Proving No Eigenvalues Exist for an Operator on a Continuous Function Space

In summary, the conversation discusses the existence of eigenvalues for a given operator A. It is shown that no eigenvalues exist, including the case for λ=0, by considering the equation (λ-x)f(x)=0 and the continuity of the function f(x). It is suggested that part (b) of the homework assignment, which asked to show 0 is not an eigenvalue, may not be necessary as this logic holds for all λ. It is also noted that eigenvalue λ=0 can only occur when A is non-injective, but this is not the case for the given map.
  • #1
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Homework Statement
Show no eigenvalues exist for the operator ##Af(x) = xf(x)## where ##A:C[0,1]\to C[0,1]##.
Relevant Equations
Nothing comes to mind.
Eigenvalues ##\lambda## for some operator ##A## satisfy ##A f(x) = \lambda f(x)##. Then

$$
Af(x) = \lambda f(x) \implies\\
xf(x) = \lambda f(x)\implies\\
(\lambda-x)f(x) = 0.$$

How do I then show that no eigenvalues exist? Seems obvious one doesn't exists since ##\lambda-x \neq 0## for all ##x\in [0,1]##.
 
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  • #2
Well the argument is subtle.

Since ##x## ranges over every point in ##[0,1]## and the supposed eigenvalue is some constant you cannot have ##\lambda=x##, since the function ##x## is not constant.
And obviously ##f(x)\ne 0 ##, otherwise it wouldn't be an eigenvalues' problem.
 
  • #3
You are in fact almost there. The condition
##(\lambda-x)f(x) =0## implies that we have ##f(x) = 0## for all ##x\neq \lambda##. By continuity however ##f=0##. Thus, no eigenvector exists, as any possible eigenvalue leads to the trivial solution.
 
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  • #4
Math_QED said:
You are in fact almost there. The condition
##(\lambda-x)f(x) =0## implies that we have ##f(x) = 0## for all ##x\neq \lambda##. By continuity however ##f=0##. Thus, no eigenvector exists, as any possible eigenvalue leads to the trivial solution.
This just seems so simple. See, the HW was assigned in three parts, where part b) asked to show 0 is not an eigenvalue and part c) asked to prove no eigenvalues exist. But if this is part c), then why bother doing b)? Have I missed anything about ##\lambda = 0##? It seems this logic holds for all ##\lambda##.
 
  • #5
joshmccraney said:
This just seems so simple. See, the HW was assigned in three parts, where part b) asked to show 0 is not an eigenvalue and part c) asked to prove no eigenvalues exist. But if this is part c), then why bother doing b)? Have I missed anything about ##\lambda = 0##? It seems this logic holds for all ##\lambda##.

It also works for ##\lambda = 0##. Then your equation leads to ##xf(x) = 0 \forall x## and for ##x \neq 0## we then have ##f(x) = 0##. By continuity again also ##f(0) = 0##, so ##f=0## everywhere.

So, to answer your question concretely, I don't think doing part (b) separately is necessary.

Note that eigenvalue ##\lambda = 0## can only happen when ##A## is non-injective. This map is injective however (continuity is key again here). Maybe that's what (b) wants you to write?
 
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1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe the behavior of a linear transformation or operator. Eigenvalues represent the scalar values that are multiplied by the eigenvectors when the linear transformation is applied.

2. How are eigenvalues and eigenvectors calculated?

The eigenvalues and eigenvectors of an operator can be calculated by solving the characteristic equation, which is obtained by setting the determinant of the operator's matrix representation equal to zero. The resulting eigenvalues and eigenvectors can then be used to diagonalize the operator.

3. What is the significance of eigenvalues for an operator?

Eigenvalues are important because they provide information about the behavior of an operator. They can help determine whether the operator is invertible, the dimension of its null space, and the stability of its solutions.

4. Can an operator have complex eigenvalues?

Yes, an operator can have complex eigenvalues. In fact, complex eigenvalues are often encountered when dealing with non-symmetric or non-Hermitian operators. In these cases, the corresponding eigenvectors will also be complex.

5. How are eigenvalues used in practical applications?

Eigenvalues have a wide range of applications in various fields such as physics, engineering, and computer science. They are used in solving differential equations, image and signal processing, and data analysis. In quantum mechanics, eigenvalues are used to describe the energy levels of a physical system.

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