Eigenspace (E's question at Yahoo Answers)

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In summary, the largest eigenvalue of the given matrix is 9, and the general form of the corresponding eigenvector is (x1,x2,x3)^T = alpha(-1,0,1)^T, where alpha is any real number.
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Hello E,

The eigenspace associated to $\lambda=9$ is $\ker (A-9I)$, that is: $$\ker (A-9 I)\equiv{}\begin{bmatrix}{-9}&{\;\;0}&{-9}\\{\;\;0}&{-9}&{\;\;0}\\{-9}&{\;\;0}&{-9}\end{bmatrix} \begin{bmatrix}{x_1}\\{x_2}\\{x_3}\end{bmatrix}= \begin{bmatrix}{0}\\{0}\\{0}\end{bmatrix} \Leftrightarrow \left \{ \begin{matrix} x_1+x_3=0\\x_2=0\end{matrix}\right.\Leftrightarrow \left \{ \begin{matrix} x_1=-\alpha\\x_2=0\\x_3=\alpha\end{matrix}\right. \;(\alpha \in\mathbb{R})$$ so, the general form of the eigenvectors corresponding to $\lambda=9$ is $(x_1,x_2,x_3)^T=\alpha(-1,0,1)^T$ with $\alpha \in\mathbb{R}$.
 

Related to Eigenspace (E's question at Yahoo Answers)

1. What is an eigenspace?

An eigenspace is a vector space associated with a specific eigenvalue of a linear transformation. It is a subspace of the original vector space that contains all of the eigenvectors corresponding to that eigenvalue.

2. How is an eigenspace calculated?

To calculate an eigenspace, we first find the eigenvalues of the linear transformation. Then, for each eigenvalue, we find the corresponding eigenvectors and combine them to form the eigenspace.

3. What is the significance of an eigenspace?

Eigenspaces are important in linear algebra because they represent the directions in which a linear transformation acts like a simple scaling operation. They also help us understand the behavior of a system under repeated transformations.

4. Can an eigenspace have more than one eigenvalue?

Yes, an eigenspace can have multiple eigenvalues. In fact, if a linear transformation has distinct eigenvalues, then the corresponding eigenspaces will be distinct as well.

5. How is an eigenspace used in real-world applications?

Eigenspaces have various applications in fields such as physics, engineering, and computer science. They are used for data compression, image processing, and solving differential equations, among other things.

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