Understanding Eigenvectors: Solving for Eigenvalues and Corresponding Vectors

In summary, the conversation discusses finding eigenvalues and eigenvectors for a linear transformation and determining the basis for the kernel and range of the transformation. It is mentioned that the eigenvectors found can be used to form a basis for the range, but it is unclear if they span the range. The conversation also touches on the concept of linear independence for eigenvectors with distinct eigenvalues.
  • #1
squenshl
479
4
Homework Statement
Let ##\mathbb{R}^3\mapsto \mathbb{R}^3## be given by $$T\begin{pmatrix}
x \\
y \\
z
\end{pmatrix} := \begin{pmatrix}
x+y-2z \\
2x-2z \\
y-x
\end{pmatrix}.$$

1. Give a basis of eigenvectors for ##\text{ker}(T)##.
2. Give a basis of eigenvectors for ##\text{ran}(T)##.
Relevant Equations
None
Okay so I found the eigenvalues to be ##\lambda = 0,-1,2## with corresponding eigenvectors ##v =
\begin{pmatrix}
1 \\
1 \\
1
\end{pmatrix},
\begin{pmatrix}
1 \\
0 \\
1
\end{pmatrix},
\begin{pmatrix}
1 \\
1 \\
0
\end{pmatrix}
##.
Not sure what to do next. Thanks!
 
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  • #2
What are ##\ker T## and ##\operatorname{im}T##?
 
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  • #3
squenshl said:
1. Give a basis of eigenvectors for ##\text{ker}(T)##.
2. Give a basis of eigenvectors for ##\text{ran}(T)##.
I don't think it makes sense to talk about a basis of eigenvectors for ##\text{ker}(T)## and ##\text{ran}(T)##.
It's more usual for a problem to ask for a basis for each of these subspaces.
 
  • #4
If ##T## is semisimple (diagonalizable) then we can find a basis of eigenvectors, in any case generalized eigenvectors. He has even calculated them already.
 
  • #5
Ok so the kernel of ##T## is ##(x,y,z)## such that ##T(x,y,z)=0## & this only occurs when we have ## (1,1,1)## so I guess that is the basis for the kernel right?
 
  • #6
So the basis for the range of ##T## are the other two eigenvectors.
 
  • #7
Yes. But ##(1,1,1)## is not the only vector, all multiples are as well sent to zero. If the kernel is one dimensional, then the range is two dimensional. The other two eigenvectors are in the range. Now do they span the range? Or more generally: Are eigenvectors to distinct eigenvalues always linearly independent, and why?
 
  • #8
Right the basis for the kernel is the span of ##(1,1,1)##. Yes eigenvectors are linearly independent so they do span the range thanks!
 
  • #9
squenshl said:
Right the basis for the kernel is the span of (1,1,1).
A basis for the kernel is the vector <1, 1, 1>, not the span of this vector. ##\text{Ker} (T)## is the set of all constant multiples of <1, 1, 1>; i.e., the span of <1, 1, 1>.
 
  • #10
Notice that for ##x ## the kernel ##Ax=0 = \lambda 0 ## for any ## \lambda ##
 

1. What are eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are concepts in linear algebra that are used to analyze the behavior of matrices. Eigenvectors are special vectors that do not change direction when multiplied by a certain matrix, and eigenvalues are the corresponding scalar values that represent the amount of stretching or shrinking that occurs when the eigenvector is multiplied by the matrix.

2. Why are eigenvectors and eigenvalues important?

Eigenvectors and eigenvalues are important because they provide insight into the behavior of matrices. They can be used to simplify complex systems and make predictions about their behavior. They are also essential in many fields such as physics, engineering, and computer science.

3. How do you find eigenvectors and eigenvalues?

To find eigenvectors and eigenvalues, you need to solve a special equation called the characteristic equation. This involves finding the determinant of the matrix, setting it equal to 0, and then solving for the eigenvalues. Once the eigenvalues are found, you can plug them back into the matrix to find the corresponding eigenvectors.

4. What is the significance of the eigendecomposition of a matrix?

The eigendecomposition of a matrix is the process of breaking down a matrix into its eigenvectors and eigenvalues. This is significant because it allows us to understand the behavior of the matrix in a simpler form. It also allows for easier manipulation and analysis of the matrix.

5. How are eigenvectors and eigenvalues used in data analysis?

Eigenvectors and eigenvalues are commonly used in data analysis and machine learning. They are used to reduce the dimensionality of data and extract important features from large datasets. They can also be used to find patterns and relationships within the data, making it easier to understand and interpret.

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