Proving Equality of Image and Eigenspace for Eigenvalue 1

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In summary, the given problem involves showing that the image of a linear transformation is equal to the eigenspace for the eigenvalue 1, given that the linear transformation is represented by a matrix A and A^2=I_n. It is also stated that the eigenvalues of A must be ±1. The goal is to show that im(L_{I_{n}+A})=E_{1}(A), where L_{I_{n}+A} represents the linear transformation and E_{1}(A) is the eigenspace for the eigenvalue 1. The solution involves showing that both sets are contained within each other. One direction is shown, but the other direction still needs to be proven.
  • #1
chrisb93
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Homework Statement


It's given or I've already shown in previous parts of the question:
[itex]A \in M_{nxn}(F)\\
A^{2}=I_{n}\\
F = \mathbb{Q}, \mathbb{R} or \mathbb{C}\\
ker(L_{I_{n}+A})=E_{-1}(A)[/itex]
Eigenvalues of A must be [itex]\pm1[/itex]

Show [itex]im(L_{I_{n}+A})=E_{1}(A)[/itex] where E is the eigenspace for the eigenvalue 1

(I also need to show that [itex]im(L_{I_{n}-A})=E_{-1}(A)[/itex] but I think that should be simple once I've done one of them)

Homework Equations


The Attempt at a Solution


I know that I need to show both sets are contained within the other set so,

Show [itex]im(L_{I_{n}+A}) \subseteq E_{1}(A)[/itex]
[itex]y=L_{I_{n}+A}(x)[/itex] Let y be a general element of the image
[itex]=x+Ax[/itex] By definition of the transformation
[itex]\Rightarrow A y = A x + A^{2} x[/itex] Multiply through by A
[itex]= A x + x [/itex] As A2 is the identity element
[itex]\Rightarrow A y = y \in E_{1}(A)[/itex] As [itex]E_{1}(A) := \{ x | A x = x \}[/itex]

I've no idea how to show [itex]E_{1}(A) \subseteq im(L_{I_{n}+A})[/itex]
 
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  • #2
You don't seem to have defined "[itex]L_{I_n+ A}[/itex]" in all that.
 
  • #3
HallsofIvy said:
You don't seem to have defined "[itex]L_{I_n+ A}[/itex]" in all that.

It's never specifically defined in the question but I believe the subscript is the matrix representing the map so [itex]L(x) = (I_{n} + A)x[/itex]
 
  • #4
Take an eigenvector with eigenvalue 1. So Ay=y.

You need to find x such that (A+I)x=y.

What if you take x=y??
 
  • #5
micromass said:
Take an eigenvector with eigenvalue 1. So Ay=y.

You need to find x such that (A+I)x=y.

What if you take x=y??

I don't see how that works, if x=y then Ax=x therefore (A+I)x=Ax+x=2x=y which contradicts itself.
 
Last edited:

1. How do you show that two sets are equal?

To show that two sets are equal, you need to demonstrate that they have the same elements. This can be done by listing out the elements of both sets and comparing them, or by using set operations such as union, intersection, and complement to show that both sets produce the same result.

2. What is the notation used to show that two sets are equal?

The notation used to show that two sets are equal is the equal sign (=) with a line on top, known as the "set equality symbol". This symbol indicates that both sets have the same elements and are therefore equal.

3. Can two sets be equal if they have the same elements but in a different order?

Yes, two sets can be equal even if they have the same elements in a different order. The order of elements does not affect the equality of sets, as long as they have the same elements.

4. What is the difference between showing sets to be equal and equivalent?

Showing sets to be equal means that both sets have exactly the same elements, while showing sets to be equivalent means that both sets have the same number of elements and can be mapped onto each other. For example, the sets {1, 2, 3} and {3, 2, 1} are equal, but the sets {1, 2, 3} and {2, 4, 6} are equivalent.

5. Are there any other methods for showing sets to be equal?

Yes, there are other methods for showing sets to be equal, such as using Venn diagrams or proof by contradiction. Venn diagrams can visually demonstrate the equality of sets by showing the overlap between them, while proof by contradiction involves assuming that the sets are not equal and then arriving at a contradiction.

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