Eigenspace of A for Eigenvalue 3: Basis Find

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In summary, the question is asking for a basis for the eigenspace corresponding to the eigenvalue 3 of the matrix A, where A is given as A = \left[\begin{array}{ccccc} 2&2&-1 \\ 1&3&-1 \\ 0&2&1 \end{array}\right]. The eigenvector corresponding to the eigenvalue 3 is {1, 1, 1}, and this can be written as a basis for the eigenspace. It is also important to note that any constant multiple of this eigenvector is also an eigenvector, making it a suitable basis for the eigenspace.
  • #1
roam
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Homework Statement



[tex]A = \left[\begin{array}{ccccc} 2&2&-1 \\ 1&3&-1 \\ 0&2&1 \end{array}\right][/tex]

Given that [tex]\lambda = 3[/tex] is an eigenvalue of A, find a basis for the eigenspace corresponding to the eigenvalue 3.


Homework Equations





The Attempt at a Solution



Is this question asking for the corresponding eigenvector to the eigenvalue [tex]\lambda = 3[/tex] is??

I already found that the corresponding eigenvector to the eigenvalue 3 is: {1, 1, 1}

So, what do I need to write as a basis for the eigenspace corresponding to [tex]\lambda = 3[/tex] ?
 
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  • #2
Remember, an eigenvector is defined by the property [tex]Ax = \lambda x[/tex].

However, for example, it is also true that [tex]A(3x) = \lambda (3x)[/tex]. Thus every constant multiple of your eigenvector is also an eigenvector of A. Your basis is the eigenvector.
 
  • #3
roam said:

Homework Statement



[tex]A = \left[\begin{array}{ccccc} 2&2&-1 \\ 1&3&-1 \\ 0&2&1 \end{array}\right][/tex]

Given that [tex]\lambda = 3[/tex] is an eigenvalue of A, find a basis for the eigenspace corresponding to the eigenvalue 3.


Homework Equations





The Attempt at a Solution



Is this question asking for the corresponding eigenvector to the eigenvalue [tex]\lambda = 3[/tex] is??

I already found that the corresponding eigenvector to the eigenvalue 3 is: {1, 1, 1}

So, what do I need to write as a basis for the eigenspace corresponding to [tex]\lambda = 3[/tex] ?
I presume that what you found was that the equations reduce to x= z and y= z. That is, that any eigenvector can be written as, say, {x, x, x}= x{1, 1, 1}. Penguino said "Your basis is the eigenvector." I would say, rather, that the basis is the singleton set containing that vector" but I doubt the distinction is important.
 
  • #4
HallsofIvy said:
I presume that what you found was that the equations reduce to x= z and y= z. That is, that any eigenvector can be written as, say, {x, x, x}= x{1, 1, 1}. Penguino said "Your basis is the eigenvector." I would say, rather, that the basis is the singleton set containing that vector" but I doubt the distinction is important.


Yes, that's right. Thanks very much :)
 

1. What is an eigenspace?

An eigenspace is a vector space associated with a specific eigenvalue of a matrix. It is composed of all the eigenvectors corresponding to that eigenvalue.

2. How is an eigenspace related to an eigenvalue?

An eigenspace is associated with a specific eigenvalue of a matrix. It is made up of all the eigenvectors that, when multiplied by the matrix, result in a scalar multiple of the original vector (the eigenvalue).

3. What is the significance of finding the eigenspace of a matrix for a specific eigenvalue?

Finding the eigenspace of a matrix for a specific eigenvalue allows us to understand the behavior and properties of the matrix. It can also be useful in solving systems of linear equations and understanding transformations in linear algebra.

4. How do you find the basis for an eigenspace of a matrix for a specific eigenvalue?

To find the basis for an eigenspace of a matrix for a specific eigenvalue, you first need to find all the eigenvectors corresponding to that eigenvalue. Then, you can use these eigenvectors to form a basis for the eigenspace. This can be done by finding a linearly independent subset of the eigenvectors.

5. Can there be more than one basis for an eigenspace of a matrix for a specific eigenvalue?

Yes, there can be multiple bases for an eigenspace of a matrix for a specific eigenvalue. This is because there can be multiple linearly independent subsets of eigenvectors that can form a basis for the eigenspace.

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