1 [c d] row 2 (2x2 matrix) with a>0, b>0,c>0,d>0. Show that A has an eigenvector [x,y] (2x1) with x>0, y>0

Homework Equations





The Attempt at a Solution



I've tried finding the characteristic polynomial by using det((lambda)I - A) =0, then using the quadratic equation to find the roots (thats ugly) so I'm trying multipying the column vector x,y by some eigenvalue to get each column of the original matrix.
if c/y is an eigenvalue that corresponds to the eigenvector [ay/c, y] and d/y is an eigenvalue that corresponds to the eigenvector [ by/d , y] a,b,c,d are all >0 therefore the eigenvector values will by >0

Will this work??
 
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  • #2
Wildcat said:

Homework Statement



Let A = matrix [a b] row 1 [c d] row 2 (2x2 matrix) with a>0, b>0,c>0,d>0. Show that A has an eigenvector [x,y] (2x1) with x>0, y>0

Homework Equations





The Attempt at a Solution



I've tried finding the characteristic polynomial by using det((lambda)I - A) =0, then using the quadratic equation to find the roots (thats ugly)
It's not that bad - don't give up so easily. The Quadratic Formula shows that there are two real eigenvalues. You should be able to use one of these eigenvalues to find an eigenvector with positive entries.
Wildcat said:
so I'm trying multipying the column vector x,y by some eigenvalue to get each column of the original matrix.
if c/y is an eigenvalue that corresponds to the eigenvector [ay/c, y] and d/y is an eigenvalue that corresponds to the eigenvector [ by/d , y] a,b,c,d are all >0 therefore the eigenvector values will by >0

Will this work??
 
  • #3
Mark44 said:
It's not that bad - don't give up so easily. The Quadratic Formula shows that there are two real eigenvalues. You should be able to use one of these eigenvalues to find an eigenvector with positive entries.


OK, I'll go back and do that. So I'm assuming by your response what I did is incorrect?? If so, can you tell me why?
 
  • #4
Wildcat said:
OK, I'll go back and do that. So I'm assuming by your response what I did is incorrect?? If so, can you tell me why?

I must be doing something wrong trying to use the quadratic formula. I'm getting my
b=(a+d) a=1 and c=(ad-bc) then when I use the quadratic formula with those values and try to put it back into the (lambda)I - A matrix and row reduce to get to row-echelon form
OH MY! Am I still giving up too soon??
 
  • #5
Wildcat said:
OK, I'll go back and do that. So I'm assuming by your response what I did is incorrect?? If so, can you tell me why?

nevermind on telling me why its wrong, I figured out what I was doing wrong, I should have known it wasn't that easy!
 
  • #6
For the eigenvalues I got lambda = [a + d +/- sqrt((a - d)^2 + 4bc)]/2.

Then work with (A - lambdaI)x = 0 to find the eigenvector associated with each eigenvalue.
 
  • #7
Mark44 said:
For the eigenvalues I got lambda = [a + d +/- sqrt((a - d)^2 + 4bc)]/2.

Then work with (A - lambdaI)x = 0 to find the eigenvector associated with each eigenvalue.

Thats what I got for the eigenvalues but when I put it in (A-lambdaI)x=0 and try to row reduce it gets really messy. I do need to row reduce right??
 
  • #9
Mark44 said:
Yes.

So using the eigenvalue [(a+d) +√((a-d)^2 +4bc)]/2

I end up with the eigenvector of [1, and some incredibly complex expression] should it be a really complex expression??
 
  • #10
It probably shouldn't, but then I haven't worked this through. In any case, all you need to do is show that both coordinates of the eigenvector are positive, so maybe there's a shortcut you can take.
 
  • #11
Mark44 said:
It probably shouldn't, but then I haven't worked this through. In any case, all you need to do is show that both coordinates of the eigenvector are positive, so maybe there's a shortcut you can take.

Ok thanks for your help. I need to look at it some more.
 
  • #12
Wildcat said:
So using the eigenvalue [(a+d) +√((a-d)^2 +4bc)]/2

I end up with the eigenvector of [1, and some incredibly complex expression] should it be a really complex expression??

I worked on this some more.
when I put the eigenvalue into the matrix lambdaI -A
I get ([(a+d) +√((a-d)^2 +4bc)]/2 - a)x - by =0 if y=1
then x = 2b/(-a+d+√(a-d)²+4bc) is that correct??

I've tried every scenario I can think of and the denominator is always positive which makes the whole expression positive since b is positive ??
 
  • #13
For lambda1, I get [a + d + √((a - d)^2 + 4bc)]/2
For lambda2, I get [a + d - √((a - d)^2 + 4bc)]/2


I think these are what you get.

To make things a little easier, let's define D = √((a - d)^2 + 4bc) and deal with it later.

After substituting lambda1 into the matrix to get lambda1*I - A, I got
[tex]\left[ \begin{array}{cc} \frac{-a+d + D}{2} & -b\\-c & \frac{a-d-D}{2}\end{array}\right][/tex]

After a couple of row-reduction steps, I got
[tex]\left[ \begin{array}{cc} 1 & \frac{2b}{a-d -D}\\0 & 0\end{array}\right][/tex]

I had to combine two fractions in the row-2, col-2 entry, but everything dropped out, as it should.

The top row of this matrix says that 1x + 2b/(a - d - D) * y = 0, which is the same as saying
x = -2b/(a - d - D) * y

Without loss of generality you can take y = 1, which makes x equal to the coefficient -2b/(a - d - D).

Recall from before that D = √((a - d)^2 + 4bc).

(a - d)^2 < (a - d)^2 + 4bc, since b and c are positive.
So |a - d| = √((a - d)^2) < √((a - d)^2 + 4bc)

This means that the expression a - d - D is negative, and so is -2b, so when y = 1, x > 0.
 
  • #14
Mark44 said:
For lambda1, I get [a + d + √((a - d)^2 + 4bc)]/2
For lambda2, I get [a + d - √((a - d)^2 + 4bc)]/2


I think these are what you get.

To make things a little easier, let's define D = √((a - d)^2 + 4bc) and deal with it later.

After substituting lambda1 into the matrix to get lambda1*I - A, I got
[tex]\left[ \begin{array}{cc} \frac{-a+d + D}{2} & -b\\-c & \frac{a-d-D}{2}\end{array}\right][/tex]

After a couple of row-reduction steps, I got
[tex]\left[ \begin{array}{cc} 1 & \frac{2b}{a-d -D}\\0 & 0\end{array}\right][/tex]

I had to combine two fractions in the row-2, col-2 entry, but everything dropped out, as it should.

The top row of this matrix says that 1x + 2b/(a - d - D) * y = 0, which is the same as saying
x = -2b/(a - d - D) * y

Without loss of generality you can take y = 1, which makes x equal to the coefficient -2b/(a - d - D).

Recall from before that D = √((a - d)^2 + 4bc).

(a - d)^2 < (a - d)^2 + 4bc, since b and c are positive.
So |a - d| = √((a - d)^2) < √((a - d)^2 + 4bc)

This means that the expression a - d - D is negative, and so is -2b, so when y = 1, x > 0.

My expression for x is technically the same, but yours would be correct because of the explanation as to why the expression is positive?? I'm just trying to get the reasoning correct.
 
  • #15
I don't understand your question. The goal of this problem is to show that there is an eigenvector with both coordinates positive. That's what my explanation is doing. Is there some of it you don't understand?
 
  • #16
Mark44 said:
I don't understand your question. The goal of this problem is to show that there is an eigenvector with both coordinates positive. That's what my explanation is doing. Is there some of it you don't understand?


Sorry, I do completely understand. I'm just trying to overkill it in my mind so my confidence will increase. Thank you soooo much for your help!
 

Related to Proving Existence of Positive Eigenvector in 2x2 Matrix with Positive Elements

What is linear algebra?

Linear algebra is a branch of mathematics that deals with linear equations and their representations in vector spaces. It involves the study of vectors, matrices, and linear transformations.

What are eigenvectors?

Eigenvectors are special vectors that do not change direction when multiplied by a matrix. They have the property that they are only scaled by a constant factor when multiplied by a matrix, making them useful for solving systems of equations.

Why are eigenvectors important?

Eigenvectors are important because they can help us understand the behavior of linear transformations and systems of equations. They are also used in various applications such as machine learning, physics, and engineering.

How do you find eigenvectors?

To find eigenvectors, we first need to find the eigenvalues of a matrix. This can be done by solving the characteristic equation. Once we have the eigenvalues, we can plug them back into the original matrix to find the corresponding eigenvectors.

What is the significance of the eigenvalues of a matrix?

The eigenvalues of a matrix represent the scaling factor for the corresponding eigenvectors. They also give important information about the behavior and properties of a matrix, such as its determinants and traces.

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