Linear Algebra proof with Linear Transformations

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Homework Help Overview

The discussion revolves around a proof involving a real symmetric n × n matrix A and its relationship with subspaces in R^n, specifically focusing on the containment of A(V perp) within V perp given that A(V) is contained in V.

Discussion Character

  • Conceptual clarification, Assumption checking, Exploratory

Approaches and Questions Raised

  • Participants explore the definitions of V and its orthogonal complement V perp, questioning the implications of vectors belonging to these spaces. There is an attempt to clarify what it means for a vector to be in V perp and how that relates to the transformation by A.

Discussion Status

The discussion is ongoing with participants seeking definitions and clarifications about the properties of orthogonal complements and the implications of the linear transformation A. Some participants are attempting to connect the concepts of inner products and kernel spaces to the problem at hand.

Contextual Notes

There appears to be confusion regarding the definitions and properties of V and V perp, as well as how to relate these to the matrix A. Participants are encouraged to clarify foundational concepts before proceeding with the proof.

CDrappi
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Homework Statement



Suppose that A is a real symmetric n × n matrix. Show that if V is
a subspace of R^n and that A(V) is contained in V , then A(V perp) is contained in V perp.

Homework Equations



A = A_T (A is equal to its transpose)

The Attempt at a Solution



I have no idea where to start
 
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Take a vector v\in V^\bot (what does this mean??).
You'll need to show that Av\in V^\bot (what do you need to show for that?)
 
I still am not sure what to do. Any further helpings?
 
What does v\in V^\bot mean??
What does Av\in V^\bot mean??
Just give the definition...
 
micromass said:
What does v\in V^\bot mean??
What does Av\in V^\bot mean??
Just give the definition...

That a vector v is contained in V perp

That a vector Av is contained in V perp
 
Yes, of course. But what does it mean that v is contained in V^\perp. What property must hold?
 
If V = C(B), the column space of some matrix B, then Bv = 0
 
Hmm, how did you define V^\bot? I remember that it had to do with inner products...
 
micromass said:
Hmm, how did you define V^\bot? I remember that it had to do with inner products...

Wouldn't V^\bot just be the left nullspace of B?
 
  • #10
Yes, but if you're given a set V^\bot. How do you find the matrix B??
 
  • #11
micromass said:
Yes, but if you're given a set V^\bot. How do you find the matrix B??

I do not know. Help prease!
 
  • #12
Given a subspace V. How did your course define the subspace V^\bot??
 
  • #13
For a subspace V spanned by the column space of a matrix V, Ker(VT) returns V^\bot (orthogonal complement to V). If v is in V and vp is in the orthogonal complement, then vp is in Ker(VT). Avp should also be in Ker(VT). If it is, then the inner product of Avp and v should be 0.
 
Last edited:
  • #14
Oh. We defined it as whatever part of R^n that V isn't in
 
  • #15
Gear300 said:
For a subspace V spanned by the column space of a matrix V, the Ker(VT) returns V^\bot.

Can you write the last part of that out to make it a little clearer? I can't understand exactly what you mean.
 
  • #16
hmph. it seems you've edited it on me
 

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