Mapping ( linear transformation)

Click For Summary

Discussion Overview

The discussion revolves around the properties of a linear transformation defined by an inner product in a vector space. Participants explore the mapping \( f(x) = \langle x, v \rangle v \), aiming to demonstrate its linearity, and to describe its range and kernel. Additionally, they examine the relationship between a subspace \( V_1 \) and its orthogonal complement within the context of vector spaces.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants propose that to show \( f \) is a linear transformation, one must demonstrate that \( T(ax + by) = aT(x) + bT(y) \) for scalars \( a, b \) and vectors \( x, y \) in \( V \).
  • There is a discussion about the properties of the inner product, including linearity and symmetry, and how they apply to proving the linearity of \( f \).
  • Participants express uncertainty regarding the interpretation of the transformation and the role of the transpose in the context of the problem.
  • Some participants suggest that the range of \( f \) consists of scalar multiples of the vector \( v \), while others seek clarification on how to describe the kernel.
  • There is a proposal that if \( x \) is in the kernel of \( T \), then \( \langle x, v \rangle = 0 \), indicating orthogonality.
  • Participants discuss the requirement to show that every vector in \( V \) can be uniquely decomposed into components in \( V_1 \) and its orthogonal complement.
  • One participant expresses frustration over the perceived lack of foundational understanding in linear algebra among some contributors, indicating a need for more basic comprehension.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the proofs and interpretations of the properties of the transformation \( f \). Multiple competing views and uncertainties remain regarding the application of inner product properties and the implications for the kernel and range of the transformation.

Contextual Notes

Limitations include potential misunderstandings of linear algebra concepts, the role of the inner product, and the definitions of terms used in the discussion. Some participants express confusion over the application of symmetry and linearity in the context of the transformation.

reha
Messages
7
Reaction score
0
If V is a vector space with an inner space <.,.>. V1 is an non empty subset of V. Vector x is contained in V is said to be orthogonal to v1 if <x,y>=0 for all y contained in V1.

1) if v is contained in V and define the mapping f(x)=<x,v>v. Show f is a linear transformation and describe its range and kernel.

2) if v1 is a subspace of V show that V1 and direct sum of V1 (orthogonal) = V.

I tried to prove these as they were stated in a website. But failed. PLease kindly assist me on this matter.

Thank.
 
Physics news on Phys.org
Be glad to help. Show us what you did and we'll make suggestions.
 
HallsofIvy said:
Be glad to help. Show us what you did and we'll make suggestions.

for 1)
f(x)=<x,v>v. Show f is a linear transformation.

since <x,v> = <v,x> (symmetry)

hence <x,v>v = <v,x>v = vT x v = x == f(x). Am i right? but i feel its absurd. (vT is v transpose)

range and kernel.
Range ( please help me out. I've no idea how to describe it. thanks)

Kernel. i was thinking, if x= 0, then f(0)=0. Is this right?

2) if v1 is a subspace of V show that V1 and direct sum of V1 (orthogonal) = V.

i tried to verify thinking that, since, V1 is a subspace of V, the direct sum of V1 and its orthogonal has to be in V since V is a vector space.

Please help me out. thanks.
 
HallsofIvy said:
Be glad to help. Show us what you did and we'll make suggestions.

for 1)
f(x)=<x,v>v. Show f is a linear transformation.

since <x,v> = <v,x> (symmetry)

hence <x,v>v = <v,x>v = vT x v = x == f(x). Am i right? but i feel its absurd. (vT is v transpose)

range and kernel.
Range ( please help me out. I've no idea how to describe it. thanks)

Kernel. i was thinking, if x= 0, then f(0)=0. Is this right?

2) if v1 is a subspace of V show that V1 and direct sum of V1 (orthogonal) = V.

i tried to verify thinking that, since, V1 is a subspace of V, the direct sum of V1 and its orthogonal has to be in V since V is a vector space.

Please help me out. thanks.
 
reha said:
for 1)
f(x)=<x,v>v. Show f is a linear transformation.

since <x,v> = <v,x> (symmetry)

hence <x,v>v = <v,x>v = vT x v = x == f(x). Am i right? but i feel its absurd. (vT is v transpose)
I agree with resppect to the absurdity:smile: How comes the transppose about. There is no mention of a basis in the statement of the problem, so what is this transpose supposed to be? Anyway, you don't need it. And you didn't say what f is.
What you are considering is the transformation [itex]T:\to V[/itex] defined by [itex]T=<x,v>v[/itex]
To show that this transformation is linear, you must show that [itex]T(ax+by)=aTx+bTy[/itex] where a,b are scalars and x,y elements of V. You said < , > is an inner product. What properties does it thus have. Do these properties help you prove linearity of T?
reha said:
range and kernel.
Range ( please help me out. I've no idea how to describe it. thanks)
Whatever you have T acting on, the result is a scalar multiple of v, is it not? Namely, <x,v>v, where <x,v> is said scalar.
reha said:
Kernel. i was thinking, if x= 0, then f(0)=0. Is this right?
This is right und means that the zero vector is in the kernel of T. (This is true for any linear transformation). You want to see if there are any other vectors in the kernel. If x is in ker T then <x,v>v=0, and since v is not the zero vector, this means <x,v>=0. In your first post, you stated something about orthogonality, maybe this helps here...
reha said:
2) if v1 is a subspace of V show that V1 and direct sum of V1 (orthogonal) = V.

i tried to verify thinking that, since, V1 is a subspace of V, the direct sum of V1 and its orthogonal has to be in V since V is a vector space.
To show that [itex]V=V_1\oplus V_1^\bot[/itex] you have to show that every vector x in V can be uniquely decomposed as x=v+u where v is in V1 and u is in the orthogonal complement of V1. Clearly, x=<x,v>v+(x-<x,v>v) is such a decomposition. To prove uniqueness, assume that x=v'+u' is another such decomposition. It follows that v-v'=u'-u; the vector on the left lies in V1, the vector on the right side in its complement. Since the only vector two orthogonal subspaces have in common, is the zero vector (prove it!), we see that v=v' and u=u', i.e. the decomposition is unique.
reha said:
Please help me out. thanks.
 
Last edited:
Pere Callahan said:
I agree with resppect to the absurdity:smile: How comes the transppose about. There is no mention of a basis in the statement of the problem, so what is this transpose supposed to be? Anyway, you don't need it. And you didn't say what f is.
What you are considering is the transformation [itex]T:\to V[/itex] defined by [itex]T=<x,v>v[/itex]
To show that this transformation is linear, you must show that [itex]T(ax+by)=aTx+bTy[/itex] where a,b are scalars and x,y elements of V. You said < , > is an inner product. What properties does it thus have. Do these properties help you prove linearity of T?



Kernel. i was thinking, if x= 0, then f(0)=0. Is this right?

2) if v1 is a subspace of V show that V1 and direct sum of V1 (orthogonal) = V.

i tried to verify thinking that, since, V1 is a subspace of V, the direct sum of V1 and its orthogonal has to be in V since V is a vector space.

Please help me out. thanks.
[/QUOTE]

f is a mapping. where mapping f(x)= <x,v>v

Inner product properties: linearity, symmetry (which i had used) and definiteness. should i need to use all of them?

Thanks.
 
reha said:
f is a mapping. where mapping f(x)= <x,v>v

Inner product properties: linearity, symmetry (which i had used) and definiteness. should i need to use all of them?

Thanks.
Ok, I called this f T, but this doesn't make any differnece, of course. Yes, you should use in particular the linearity of the inner product to prove linearity of f (or T).
 
Pere Callahan said:
Ok, I called this f T, but this doesn't make any differnece, of course. Yes, you should use in particular the linearity of the inner product to prove linearity of f (or T).

Thanks. Can you please tell me if the following is correct:

f(x)= <x,v>v
say a is scalar. and s be a vector. hence

f(x)= <(ax+s), v>v
= axv^2 + sv^2

Thanks.

Please correct my mistake. Btw, why can't i use the symmetry property which i had used earlier?
 
Whatever you have T acting on, the result is a scalar multiple of v, is it not? Namely, <x,v>v, where <x,v> is said scalar.
Please explain again. i don't get why it is scalar? i thought x and y are vectors.

This is right und means that the zero vector is in the kernel of T. (This is true for any linear transformation). You want to see if there are any other vectors in the kernel. If x is in ker T then <x,v>v=0, and since v is not the zero vector, this means <x,v>=0. In your first post, you stated something about orthogonality, maybe this helps here...

Orthogonality. Can i say that, if <x,v>=0,(since v is contained in V) hence there is a zero vector in f defines ker T.

Thanks.
 
  • #10
reha said:
f(x)= <x,v>v
say a is scalar. and s be a vector. hence

f(x)= <(ax+s), v>v
= axv^2 + sv^2

I'm sorry, this shows that your understanding of the terms you are using and objects your are working with is insufficient for me to able to help you any further on this question.
In a forum, I (and presumably many others) can try to resolve specific problems or questions, but I cannot make up for a whole course on linear algebra nor for the basics of mathematical insight and comprehension which is acquired only with time and practice, not by being told in a step-by-step fashion how to solve this or that specific exercise.
Good luck.
 

Similar threads

  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 3 ·
Replies
3
Views
4K
  • · Replies 9 ·
Replies
9
Views
3K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
4K
  • · Replies 23 ·
Replies
23
Views
2K
  • · Replies 52 ·
2
Replies
52
Views
4K