Finding Eigenspaces of a Matrix

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In summary, the given conversation discusses finding the eigenvalues and eigenspaces of the matrix A, with a specific example of a 2x2 matrix. The process involves calculating the determinant of A-λI and solving for λ, then using this value to find the eigenspace for each λ. It is important to note that the matrix used for calculating the eigenspace cannot reduce to the identity matrix, as this would result in a determinant of 0. The conversation also mentions a mistake made in the process of finding the second eigenspace, which was corrected by changing a value in the calculations.
  • #1
Dustinsfl
2,281
5
[tex]\begin{bmatrix}
3 & 2\\
4 & 1
\end{bmatrix}[/tex]
[tex]det(A-\lambda I)=\begin{vmatrix}
3-\lambda & 2\\
4 & 1-\lambda
\end{vmatrix}=(3-\lambda)(1-\lambda)-8=\lambda^2-4\lambda-5[/tex]
[tex]\lambda_{1}=5[/tex] and [tex]\lambda_{2}=-1[/tex]
When [tex]\lambda=5[/tex], [tex]\begin{bmatrix}
-2 & 2\\
4 & -4
\end{bmatrix}\Rightarrow \begin{bmatrix}
1 & -1\\
0 & 0
\end{bmatrix}[/tex]
The eigenspace for [tex]\lambda_{1}[/tex] is [tex]\begin{bmatrix}
1\\
1
\end{bmatrix}[/tex]
When [tex]\lambda=-1[/tex], [tex]\begin{bmatrix}
4 & 2\\
4 & 2
\end{bmatrix}\Rightarrow \begin{bmatrix}
1 & 0\\
0 & 1
\end{bmatrix}[/tex]
The eigenspace for[tex]\lambda_{2}[/tex] is [tex]\begin{bmatrix}
0\\
0
\end{bmatrix}[/tex]

I don't know what is going wrong but my second Eigenspace is wrong compared to the books answer which is [tex]\begin{bmatrix}
1\\
-2
\end{bmatrix}[/tex]
 
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  • #2


Dustinsfl said:
[tex]\begin{bmatrix}
3 & 2\\
4 & 1
\end{bmatrix}[/tex]
[tex]det(A-\lambda I)=\begin{vmatrix}
3-\lambda & 2\\
4 & 1-\lambda
\end{vmatrix}=(3-\lambda)(1-\lambda)-8=\lambda^2-4\lambda-5[/tex]
[tex]\lambda_{1}=5[/tex] and [tex]\lambda_{2}=-1[/tex]
When [tex]\lambda=5[/tex], [tex]\begin{bmatrix}
-2 & 2\\
4 & -4
\end{bmatrix}\Rightarrow \begin{bmatrix}
1 & -1\\
0 & 0
\end{bmatrix}[/tex]
The eigenspace for [tex]\lambda_{1}[/tex] is [tex]\begin{bmatrix}
1\\
1
\end{bmatrix}[/tex]
When [tex]\lambda=-1[/tex], [tex]\begin{bmatrix}
4 & 2\\
4 & 2
\end{bmatrix}\Rightarrow \begin{bmatrix}
1 & 0\\
0 & 1
\end{bmatrix}[/tex]
Your mistake is above. The [4 2; 4 2] matrix doesn't row reduce to the identity matrix. Try again.
Each matrix for calculating the eigenspace can't reduce to the identity; otherwise its determinant would not be zero.
Dustinsfl said:
The eigenspace for[tex]\lambda_{2}[/tex] is [tex]\begin{bmatrix}
0\\
0
\end{bmatrix}[/tex]

I don't know what is going wrong but my second Eigenspace is wrong compared to the books answer which is [tex]\begin{bmatrix}
1\\
-2
\end{bmatrix}[/tex]
 
  • #3


I had a -2 entered into my calc.
 

1. What is an eigenspace?

An eigenspace is a subspace of a vector space that consists of all the eigenvectors corresponding to a particular eigenvalue of a matrix. In other words, it is the set of all vectors that are scaled and transformed by the matrix in the same direction.

2. How do you find the eigenspace of a matrix?

To find the eigenspace of a matrix, you need to first find the eigenvalues of the matrix. Then, for each eigenvalue, you need to find the corresponding eigenvectors by solving the equation (A-λI)x=0, where A is the matrix, λ is the eigenvalue, and x is the eigenvector. The set of all eigenvectors corresponding to a particular eigenvalue forms the eigenspace.

3. Why is finding eigenspaces important?

Finding eigenspaces is important in many areas of mathematics and science. It allows us to understand the behavior and properties of a matrix, such as its diagonalizability and stability. Eigenspaces are also useful in solving systems of linear differential equations and in data analysis.

4. Can a matrix have multiple eigenspaces?

Yes, a matrix can have multiple eigenspaces. This is because a matrix can have multiple eigenvalues, and each eigenvalue has its own corresponding eigenspace. In fact, the dimension of the eigenspace is equal to the multiplicity of the eigenvalue, which is the number of times the eigenvalue appears in the characteristic polynomial of the matrix.

5. How can I use eigenspaces in applications?

Eigenspaces have various applications in different fields. In linear algebra, they can be used to diagonalize a matrix and simplify calculations. In physics, they are used to find the principal axes of a rigid body. In machine learning and data analysis, they are used to reduce the dimensionality of data and extract important features. In short, eigenspaces are a powerful tool for understanding and analyzing mathematical and scientific systems.

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