Proving the Properties of Subspace U⊥ in Rn | Help with Subspace Concepts

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The discussion focuses on proving the properties of the orthogonal complement of a subspace \( U \) in \( \mathbb{R}^n \), denoted as \( U^\perp \). It is established that \( U^\perp \) is indeed a subspace of \( \mathbb{R}^n \) and that the dimensions satisfy the equation \( \text{dim}(U) + \text{dim}(U^\perp) = n \). Key arguments include the necessity for \( U^\perp \) to pass through the origin and the use of the inner product to demonstrate closure under addition and scalar multiplication. The discussion also emphasizes the uniqueness of vector representation in \( \mathbb{R}^n \) and the implications of the Isomorphism Theorem.

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  • Understanding of vector spaces and subspaces in linear algebra
  • Familiarity with orthogonal complements and their properties
  • Knowledge of inner product spaces and linearity
  • Concept of dimension in vector spaces
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  • Study the properties of orthogonal complements in linear algebra
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icystrike
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1. Let U be a subspace of Rn and let
U⊥ = {w ∈ Rn : w is orthogonal to U} .
Prove that
(i) U⊥ is a subspace of Rn,
(ii) dimU + dimU⊥ = n.


Attempt.


i)
U. ( U⊥)T=0
If U⊥ does not passes the origin , the above equation cannot be satisfied.
Therefore U⊥ passes the origin.
U.( U⊥+ U⊥)T=U. ( U⊥)T+U.( U⊥)T=0+0=0
U.(k U⊥)T=k[U. (U⊥)T]=k.0=0

Therefore U⊥ is a subspace in Rn

ii) let U have rank r.
U⊥ is the nullspace of the U transpose.
and if U is a matrix of mxn , UT is nxm.
Therefore , the sum of dim of the two matrices is exactly n.
 
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I don't understand your notation. You use "T" which I guess is "transpose" and talk about U being a matrix. U is given as a subspace, not a matrix.
 
HallsofIvy said:
I don't understand your notation. You use "T" which I guess is "transpose" and talk about U being a matrix. U is given as a subspace, not a matrix.

yup you are right.. it is transpose and subspace..
 
icystrike said:
1. Let U be a subspace of Rn and let
U⊥ = {w ∈ Rn : w is orthogonal to U} .
Prove that
(i) U⊥ is a subspace of Rn,
(ii) dimU + dimU⊥ = n.


Attempt.


i)
U. ( U⊥)T=0
If U⊥ does not passes the origin , the above equation cannot be satisfied.
Therefore U⊥ passes the origin.
U.( U⊥+ U⊥)T=U. ( U⊥)T+U.( U⊥)T=0+0=0
U.(k U⊥)T=k[U. (U⊥)T]=k.0=0

Therefore U⊥ is a subspace in Rn

ii) let U have rank r.
U⊥ is the nullspace of the U transpose.
and if U is a matrix of mxn , UT is nxm.
Therefore , the sum of dim of the two matrices is exactly n.


Let u,v be in U⊥. Let k be a constant in R.
Now <u,w>=0 for all w in U and <v,w>=0 for all w in U.
Thus by linearity of inner product we have <u+kv,w>=0 for all w in U. Thus, u+kv is also in U⊥ for all u,v,k. Thus U⊥ is a subspace.

For the second part, DIY but here are some hints:
- We know that U \cap U⊥ = {0}. Easy to check (if a vector is in U and perpendicular to U then it has to be the zero vector).
- Take x in Rn. Prove that x = u+u' for u in U and u' in U⊥.
- This is unique representation. If x = u+u' = v+v' with u,v in U and u',v' in U⊥ then 0 = (u-v)+(u'-v') implies u-v = v'-u' implies u-v = v'-u' = 0 since U \cap U⊥ = {0}. Thus u=v and u'=v'.
- Thus projection map \pi: Rn -->> U defined by \pi(x)=u where x = u+u' with u in U and u' in U⊥.
- Use Isomorphism theorem corollary (that dim(range)+dim(kernel)=dim(Rn)=n)
 
adityab88 said:
Let u,v be in U⊥. Let k be a constant in R.
Now <u,w>=0 for all w in U and <v,w>=0 for all w in U.
Thus by linearity of inner product we have <u+kv,w>=0 for all w in U. Thus, u+kv is also in U⊥ for all u,v,k. Thus U⊥ is a subspace.

For the second part, DIY but here are some hints:
- We know that U \cap U⊥ = {0}. Easy to check (if a vector is in U and perpendicular to U then it has to be the zero vector).
- Take x in Rn. Prove that x = u+u' for u in U and u' in U⊥.
- This is unique representation. If x = u+u' = v+v' with u,v in U and u',v' in U⊥ then 0 = (u-v)+(u'-v') implies u-v = v'-u' implies u-v = v'-u' = 0 since U \cap U⊥ = {0}. Thus u=v and u'=v'.
- Thus projection map \pi: Rn -->> U defined by \pi(x)=u where x = u+u' with u in U and u' in U⊥.
- Use Isomorphism theorem corollary (that dim(range)+dim(kernel)=dim(Rn)=n)


Hmm.. i think for part 2 i can explain by stating that dim(U+U⊥)=dim(U)+dim(U⊥)+dim(U\cap U⊥)
Rn=dim(U)+dim(U⊥)
since dim(U\cap U⊥) ought to be atleast {0} to satisfy the condition of subspace , and all the more they are orthogonal to one another , the can't be parallel , thus they have to be {0} itself.
Hence proving part 2.
 
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