Vector Spaces and Correspondence

In summary, We are given a vector space V and a subspace U that is not equal to V. We are also given a linear operator T that satisfies T(u) = u for all u in U and T(v+U) = v+U for all v in V. We set S=T-I_V and need to prove that S^2 = 0_V->V. To do this, we use the given conditions and show that for an arbitrary vector v in V, S^2(v+u) = 0 for all u in U. This is done by breaking down S^2(v+u) into its components and showing that they cancel out, resulting in a zero vector.
  • #1
Ninty64
46
0

Homework Statement


This question came out of a section on Correspondence and Isomorphism Theorems

Let [itex]V[/itex] be a vector space and [itex]U \neq V, \left\{ \vec{0} \right\} [/itex] be a subspace of [itex]V[/itex]. Assume [itex]T \in L(V,V)[/itex] satisfies the following:
a) [itex]T(\vec{u} ) = \vec{u}[/itex] for all [itex]\vec{u} \in U[/itex]
b) [itex]T(\vec{v} + U) = \vec{v} + U[/itex] for all [itex]\vec{v} \in V[/itex]
Set [itex]S=T-I_{V}[/itex]. Prove that [itex]S^{2}=\vec{0}_{V \rightarrow V}[/itex]

Homework Equations


[itex]I_{V}[/itex] is the identity map
[itex]L(V,V)[/itex] is the map of all linear operators on V

The Attempt at a Solution


I have trouble understanding the question.
Since [itex]T \in L(V,V)[/itex] then how is [itex]T(\vec{v} + U) = \vec{v} + U[/itex] for all [itex]\vec{v} \in V[/itex]?
Wouldn't that mean [itex]T:V/U \rightarrow V/U[/itex]?
I don't understand, what is [itex]T(\vec{v})[/itex] equal to?
Does [itex]T(\vec{v})=\vec{v}[/itex] or [itex]T(\vec{v}) = [\vec{v}]_W[/itex] or something else?

I'm sorry if this is a silly question.
 
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  • #2
With [itex]T(v+U)=v+U[/itex], they mean that the set v+U is mapped to the set v+U.
So

[tex]T(\{v+u~\vert~u\in U\})=\{v+u~\vert~u\in U\}[/tex]

Or in another way: for each [itex]v\in V[/itex] and [itex]u\in U[/itex], there exists [itex]u^\prime\in U[/itex] such that [itex]T(v+u)=v+u^\prime[/itex].
 
  • #3
micromass said:
With [itex]T(v+U)=v+U[/itex], they mean that the set v+U is mapped to the set v+U.
This is where I confuse myself. T is a linear transformation from V to V. If it maps the set v+U to the set v+U, then wouldn't that be mapping cosets of V mod U to cosets of V mod U instead of mapping V to V?
 
  • #4
micromass said:
Or in another way: for each [itex]v\in V[/itex] and [itex]u\in U[/itex], there exists [itex]u^\prime\in U[/itex] such that [itex]T(v+u)=v+u^\prime[/itex].

I think I get it now. The function maps the coset of V mod U to the same coset of V mod U by mapping each individual element to another element in that coset.

I'm sorry if it seemed I brushed over your post and didn't completely read it. I did. I just didn't understand it. I'm having trouble with my Linear Algebra 2 class, and I'm glad that you responded. Thank you a lot!

So then I get
Let [itex]\vec{v} \in V[/itex] be arbitrary
[itex]S^2(\vec{v} + \vec{u}), \vec{u} \in U[/itex]
[itex]=S(T(\vec{v} + \vec{u}) - I(\vec{v} + \vec{u}))[/itex]
[itex]=S(\vec{v} + \vec{u}` - ( \vec{v} + \vec{u}))[/itex] where [itex]\vec{u}` \in U[/itex]
[itex]=S(\vec{u}` - \vec{u})[/itex]
[itex]=S(\vec{y})[/itex] where [itex]\vec{y} = \vec{u}`- \vec{u} \in U[/itex]
[itex]=T(\vec{y}) - I(\vec{y})[/itex]
[itex]=\vec{y} - \vec{y}=\vec{0}[/itex]
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations that can be performed on those vectors. The vectors must satisfy certain properties, such as closure under addition and scalar multiplication, to be considered a vector space.

2. What is the dimension of a vector space?

The dimension of a vector space is the number of independent vectors that can be used to span the entire space. It is also equal to the number of coordinates needed to specify any vector in the space.

3. How do you determine if two vector spaces are isomorphic?

Two vector spaces are isomorphic if there exists a bijective linear transformation (also known as an isomorphism) between them. This means that the two spaces have the same dimension and the same algebraic structure.

4. What is the difference between a vector space and a subspace?

A vector space is a set of vectors that satisfies certain properties, while a subspace is a subset of a vector space that also satisfies those properties. In other words, a subspace is a smaller vector space that is contained within a larger vector space.

5. What is the correspondence between vectors and matrices?

Vectors can be represented as matrices by using a one-dimensional array or column vector. This allows for operations to be performed on vectors using matrix operations, such as addition, multiplication, and inversion.

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