Trivial Eigenspace: Definition & Meaning

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In summary, the conversation discusses the concept of a "trivial eigenspace" in the context of linear transformations in a vector space. It is defined as the 0 vector, but not considered an eigenvector. The conversation then moves on to the topic of commutative rings and how it relates to diagonalizable linear transformations. The significance of the minimal and characteristic polynomials and their relationship is brought up as a potential way to approach the problem.
  • #1
Treadstone 71
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What's a "trivial eigenspace"?
 
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  • #2
Put it in context. Probably something to do with 0, and probably 0 as a vector.
 
  • #3
"Let V be a vector space of dimension n over a field F, and let T:V->V be linear. Let R={linear transformations V->V that commute with T}.

Suppose T is diagonalisable, show that T is a commutative ring <=> all non-trivial eigenspaces of T are one-dimensional."

I just need to know what "non-trivial" means in this case. We defined the 0 vector to NOT be an eigenvector.
 
  • #4
0 is not en eigenVECTOR, agreed, but as a subvector space 0 is an eigenSPACE, the trivial eigenspace.
 
  • #5
How do I show that R is commutative <=> the minimal and characteristic polynomials of T are the same? (T might not be diagonalizable)
 
  • #6
does that mean you've done the first question?

There is no harm in passing to the algebraic closure of your field if it helps you to think about it. Put everythin in jordan form, what can you say about the commuting matrices? just to give you some idea of how to use the difference between the minimal and characeristic poly.
 
  • #7
Yes, I did the first question. I've got an idea about the second one that doesn't involve Jordan forms, but I'm still working on it.
 

What is a trivial eigenspace?

A trivial eigenspace is a special type of eigenspace that contains only the zero vector. This means that when a linear transformation is applied to the zero vector, the resulting vector is still the zero vector.

How is a trivial eigenspace different from a regular eigenspace?

Unlike a regular eigenspace, which contains at least one non-zero vector, a trivial eigenspace only contains the zero vector. This means that the eigenvalue associated with the trivial eigenspace is always 0.

What does the trivial eigenspace tell us about a linear transformation?

The existence of a trivial eigenspace for a linear transformation means that the transformation does not have any distinct eigenvectors. This can provide information about the nature of the transformation and its effect on vectors.

Can a linear transformation have more than one trivial eigenspace?

No, a linear transformation can only have one trivial eigenspace, which is the eigenspace associated with the eigenvalue of 0.

How are trivial eigenspaces used in linear algebra?

Trivial eigenspaces can be used to determine the eigenvalues and eigenvectors of a linear transformation. They can also provide information about the invertibility and diagonalizability of a matrix.

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