Understanding Eigenvalues of a Matrix

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Homework Help Overview

The discussion revolves around understanding the eigenvalues of a matrix, particularly focusing on the implications of a matrix being non-invertible and the conditions under which certain equations hold true.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants express confusion regarding the implications of a matrix lacking an inverse and question the validity of certain statements related to eigenvalues. There are attempts to clarify the relationship between the matrix's properties and its eigenvalues.

Discussion Status

Participants are exploring the implications of the matrix being singular and discussing the meanings of certain equations. Some have provided insights into the nature of the matrix and its eigenvalues, while others are questioning the assumptions made in the original statements.

Contextual Notes

There are references to specific matrices and their properties, with some participants noting the lack of an inverse and discussing the kernel of the matrix. The conversation includes a mix of interpretations regarding the eigenvalues and their corresponding eigenvectors.

member 731016
Homework Statement
Please see below
Relevant Equations
Please see below
For this,
1684540765113.png

I am confused by the second line. Does someone please know how it can it be true since the matrix dose not have an inverse.

Many thanks!
 
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Why do you think it is true?
 
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ChiralSuperfields said:
Homework Statement: Please see below
Relevant Equations: Please see below

For this,
View attachment 326802
I am confused by the second line.
Me, too. I don't know what this means. ##A\cdot 0=0## no matter whether ##A## is invertible or not.
ChiralSuperfields said:
Does someone please know how it can it be true since the matrix dose not have an inverse.

Many thanks!
The matrix doesn't have an inverse. If we want to prove this by contradiction then we assume it has an inverse. Say the matrix is ##A.## Then ##A## maps the entire space ##\mathbb{R}^2## onto itself: ##A\cdot v= w.## Now, if we set ##v=\begin{pmatrix}x\\ y\end{pmatrix}## then ##A\cdot v=\begin{pmatrix}-x+2y\\-x+2y\end{pmatrix}.## But this means that both coordinates of ##w## are the same and we have no chance to get any other vector with different coordinates. So the image of ##A## is one-dimensional, not two-dimensional, so it cannot be invertible.

You can also argue with a vector in the kernel of ##A##. Can you name one for which ##A\cdot v=0## while ##v\neq 0?##
 
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The statement in the first two lines is vacuously true: if a singular matrix has an inverse, then the equality holds. The equality has no meaning because ##^{-1}## doesn't exist for this matrix.
 
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The part in the lower half of the image doesn't make much sense in the context of what you're asking.

You have ##\begin{bmatrix} 1 & -2 \\ 1 & -2\end{bmatrix} = A - 2I##
If you work this out, you find that ##A = \begin{bmatrix} -1 & -2 \\ 1 & -4\end{bmatrix}##.

Since you're asking about eigenvalues for a matrix (presumably A, above), it turns out that the eigenvalues are -2 and -3. This means that for one eigenvector ##x_1##, ##Ax_1 = -2x_1##, and for the other eigenvector ##x_2##, ##Ax_2 = -3x_2##.
 
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