Question on proof ##\Lambda^{\perp}(AU) = U^{-1} \Lambda^{\perp}(A)##

In summary: I hope this is not too simple thinking and therefore I am interested in your opinions.In summary, the author is asking for opinions on how to proceed with a proof. He poses the question of whether or not it is more efficient to rewrite the proof as opposed to using the restriction that a matrix have integer entries.
  • #1
Peter_Newman
155
11
Say we have as special lattice ## \Lambda^{\perp}(A) = \left\{z \in \mathbf{Z^m} : Az = 0 \in \mathbf{Z_q^n}\right\}##. We define ##U \in \mathbf{Z^{m \times m}}## as an invertible matrix then I want to proof the following fact:
$$ \Lambda^{\perp}(AU) = U^{-1} \Lambda^{\perp}(A) $$
My idea:
Let ##y \in \Lambda^{\perp}(A)## that is ##y \in Az = 0##, now ##U^{-1}y = (U^{-1}Az = 0) \in U^{-1}\Lambda^{\perp}(A)## and let ##y' \in \Lambda^{\perp}(AU)## that is ##y' \in AUz = 0##, this implies ##y \in y'## which shows one direction.

I hope that this is not too simple thinking and therefore I am interested in your opinions.
 
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  • #2
Your notation is confusing. You don't mean [itex]y \in Az = 0[/itex] etc.; you mean [itex]y \in \{ z \i n\mathbb{Z}^m: Az = 0 \}[/itex], but you can just write "Let y \in \Lambda^{\perp}(A). Then [itex]Ay = 0[/itex]."

The central point is that if [itex]Ay = 0[/itex] then we can write [itex]Ay = AUU^{-1}y[/itex] so that [itex]U^{-1} y \in \Lambda^{\perp}(AU)[/itex]; hence [tex]U^{-1}\Lambda^{\perp}(A) \subset \Lambda^{\perp}(AU).[/tex] But conersely, if [itex]AUy = 0[/itex] then [itex]Uy \in \Lambda^{\perp}(A)[/itex] so that [tex]
U\Lambda^{\perp}(AU) \subset \Lambda^{\perp}(A).[/tex] But if [itex]U[/itex] is invertible then [tex]U(B) = C \Leftrightarrow B = U^{-1}(C)[/tex] for any subsets [itex]B[/itex] and [itex]C[/itex] of [itex]\mathbb{Z}^m[/itex]. The result follows.

The requirement that a matrix have integer entries and have an inverse with integer entries is somewhat restrictive; the only ones which come to mind are [itex]\pm I[/itex] and matrices which permute the standard basis vectors.
 
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  • #3
Thanks for your great help @pasmith ! Yes my notation is a bit confusing and also kept myself from seeing the result directly.

In the second part, you could have done the following: ##y \in \Lambda^{\perp}(AU)## then ##AUy = 0##, then ##Uy \in \Lambda^{\perp}(A)## which implies ##y \in U^{-1}\Lambda^{\perp}(A)## , right? The "advantage" would be that then ##\Lambda^{\perp}(AU) = U^{-1} \Lambda^{\perp}(A)## is directly recognizable, but this is more or less a rewriting.
 

What is the meaning of ##\Lambda^{\perp}(AU) = U^{-1} \Lambda^{\perp}(A)##?

This equation represents the relationship between the orthogonal complements of two subspaces, denoted by ##\Lambda^{\perp}(AU)## and ##\Lambda^{\perp}(A)##. It states that the orthogonal complement of the image of the linear transformation AU is equal to the inverse of U applied to the orthogonal complement of the image of A.

What is the significance of this equation in linear algebra?

This equation is important in linear algebra because it helps us understand the relationship between subspaces and linear transformations. It also allows us to simplify calculations involving orthogonal complements.

Can this equation be applied to any linear transformation and subspaces?

Yes, this equation is a general result that applies to any linear transformation and subspaces in a vector space. However, it is most commonly used in the context of finite-dimensional vector spaces.

What are some real-world applications of this equation?

This equation has applications in fields such as computer graphics, signal processing, and physics. It is used to analyze and manipulate data in these fields, as well as in other areas of science and engineering.

Are there any other important properties or theorems related to this equation?

Yes, there are several other important properties and theorems related to this equation, such as the Rank-Nullity Theorem and the Orthogonal Decomposition Theorem. These results help us further understand the relationship between subspaces and linear transformations.

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