Using inverse to find eigenvalues

In summary: Maybe the OP thought the discussion was about a particular matrix, but that's a pretty big assumption.
  • #1
member 731016
Homework Statement
Pleases ee below
Relevant Equations
Please see below
For this,
1684729405326.png

I don't understand how if ##(A - 2I_2)^{-1}## has an inverse then the next line is true.

Many thanks!
 

Attachments

  • 1684729399030.png
    1684729399030.png
    10 KB · Views: 109
Physics news on Phys.org
  • #2
$$
A-2I =\begin{pmatrix}1&-2\\1&-2\end{pmatrix}-\begin{pmatrix}2&0\\0&2\end{pmatrix}=\begin{pmatrix}-1&-2\\1&-4\end{pmatrix}
$$
and thus ##(A-2I)^{-1}=\dfrac{1}{6}\begin{pmatrix}-4&2\\-1&-1\end{pmatrix}##

So ##A-2I## is invertible. Since we have ##A\cdot \begin{pmatrix}x\\y\end{pmatrix}=2I\cdot \begin{pmatrix}x\\y\end{pmatrix},## we get
$$
A\cdot \begin{pmatrix}x\\y\end{pmatrix}-2I\cdot \begin{pmatrix}x\\y\end{pmatrix}= (A-2I)\begin{pmatrix}x\\y\end{pmatrix}=\begin{pmatrix}0\\0\end{pmatrix}
$$
Applying ##(A-2I)^{-1}## on both sides results in
$$
(A-2I)^{-1}\cdot (A-2I)\cdot \begin{pmatrix}x\\y\end{pmatrix}= I\cdot \begin{pmatrix}x\\y\end{pmatrix} =\begin{pmatrix}x\\y\end{pmatrix} =(A-2I)^{-1}\cdot \begin{pmatrix}0\\0\end{pmatrix}=\begin{pmatrix}0\\0\end{pmatrix}
$$
 
  • Like
Likes member 731016
  • #3
The thread title, "Using inverse to find eigenvalues," doesn't make sense to me. When your goal is to find the eigenvalues and eigenvectors for a given matrix A, the matrix expression you work with is by definition noninvertible. The process of finding eigenvalues involves a matrix expression whose determinant is zero; i.e., ##|A - \lambda I| = 0##. The determinant of a invertible matrix is always nonzero.

The matrix expression ##A - 2I_2## in this thread turns out to be invertible precisely because 2 is not an eigenvalue of A. If the author of the material in the picture you uploaded has a point, it's not clear to me what it is.

The matrix A - 2I that fresh_42 shows is the same as the matrix A I found in your thread from yesterday, namely ##A = \begin{bmatrix}-1 & -2 \\ 1 & -4 \end{bmatrix}##. I mentioned yesterday that the eigenvalues for this matrix expression happen to be -2 and -3.

The matrix ##A + 2I_2 = A - (-2)I_2 = \begin{bmatrix}1 & -2 \\ 1 & -2 \end{bmatrix}##. Because the determinant of ##A + 2I_2 = 0##, ##A + 2I_2## does not have an inverse. The same is true for the matrix ##A + 3I_2 = A - (-3)I_2##.

The process of finding an eigenvalue ##\lambda## for a matrix A is this:
  1. Write the matrix ##A - \lambda I_n##, with n = 2 for 2 x 2 matrices, n = 3 for 3 x 3 matrices, and so on.
  2. Set the determinant of ##A - \lambda I_n## to zero, and solve the resulting polynomial involving powers of ##\lambda##.
 
Last edited:
  • Like
Likes member 731016
  • #4
fresh_42 said:
$$
A-2I =\begin{pmatrix}1&-2\\1&-2\end{pmatrix}-\begin{pmatrix}2&0\\0&2\end{pmatrix}=\begin{pmatrix}-1&-2\\1&-4\end{pmatrix}
$$
and thus ##(A-2I)^{-1}=\dfrac{1}{6}\begin{pmatrix}-4&2\\-1&-1\end{pmatrix}##
The attached image in post 1 of this thread doesn't specify any particular matrix, so it's not possible to determine the entries of A - 2I. You might be confusing what was in the hand-drawn sketch of the previous thread from the OP, which itself was confused.
fresh_42 said:
So ##A-2I## is invertible. Since we have ##A\cdot \begin{pmatrix}x\\y\end{pmatrix}=2I\cdot \begin{pmatrix}x\\y\end{pmatrix},## we get
$$
A\cdot \begin{pmatrix}x\\y\end{pmatrix}-2I\cdot \begin{pmatrix}x\\y\end{pmatrix}= (A-2I)\begin{pmatrix}x\\y\end{pmatrix}=\begin{pmatrix}0\\0\end{pmatrix}
$$
Applying ##(A-2I)^{-1}## on both sides results in
$$
(A-2I)^{-1}\cdot (A-2I)\cdot \begin{pmatrix}x\\y\end{pmatrix}= I\cdot \begin{pmatrix}x\\y\end{pmatrix} =\begin{pmatrix}x\\y\end{pmatrix} =(A-2I)^{-1}\cdot \begin{pmatrix}0\\0\end{pmatrix}=\begin{pmatrix}0\\0\end{pmatrix}
$$
 
  • Like
Likes member 731016
  • #5
Mark44 said:
The attached image in post 1 of this thread doesn't specify any particular matrix, so it's not possible to determine the entries of A - 2I.
Occam's razor. Why complicate things? I do not debate typos.

However, the nice part is: it does not even matter! The line of the argument remains the same if ##A## has a different form as long as ##A-2I## remains regular!
 
  • Like
Likes member 731016
  • #6
fresh_42 said:
Occam's razor. Why complicate things?
The OP is already sufficiently confused as evidenced in the thread title, in thinking that finding the inverse of a matrix plays any role in finding eigenvalues. Muddying up the water by tossing in a specific matrix where none was given doesn't help alleviate that confusion.
 
  • Like
Likes member 731016

FAQ: Using inverse to find eigenvalues

What is the inverse matrix method for finding eigenvalues?

The inverse matrix method for finding eigenvalues involves using the inverse of a matrix to simplify the process of determining its eigenvalues. Specifically, if you have a matrix \( A \), you can find its eigenvalues by solving the characteristic equation \( \det(A - \lambda I) = 0 \). The inverse matrix can sometimes provide insights or simplifications in this process, especially when dealing with certain types of matrices.

How does the inverse matrix relate to eigenvalues?

The eigenvalues of a matrix \( A \) and its inverse \( A^{-1} \) are closely related. If \( \lambda \) is an eigenvalue of \( A \), then \( \frac{1}{\lambda} \) is an eigenvalue of \( A^{-1} \). This relationship can be useful in certain computational techniques or theoretical analyses, but it does not directly simplify the process of finding eigenvalues from scratch.

Can the inverse matrix method be used for all matrices?

No, the inverse matrix method cannot be used for all matrices. Specifically, it requires that the matrix \( A \) be invertible, meaning it must have a non-zero determinant. If \( A \) is not invertible (i.e., it has a determinant of zero), then it does not have an inverse, and this method cannot be applied.

What are the advantages of using the inverse matrix method to find eigenvalues?

One advantage of using the inverse matrix method is that it can sometimes simplify calculations, especially for matrices that are difficult to handle directly. By working with the inverse, you might gain insights or find patterns that are not immediately obvious. Additionally, the relationship between the eigenvalues of \( A \) and \( A^{-1} \) can be useful in certain theoretical contexts.

What are the limitations of using the inverse matrix method to find eigenvalues?

The limitations of using the inverse matrix method include the requirement that the matrix be invertible, which excludes singular matrices. Additionally, finding the inverse of a matrix can be computationally intensive, especially for large matrices, and may not always lead to a straightforward solution for the eigenvalues. In many cases, traditional methods like solving the characteristic equation directly or using numerical algorithms may be more efficient.

Back
Top