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

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In summary, U⊥ is a subspace of Rn. It is the nullspace of the U transpose and the sum of the dimensions of the two matrices U and U⊥ is n.
  • #1
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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  • #2
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.
 
  • #3
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..
 
  • #4
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 [tex]\cap[/tex] 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 [tex]\cap[/tex] U⊥ = {0}. Thus u=v and u'=v'.
- Thus projection map [tex]\pi[/tex]: Rn -->> U defined by [tex]\pi[/tex](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)
 
  • #5
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 [tex]\cap[/tex] 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 [tex]\cap[/tex] U⊥ = {0}. Thus u=v and u'=v'.
- Thus projection map [tex]\pi[/tex]: Rn -->> U defined by [tex]\pi[/tex](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[tex]\cap[/tex] U⊥)
Rn=dim(U)+dim(U⊥)
since dim(U[tex]\cap[/tex] 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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1. What is subspace?

Subspace refers to a mathematical concept in linear algebra where a subset of a vector space is considered as its own vector space. This subset must satisfy three conditions: it contains the zero vector, it is closed under vector addition, and it is closed under scalar multiplication.

2. How is subspace different from a vector space?

A vector space is a mathematical structure that consists of a set of vectors and operations such as addition and scalar multiplication. A subspace is a subset of a vector space that also satisfies the conditions of a vector space. In other words, every subspace is a vector space, but not every vector space is a subspace.

3. What are some examples of subspaces?

Examples of subspaces include the x-y plane in three-dimensional space, the set of all solutions to a homogeneous linear system of equations, and the set of all polynomials of degree n or less.

4. How is subspace used in real-world applications?

Subspace is used in various fields of science, including physics, engineering, and computer science. It has applications in data analysis, signal processing, and image compression. In physics, subspace is used to represent the possible states of a system, while in engineering, it is used in control systems and optimization problems.

5. What are some common properties of subspaces?

Some common properties of subspaces include closure under addition and scalar multiplication, the existence of a zero vector, and the existence of an additive inverse for every vector in the subspace. Additionally, the dimension of a subspace cannot exceed the dimension of its parent vector space.

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