Confusion about eigenvalues of an operator

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Discussion Overview

The discussion revolves around the eigenvalues of an operator ##T## in a complex vector space of dimension ##n##. Participants explore the implications of forming a list of vectors generated by applying the operator to a vector ##v## multiple times, leading to a polynomial equation. The conversation examines the conditions under which the number of eigenvalues can be determined and the potential contradictions that arise from the assumptions made.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant asserts that the linear dependence of the vectors ##(v,Tv,T^2v,\ldots,T^mv)## implies that the operator ##T## has ##m## eigenvalues, which seems contradictory given the dimension of the vector space.
  • Another participant corrects the first by stating that the equation should involve the identity operator, suggesting that the conclusion about the number of eigenvalues is flawed.
  • Some participants question the necessity of setting ##m=n+1##, arguing that ##m## can be any value greater than ##n##, indicating a broader scope for the discussion.
  • It is noted that the eigenvalues of ##T## will always be a proper subset of the proposed values, implying that at least one of the chosen values for ##\mu_i## can be arbitrary.
  • One participant challenges the interpretation of the polynomial equation, stating that it does not necessarily imply that ##(T-\mu_i)v=0## for all ##i##.

Areas of Agreement / Disagreement

Participants express differing views on the implications of the polynomial equation regarding the number of eigenvalues. There is no consensus on the interpretation of the relationship between ##m## and the eigenvalues of ##T##, and the discussion remains unresolved.

Contextual Notes

Participants have not fully resolved the implications of the polynomial equation or the conditions under which the number of eigenvalues can be determined. The discussion highlights the dependence on definitions and assumptions regarding the operator and the vector space.

maNoFchangE
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Suppose ##V## is a complex vector space of dimension ##n## and ##T## an operator in it. Furthermore, suppose ##v\in V##. Then I form a list of vectors in ##V##, ##(v,Tv,T^2v,\ldots,T^mv)## where ##m>n##. Due to the last inequality, the vectors in that list must be linearly dependent. This implies that the equation
$$
0=a_0v+a_1Tv+a_2T^2v+\ldots+a_mT^mv
$$
are satisfied by some nonzero coefficients. For a particular case, assume that the above equation is satisfied by some choice of coefficients where all of them are nonzero.
Now since the equation above forms a polynomial, I can write the factorized form
$$
0=A(T-\mu_1)\ldots(T-\mu_m)v
$$
The last equation suggests that ##T## has ##m## eigenvalues. But this contradicts the fact that ##T## is an operator in a vector space of dimension ##n<m##. Where is my mistake?
 
Last edited:
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##T## is an operator whereas ##\mu_i## is a scalar. Instead you should write ##T-\mu_iI_n##.

Suppose ##m=n+1##, and take ##\mu_1, \ldots, \mu_n## to be the eigenvalues of ##T##. Then clearly the final equation holds for an arbitrary choice of ##\mu_{n+1}##. So your mistake is in concluding that ##T## must have more than ##n## eigenvalues, which is a non sequitur.
 
suremarc said:
Suppose ##m=n+1##, and take ##\mu_1, \ldots, \mu_n## to be the eigenvalues of ##T##. Then clearly the final equation holds for an arbitrary choice of ##\mu_{n+1}##. So your mistake is in concluding that ##T## must have more than ##n## eigenvalues, which is a non sequitur.
Why does it have to be ##m=n+1##? I can take ##m## any value I want such that it is bigger than ##n##.
 
maNoFchangE said:
Why does it have to be ##m=n+1##? I can take ##m## any value I want such that it is bigger than ##n##.
Indeed, you can--I was simply providing a counterexample to get you started.
The eigenvalues of ##T## will always be contained in some proper subset of ##\{\mu_1,\ldots,\mu_m\}##, so at least 1 of the choices for ##\mu_i## will be arbitrary.
 
maNoFchangE said:
$$
0=A(T-\mu_1)\ldots(T-\mu_m)v
$$
The last equation suggests that ##T## has ##m## eigenvalues.
No it doesn't. From the equation above it does not follow that
$$(T-\mu_i)v=0$$
 

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