Is There a Simpler Way to Construct a Linear Functional Given a Linear Operator?

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Discussion Overview

The discussion revolves around the construction of a linear functional given a linear operator on a finite-dimensional vector space. Participants explore methods to prove the existence of such a functional that satisfies specific properties related to the operator and a non-zero vector in the space.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant proposes defining a linear functional based on a chosen basis that includes a specific non-zero vector, but encounters complications when applying the linear operator.
  • Another participant suggests examining the implications of the operator modified by a scalar multiple of the identity, raising questions about invertibility and the dimensions of the kernel and image.
  • A later reply indicates that since the operator applied to the non-zero vector results in a zero vector, the dimension of the kernel is greater than zero, leading to the conclusion that the image must be of lower dimension than the vector space.
  • Another participant emphasizes the need to select a basis that spans the image of the operator while ensuring the functional does not vanish on at least one vector outside this image.

Areas of Agreement / Disagreement

Participants express differing strategies for constructing the functional and do not reach a consensus on the best approach. The discussion remains unresolved regarding the optimal choice of basis and the implications of the operator's properties.

Contextual Notes

Limitations include the dependence on the choice of basis and the unresolved nature of the implications of the operator's characteristics on the construction of the functional.

ihggin
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Let [tex]V[/tex] be a finite-dimensional vector space over the field [tex]F[/tex] and let [tex]T[/tex] be a linear operator on [tex]V[/tex]. Let [tex]c[/tex] be a scalar and suppose there is a non-zero vector [tex]\alpha[/tex] in [tex]V[/tex] such that [tex]t \alpha = c \alpha[/tex]. Prove that there is a non-zero linear functional [tex]f[/tex] on [tex]V[/tex] such that [tex]T^{t}f=cf[/tex], where [tex]T^{t}f=f\circ T[/tex] is the transpose.

I tried the following: let [tex]B[/tex] be a basis for [tex]V[/tex] that contains [tex]\alpha[/tex] (we can do this since [tex]\alpha \neq 0[/tex]). Then define [tex]f[/tex] such that [tex]f(\alpha)=1[/tex] and [tex]f(b)=0[/tex] for all the other basis vectors. Extend the definition to arbitrary vectors using linearity of [tex]f[/tex]. So if [tex]v=\sum c_i b_i[/tex] for scalars [tex]c_i[/tex] and basis vectors [tex]b_i[/tex] with [tex]b_1= \alpha[/tex], we have [tex]f(v)=c_1[/tex]. However, I then ran into the problem that when I take [tex]f(Tv)[/tex], [tex]T[/tex] can map other basis vectors into vectors with components in [tex]\alpha[/tex], which messes my strategy up.

Is there some smart choice of basis vectors that can prevent this from happening? Or is this just not a good way of doing the problem?
 
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I would suggest, contemplate these equations and questions:

[tex]f(Tv)=cf(v)[/tex]

[tex]f((T-cI)v)=0[/tex]

Can [tex]T-cI[/tex] be invertible? How big is the image [tex](T-cI)V[/tex]? Can it be the whole of V?

Can you find a nonzero functional vanishing on this image?
 
Thank you! So basically [tex](T-cI)\alpha=0[/tex] so the dimension of the kernel is greater than zero, which means the dimension of the image is less than that of [tex]V[/tex]. We can then take a vector [tex]x[/tex] that is not in the image and generate a basis from it. We then define [tex]f[/tex] so that it's zero on all the basis vectors except [tex]x[/tex].
 
You almost got it. But you need to choose a basis in such a way that the dim(Im) vectors span the image and set your functional to vanish on these and not to vanish on at least one basis vector outside.
 
Last edited:

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