How can I find the formula for M^n if M is a diagonalizable matrix?

  • Thread starter UrbanXrisis
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In summary: I really don't know what you're doing here. It's like you're making things up as you go. By the way, if you think the determinant of S is -1, then how is it that you're getting 2 and -2 as entries of the inverse? Since the determinant is supposed to be the product of the entries of the inverse, it should be 1, not -1. You really need to slow down and think about what you're doing. You need to understand what a determinant is, and what a matrix is, and what an eigenvalue is, and what an eigenvector is. You need to understand what you're doing.
  • #1
UrbanXrisis
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[tex]M = \left(\begin{array}{cc}4&-1 \\ 2&7 \end{array}\right)[/tex]

I need to show M^n as a formula of entries where n>0:

so say [tex]M = \left(\begin{array}{cc}4&-1 \\ 2&7 \end{array}\right)[/tex] is diagonalizable: [tex]M = SDS^{-1}[/tex]

then [tex]M^2=S^2 D^2 S^{-2} [/tex]

and... [tex]M^3=S^3 D^3 S^{-3} [/tex]

I can see from this that [tex]D^n = \left(\begin{array}{cc}\alpha^n&0 \\ 0&\beta^2 \end{array}\right)[/tex] assuming [tex]\alpha= \lambda_1[/tex] and that [tex]\beta= \lambda_2[/tex]

[tex]\alpha= 5[/tex] and [tex]\beta= 6[/tex]

[tex]D^n = \left(\begin{array}{cc}5^n&0 \\ 0&6^n \end{array}\right)[/tex]

so to find S...

[tex]\left(\begin{array}{cc}4-\alpha&-1 \\ 2&7-\beta \end{array}\right)[/tex]
[tex]\left(\begin{array}{cc}-1&-1 \\ 2&2 \end{array}\right)[/tex]

[tex]v_1= \left(\begin{array}{c}1\\ -1 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}2&1 \\ 2&1 \end{array}\right)[/tex]

[tex]v_2= \left(\begin{array}{c}1\\ -2 \end{array}\right)[/tex]

[tex]S = \left(\begin{array}{cc}1&1 \\-1&-2 \end{array}\right)[/tex]

[tex]det(S)=-1[/tex]

[tex]S^{-1} = \left(\begin{array}{cc}-1&-1 \\1&2 \end{array}\right)[/tex]

this is where I am stuck, i don't know how to get S^n or S^-n

any ideas?
 
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  • #2
UrbanXrisis said:
then [tex]M^2=S^2 D^2 S^{-2} [/tex]

and... [tex]M^3=S^3 D^3 S^{-3} [/tex]
Not true.
M = SDS^-1.
MM = SDS^-1SDS^-1
How does this simplify?
 
  • #3
AKG helped me out and I realized that [tex]M^n=S D^n S^{-1} [/tex]

what i don't understand now is now a diagonal matrix can be multiplied with other matricies to get a matrix with non-zero numbers?

[tex]D^n = \left(\begin{array}{cc}5^n&0 \\ 0&6^n \end{array}\right)[/tex]

so D multiplied by some other matrix will always be [tex] \left(\begin{array}{cc}a&0 \\ 0&b \end{array}\right)[/tex]

so how does this produce the original matrix [tex]M = \left(\begin{array}{cc}4&-1 \\ 2&7 \end{array}\right)[/tex]?
 
  • #4
Multiply D with the matrix full of 1's. It has no zeroes. Anyways, we want to know how to find an invertible matrix S such that M = SDS-1. You were on the right track to finding S in that other thread.
 
  • #5
UrbanXrisis said:
AKG helped me out and I realized that [tex]M^n=S D^n S^{-1} [/tex]

what i don't understand now is now a diagonal matrix can be multiplied with other matricies to get a matrix with non-zero numbers?

[tex]D^n = \left(\begin{array}{cc}5^n&0 \\ 0&6^n \end{array}\right)[/tex]

so D multiplied by some other matrix will always be [tex] \left(\begin{array}{cc}a&0 \\ 0&b \end{array}\right)[/tex]
No, that's not true! Did you actually try it?

so how does this produce the original matrix [tex]M = \left(\begin{array}{cc}4&-1 \\ 2&7 \end{array}\right)[/tex]?
How did you get D? Wasn't it D= SMS-1? So then M= S-1DS.

You saw, of course, that the numbers on the diagonal of D are the eigenvalues of M. A standard way of finding S is to use the corresponding eigenvectors as columns of S.
 
  • #6
Lets give it a try:

[tex]M = \left(\begin{array}{cc}4&-1 \\ 2&7 \end{array}\right)[/tex]

[tex]M^n=S D^n S^{-1} [/tex]

Find the eigenvalues:
[tex]\left(\begin{array}{cc}4-\lambda&-1 \\ 2&7-\lambda \end{array}\right)[/tex]

[tex]\lambda_1=5, \lambda_2=6[/tex]

This mean:

[tex]D = \left(\begin{array}{cc}5&0 \\ 0&6 \end{array}\right)[/tex]

[tex]D^n = \left(\begin{array}{cc}5^n&0 \\ 0&6^n \end{array}\right)[/tex]

To find the eigenvectors:
[tex]\left(\begin{array}{cc}4-\lambda_1&-1 \\ 2&7-\lambda _1 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}4-5&-1 \\ 2&7-5 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}-1&-1 \\ 2&2 \end{array}\right)[/tex]

Solving the matrix:
[tex]\left(\begin{array}{ccc}-1&-1&0\\ 2&2 &0 \end{array}\right)[/tex]

[tex]v_1=\left(\begin{array}{c}1\\-1 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}4-\lambda_2&-1 \\ 2&7-\lambda_2 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}4-6&-1 \\ 2&7-6 \end{array}\right)[/tex]

[tex]\left(\begin{array}{cc}-2&-1 \\ 2&1 \end{array}\right)[/tex]

Solving the matrix:
[tex]\left(\begin{array}{ccc}-2&-1&0\\ 2&1 &0 \end{array}\right)[/tex]

[tex]v_2=\left(\begin{array}{c}1\\-2 \end{array}\right)[/tex]

The eigenspace is then:
[tex]S=\left(\begin{array}{cc}1&1 \\ -1&-2 \end{array}\right)[/tex]

[tex]det\left(\begin{array}{cc}1&1 \\ -1&-2 \end{array}\right)=-1[/tex]

[tex]S^{-1}=\left(\begin{array}{cc}-1&-1 \\ 1&2 \end{array}\right)[/tex]

So this means that:
[tex]M= \left(\begin{array}{cc}1&1 \\ -1&-2 \end{array}\right)\left(\begin{array}{cc}5&0 \\ 0&6 \end{array}\right) \left(\begin{array}{cc}-1&-1 \\ 1&2 \end{array}\right) [/tex]

[tex]M^n= \left(\begin{array}{cc}1&1 \\ -1&-2 \end{array}\right)\left(\begin{array}{cc}5^n&0 \\ 0&6^n \end{array}\right) \left(\begin{array}{cc}-1&-1 \\ 1&2 \end{array}\right) [/tex]

When I solve this out.. I get:

[tex]M^n= \left(\begin{array}{cc}-5^n+6^n&-5^n+2(6^n) \\ 5^n-2(6^n)&5^n-4(6^n) \end{array}\right) [/tex]

When I sub in n=1, I do not get back the original matrix... could someone help?
 
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  • #7
UrbanXrisis said:
Solving the matrix:
[tex]\left(\begin{array}{cc}-1&-1&0\\ 2&2 &0 \end{array}\right)[/tex]
You don't solve a matrix, you solve an equation, and the equation you want to solve is:

[tex]\left(\begin{array}{cc}-1&-1\\ 2&2 \end{array}\right)v_1 = \left (\begin{array}{c}0\\0\end{array}\right)[/tex]

for v1, which coincidentally you have:
[tex]v_1=\left(\begin{array}{c}1\\-1 \end{array}\right)[/tex]
Next
Solving the matrix:
[tex]\left(\begin{array}{cc}-2&-1&0\\ 2&1 &0 \end{array}\right)[/tex]
[tex]v_2=\left(\begin{array}{c}1\\-2 \end{array}\right)[/tex]
Again, you're solving the equation:

[tex]\left(\begin{array}{cc}-2&-1\\ 2&1 \end{array}\right)v_2 = \left(\begin{array}{c}0\\0\end{array}\right )[/tex]

which you have already done.
The eigenspace is then:
[tex]S=\left(\begin{array}{cc}1&-1 \\ 2&-2 \end{array}\right)[/tex]
Just as a matrix is not something you solve, a matrix is not an eigenspace either. Moreover, it's not clear as to how you got this matrix. You're supposed to let S be the matrix whose rows (or columns, I can't remember) are the eigenvectors you found. You had vectors (1 -1)T and (1 -2)T, so I don't know where you're getting this S from.
[tex]det\left(\begin{array}{cc}-1&-1 \\ 2&2 \end{array}\right)=-1[/tex]
First of all, this matrix you're finding the determinat of is not the S you wrote down just a few lines above, and moreover it still isn't a matrix whose rows/columns are eigenvectors. It's yet another unexplained matrix. Thirdly, the determinant of that matrix there isn't -1, it's 0. Just look at the columns, they're the exact same, so the columns are clearly linearly dependent, so the determinant is 0. Of course, a very simply computation: (-1)(2) - (-1)(2) = 0 shows this as well. The original matrix for S that you had also has determinant 0, since its second row is just 2x the first, so they rows are dependant, hence determinant is 0. Again, a computation shows this: 1(-2) - (2)(-1) = 0.
[tex]S^{-1}=\left(\begin{array}{cc}-1&1 \\ -2&2 \end{array}\right)[/tex]
No matter which of the two matrices you're talking about, this is wrong since neither of those matrices are invertible (both have det 0). And I have no idea how you came up with this answer. I mean, if you did the thing you learned from high school: Take S, switch the things in positions a and d, replace c and d with their negatives, then divide the whole thing by det(S) [which you thought to be -1], you still wouldn't get what you have above, no matter which of the two matrices were taken to be S.
 
  • #8
Okay, it looks as though you've edited your post. Now everything's right up to the point where you find S-1 (well, you still don't solve matrices or call them eigenspaces, but anyways...). It looks like all you've done is taken S and divided by det(S) [which is indeed -1].
 
  • #9
sorry... still not so good with LaTeX.
 
  • #10
thanks for catching that mistake and then help AKG, I've got it figured out!
 

1. What is a diagonalizable matrix?

A diagonalizable matrix is a square matrix that can be transformed into a diagonal matrix through a similarity transform. This means that the matrix has a set of linearly independent eigenvectors that can be used to form the transformation matrix.

2. How do you determine if a matrix is diagonalizable?

A matrix is diagonalizable if it has n linearly independent eigenvectors, where n is the size of the matrix. This can be checked by finding the eigenvalues and eigenvectors of the matrix and checking if they are linearly independent.

3. What are the benefits of having a diagonalizable matrix?

A diagonalizable matrix is easier to work with in calculations and has simpler properties compared to a non-diagonalizable matrix. It also allows for easier identification of eigenvalues and diagonal elements, which can provide useful information about the matrix.

4. Can all matrices be diagonalizable?

No, not all matrices are diagonalizable. For a matrix to be diagonalizable, it must have n linearly independent eigenvectors, where n is the size of the matrix. If a matrix does not have enough linearly independent eigenvectors, it cannot be diagonalizable.

5. How is diagonalization used in real-world applications?

Diagonalization is used in various fields such as physics, engineering, and computer science. It can be used to simplify complex systems and make calculations easier. In physics, diagonalization is used in quantum mechanics to solve problems related to energy levels and observables. In engineering, diagonalization is used in control systems to simplify the analysis and design of systems. In computer science, diagonalization is used in data compression and image processing algorithms.

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