Prove that dim(Ker(T)) is either n or n-1

  • Thread starter Hernaner28
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In summary, if two transformations are equal, their kernels must be equal. However, it is not clear why their images and everything else in the space must be equal.
  • #1
Hernaner28
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Homework Statement


Having:

[tex] \displaystyle T:V\to \mathbb{R}[/tex]
[tex] \displaystyle \dim\left( V \right)=n[/tex]

Prove that [tex] \displaystyle \dim\left( \ker \left( T \right) \right)=n\text{ or }n-1[/tex]

Homework Equations



The Attempt at a Solution



I would start stating the theorem of the dimensions:

[tex] \displaystyle \dim\left( V \right)=\dim\left( \ker \left( T \right) \right)+\dim\left( \operatorname{Im}\left( T \right) \right)[/tex]

But I get nowhere with that theorem. Any clue?

Thanks!
 
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  • #2
Hernaner28 said:

Homework Statement


Having:

[tex] \displaystyle T:V\to \mathbb{R}[/tex]
[tex] \displaystyle \dim\left( V \right)=n[/tex]

Prove that [tex] \displaystyle \dim\left( \ker \left( T \right) \right)=n\text{ or }n-1[/tex]

Homework Equations



The Attempt at a Solution



I would start stating the theorem of the dimensions:

[tex] \displaystyle \dim\left( V \right)=\dim\left( \ker \left( T \right) \right)+\dim\left( \operatorname{Im}\left( T \right) \right)[/tex]

But I get nowhere with that theorem. Any clue?

Thanks!

What is dim(V)? What are possibilities for dim(im(T))?
 
  • #3
Hmm yes, I know how to determine the rank of a matrix. What do you mean by the dimension of T? Im(T)?

Thanks
 
  • #4
Ahhhhmmm, dim(Im(T)) has to be less or equal than 1... or better: it either is 1 or zero Thanks!
 
  • #5
Alright, then I'm asked the following:

Having:
[tex] \displaystyle T:V\to \mathbb{R}[/tex]
[tex] \displaystyle S:V\to \mathbb{R}[/tex]
[tex] \displaystyle \dim\left( V \right)=n[/tex]

Prove that ker(T)=ker(S) iff exists a real number a (different to zero) such that T=aS

This looks obvious for me because if the transformations are equal, then not only their kernels but also their images and everything must be equal.. What is what I have to show then?

Thanks
 
  • #6
Sure, if two transformations are equal then their kernels must be equal. But what does that have to do with the problem here??
 
  • #7
Hernaner28 said:
Alright, then I'm asked the following:

Having:
[tex] \displaystyle T:V\to \mathbb{R}[/tex]
[tex] \displaystyle S:V\to \mathbb{R}[/tex]
[tex] \displaystyle \dim\left( V \right)=n[/tex]

Prove that ker(T)=ker(S) iff exists a real number a (different to zero) such that T=aS

This looks obvious for me because if the transformations are equal, then not only their kernels but also their images and everything must be equal.. What is what I have to show then?

Thanks

I don't see why that is obvious. One direction looks fairly clear, but you should still show the details, because I think it will familiarize you more with the elements of vector spaces and linear maps. (By the way, why do you say equal, we don't have T=S, we have T=aS.)

The other direction looks less clear, if their kernels are equal, It's not obvious to me why they have to be scalar multiples of one another. It may require the use of the image being 1-d.
You may ask, "why should I be so careful?" You could easily lead yourself to believing something false. We need to establish whether a given statement is true. That takes some of what's called mathematical maturity. You can come to recognize the care you must take. Your belief in the statement does not use the fact that a is nonzero. In fact, you seem to read it as S=T. Your belief in the statement does not use the fact that the codomain is ℝ, 1-d. It may require this fact. It seems if somebody adjusted the statement a bit so that it was false, you would not notice, and and that you would not be prepared to verify.

Now these things come with lots of practice, but I just wanted to point out some possible goals. And let's keep trying to investigate the problem you stated above!
 

Related to Prove that dim(Ker(T)) is either n or n-1

1. How do you define the dimension of the kernel of a linear transformation?

The dimension of the kernel of a linear transformation T, denoted as dim(Ker(T)), is the number of linearly independent vectors in the null space of T. In other words, it is the number of solutions to the equation T(x) = 0.

2. Why is it important to prove the dimension of the kernel of a linear transformation?

The dimension of the kernel gives important information about the properties of the linear transformation. It can tell us about the injectivity and surjectivity of T, as well as the rank and nullity of the associated matrix. This information is crucial in many applications of linear algebra.

3. How can we prove that dim(Ker(T)) is either n or n-1?

To prove that dim(Ker(T)) is either n or n-1, we can use the Rank-Nullity Theorem which states that the sum of the rank and nullity of a linear transformation is equal to the dimension of the domain. If T is an n x n matrix, then the dimension of the domain is n. If T is an (n+1) x n matrix, then the dimension of the domain is n+1.

4. Can dim(Ker(T)) be any other value besides n or n-1?

No, according to the Rank-Nullity Theorem, the dimension of the kernel can only be n or n-1. This is because the rank of a matrix is the number of linearly independent columns, and the nullity is the number of free variables in the reduced row echelon form of the matrix. These values can only add up to the dimension of the domain.

5. Can dim(Ker(T)) be negative?

No, the dimension of a vector space cannot be negative. Therefore, dim(Ker(T)) cannot be negative. It is always a non-negative integer.

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