- #1
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:$$
A-2I =\begin{pmatrix}1&-2\\1&-2\end{pmatrix}-\begin{pmatrix}2&0\\0&2\end{pmatrix}=\begin{pmatrix}-1&-2\\1&-4\end{pmatrix}
$$
and thus ##(A-2I)^{-1}=\dfrac{1}{6}\begin{pmatrix}-4&2\\-1&-1\end{pmatrix}##
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
$$
(A-2I)^{-1}\cdot (A-2I)\cdot \begin{pmatrix}x\\y\end{pmatrix}= I\cdot \begin{pmatrix}x\\y\end{pmatrix} =\begin{pmatrix}x\\y\end{pmatrix} =(A-2I)^{-1}\cdot \begin{pmatrix}0\\0\end{pmatrix}=\begin{pmatrix}0\\0\end{pmatrix}
$$
Occam's razor. Why complicate things? I do not debate typos.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.
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.fresh_42 said:Occam's razor. Why complicate things?
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.
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.
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.
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.
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.