Understanding Eigenspaces: Solving Exam Questions

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In summary, the conversation discusses the concept of eigenspace and whether the vectors 1,-1 and -1,1 are the same in this context. The expert explains that the order of the eigenvalues does not matter and clarifies that the eigenspace is a vector subspace. They also mention that the two vectors in question lie along the same line but point in opposite directions.
  • #1
klingenberg
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I know this might be trivial, but when practicing for exam, I usually write the "inverse" values of the cheat sheet, and want to make sure I'm not making a mistake.

Is the eigenspace 1,-1 the same as -1,1?
 
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  • #2
I think your difficulty is your "notation" which is so terse as to be confusing. 'The eigenspace 1, -1' makes no sense to me. If you mean "the subspace spanned by eigenvectors of linear operator A corresponding to eigenvalues 1 and -1" then it should be clear that the order in which you mention the eigenvalues is irrelevant.

(I started to write "the subspace of all eigenvectors of linear operator A corresponding to eigenvalues 1 and -1" but you understand that that set is NOT a subspace, right?)
 
  • #3
To put it a bit differently, an eigenspace is a (vector ) subspace , and {1,-1} is not, at least not in any way I' familiar with. Did you mean the eigenspaces associated to each of these eigenvectors?
 
  • #4
If your notation 1, -1 is intended to mean the vector <1, -1> in R2, then yes, the space spanned by the eigenvector <1, -1> is the same as that spanned by the vector <-1, 1>. Both vectors lie along the line y = -x but point in opposite directions.
 
  • #5
Mark44 said:
If your notation 1, -1 is intended to mean the vector <1, -1> in R2, then yes, the space spanned by the eigenvector <1, -1> is the same as that spanned by the vector <-1, 1>. Both vectors lie along the line y = -x but point in opposite directions.

Perfect, thanks. Sorry about the confusing notation.
 

FAQ: Understanding Eigenspaces: Solving Exam Questions

What is an eigenspace?

An eigenspace is a subspace of a vector space that consists of all the eigenvectors corresponding to a specific eigenvalue of a linear transformation.

How is an eigenspace different from an eigenvector?

An eigenvector is a vector that remains unchanged in direction after a linear transformation, while an eigenspace is a collection of all the eigenvectors for a specific eigenvalue of a linear transformation.

What is the significance of eigenspaces in linear algebra?

Eigenspaces are important in linear algebra as they provide a way to break down a linear transformation into simpler components and understand its behavior on different subspaces.

How can eigenspaces be used in data analysis and machine learning?

Eigenspaces are commonly used in data analysis and machine learning to reduce the dimensionality of a dataset and extract the most important features. This is done by finding the eigenvectors and corresponding eigenvalues of a data matrix.

Can eigenspaces exist for non-square matrices?

No, eigenspaces only exist for square matrices. However, for non-square matrices, we can find the left and right eigenvectors, which can be used to construct a generalized eigenspace.

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