Distinct Eigenvalues: Can Zero be an Eigenvalue?

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Discussion Overview

The discussion revolves around whether zero can be considered a distinct eigenvalue in the context of linear transformations and eigenvectors. Participants explore the implications of zero being an eigenvalue on the linear independence of eigenvectors and the definitions involved in eigenvalue theory.

Discussion Character

  • Debate/contested
  • Mathematical reasoning
  • Conceptual clarification

Main Points Raised

  • Some participants question if zero can be a distinct eigenvalue, with one noting that it is no more special than other eigenvalues like 1 or 2.
  • There is a concern that if zero cannot be a distinct eigenvalue, it complicates a proof regarding the linear independence of eigenvectors corresponding to distinct eigenvalues.
  • One participant expresses confusion over the term "distinct" and clarifies that it refers to different eigenvalues, including zero if it is different from others.
  • Another participant argues that zero can be an eigenvector, emphasizing the definition of eigenvalues and eigenvectors.
  • There is a discussion about the definitions of eigenvectors and the role of the zero vector in the context of subspaces, with differing opinions on whether zero should be included as an eigenvector.

Areas of Agreement / Disagreement

Participants do not reach a consensus on whether zero can be considered a distinct eigenvalue. There are competing views on the definitions and implications of zero being an eigenvalue or eigenvector, leading to unresolved questions about its role in linear independence proofs.

Contextual Notes

Limitations in the discussion include varying definitions of eigenvalues and eigenvectors, particularly regarding the inclusion of zero, and the implications of these definitions on linear independence proofs. Some mathematical steps in the proofs are not fully resolved.

Juggler123
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Can zero be a distinct eigernvalue?
 
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Imagine you get to a characteristic equation

\lambda (\lambda-1)(\lambda -2)=0

during your calculations. Perfectly valid. Now what would your eigenvalues be? :wink:
 
Yes the eigenvalues are 0 1 and 2 but is 0 distinct?
 
Yes, it is no more special than having 1 or 2. All it means is that your matrix is singular. What we can't have though is a 0 eigenvector (for reasons that I can't recall right now).
 
This is what I thought, problem is that it messes with a proof I'm trying to complete.

I have to prove the following;

Let V be a finite dimensional vector space and T: V to V be a linear transformation. Let a1,...,ak be distinct eigenvalues of T and let v1,..,vk be corresponding eigenvectors. Prove that {v1,...,vk} is linearly independent.

If it was the case that 0 cannot be a distinct eigenvalue then I can complete the proof but since this isn't the case I'm a liitle stuck! Could anyone possible help me out here?
Thanks.
 
I don't understand how one number can be 'distinct', but anyway: 0 is perfectly allowed as eigenvalue, i.e. one of the a_i may or may not equal 0. Please write out explicitly what proof you had in mind and where you're stuck, then we can help you!
 
Well basically the proof (as far as I can see) is asking to show that the sum from i=1 to k of alpha(i)v(i) =0 implies that alpha(i)=0 for all i, alpha being an element of the field F.
Now taking x element of V, x = sum from i=1 to k of alpha(i)v(i)
then T(x) = sum from i=1 to k of alpha(i)T(v(i)) = sum from i=1 to k of alpha(i)a(i)v(i)
Now for this sum to equal zero then either aplha(i)=0 (as required) OR a(i)v(i)=0, if it was the case that a(i) could not be equal to zero then the prrof would be complete, but since there could be one case where a(i)=0 I am stuck and don't know how to move on from here.
Sorry I haven't Latexed anything, I don't know how!
 
Hmm, this doesn't look right.
Juggler123 said:
Well basically the proof (as far as I can see) is asking to show that \sum _{i=1}^{k}\alpha_iv_i =0 implies that \alpha_i=0 for all i, alpha_i being an element of the field F.
Yes, so far so good. However:
\sum _{i=1}^{k}\alpha_iTv_i =\sum _{i=1}^{k}\alpha_ia_iv_i

Now for this sum to equal zero then either aplha(i)=0 (as required) OR a(i)v(i)=0,
How so?! No, this is not a correct conclusion. You are basically saying if a+b=0 then a=0 and b=0.

A more fruitful approach: by contradiction, assume that {v1,...,vk} is dependent. Let j\leq k[/tex] be the <b>smallest</b> index such that v_j\in\mbox{span}(v_1,...v_{j-1}), i.e. j is minimal with the property of having a non-trivial linear combination<br /> \alpha_1v_1+...+\alpha_j v_j=0.<br /> Now apply T to this equation, and think some more.
 
"Distinct" numbers just means different numbers. If a and b are eigen values of operator T and a\ne b then they are "distinct" eigenvalues. If they happen to be 0 and 1, then, since they are different, they are "distinct". That's why Landau said "I don't understand how one number can be 'distinct'". The word "distinct" doesn't apply to a single number, it applies to sets of numbers- the numbers in the set are "distinct" if and only if they are all different.

phyzmatic, 0 can be an eigenvector. A number, \lambda is defined to be an eigenvalue of operator T if and only if there exist a non-zero vector v such that Tv= \lambda v but once we have that any vector, including 0, such that Tv= \lambda v is an eigenvector of T.

We need to have 0 an eigenvector in order to say "the set of all eigenvectors of T, corresponding to eigenvalue \lambda, is a subspace".
 
  • #10
HallsofIvy said:
phyzmatic, 0 can be an eigenvector. A number, \lambda is defined to be an eigenvalue of operator T if and only if there exist a non-zero vector v such that Tv= \lambda v but once we have that any vector, including 0, such that Tv= \lambda v is an eigenvector of T.

We need to have 0 an eigenvector in order to say "the set of all eigenvectors of T, corresponding to eigenvalue \lambda, is a subspace".
That's just a matter of definition. A lot of people exclude 0 from being an eigenvector. They then say "the set of all eigenvectors, together with the zero vector, is a subspace". Other authors, like Axler, do the same as you. It doesn't matter, as long as you be clear on your definition.
 

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