Proving Equality of Inverse Diagonalizable Matrix with Eigenvalues of 1 and -1

  • Thread starter Dustinsfl
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In summary, to show that A^{-1}=A, we can use the equation A=X\begin{bmatrix}\pm 1 & \cdots & 0 \\ \vdots & \ddots & \vdots \\ 0 & \cdots & \pm 1\end{bmatrix}X^{-1}, and by substituting in the inverse of the diagonal matrix, we get A=X\begin{bmatrix}\pm 1 & \cdots & 0 \\ \vdots & \ddots & \vdots \\ 0 & \cdots & \pm 1\end{bmatrix}^{-1}X^{-1
  • #1
Dustinsfl
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Let A be a diagonalizable matrix whose eigenvalues are all either 1 or -1. Show that [tex]A^{-1}=A[/tex].

[tex]A=X\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}X^{-1}
[/tex] and [tex]A^{-1}=X\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}^{-1}X^{-1}
[/tex]

How do I show they are equal?
 
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  • #2


How would you compute the diagonal matrix raised to any power n?
 
  • #3


rock.freak667 said:
How would you compute the diagonal matrix raised to any power n?

[tex]
A^n=X\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}^nX^{-1}

[/tex]
 
  • #4


He was asking how you compute/simplify

[tex]\begin{bmatrix}
\lambda_1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \lambda_m
\end{bmatrix}^n[/tex]
 
  • #5


gabbagabbahey said:
He was asking how you compute/simplify

[tex]\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}^n[/tex]

Raise the diagonal terms by n since this just a diagonal matrix.
 
  • #6


Right, so do that with [itex]n=-1[/itex]...what is [itex](\pm 1)^{-1}[/itex]?
 
  • #7


gabbagabbahey said:
Right, so do that with [itex]n=-1[/itex]...what is [itex](\pm 1)^{-1}[/itex]?

I don't understand what you mean with your latex code
 
  • #8


Dustinsfl said:
I don't understand what you mean with your latex code

It should be displaying properly now, try refreshing your page.
 
  • #9


[tex](\pm 1)^{-1}=\pm 1[/tex]
 
  • #10


Right, so

[tex]\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}^{-1}=\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}[/tex]

...Plug that into your equation for [itex]A^{-1}[/itex]
 
  • #11


gabbagabbahey said:
Right, so

[tex]\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}^{-1}=\begin{bmatrix}
\pm 1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & \pm 1
\end{bmatrix}[/tex]

...Plug that into your equation for [itex]A^{-1}[/itex]

Ok I understand thanks.
 

1. What is a diagonalizable matrix?

A diagonalizable matrix is a square matrix that can be written in the form A = PDP-1, where P is an invertible matrix and D is a diagonal matrix. This means that the matrix A can be transformed into a diagonal matrix through a change of basis.

2. What does it mean for a matrix to be invertible?

A matrix is invertible if there exists another matrix, called its inverse, that when multiplied together, result in the identity matrix. In other words, if A is an invertible matrix, then A-1A = AA-1 = I, where I is the identity matrix.

3. How does the inverse of a diagonalizable matrix relate to its diagonal form?

The inverse of a diagonalizable matrix A is equal to PDP-1, where P is the same invertible matrix used to transform A into its diagonal form. This means that the inverse of a diagonalizable matrix can be easily calculated from its diagonal form.

4. Are all diagonalizable matrices invertible?

Yes, all diagonalizable matrices are invertible. This is because if a matrix is diagonalizable, it means that it can be transformed into a diagonal matrix, which is always invertible. Therefore, the original matrix must also be invertible.

5. How can diagonalizable matrices be used in real-world applications?

Diagonalizable matrices have many applications in fields such as physics, engineering, and computer science. For example, they can be used to solve systems of linear equations, represent linear transformations, and simplify complex calculations. They are also useful in areas such as data compression, image processing, and cryptography.

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