Finding the dimension of a subspace

In summary, we have proven that if ##y## is a linear functional on an ##n##-dimensional vector space ##V##, then the set of all vectors ##x## such that ##[x, y] = 0## is a subspace of ##V## with dimension equal to either ##n## or ##n-1## depending on the rank of ##y##. This was proven by first showing that the set is non-empty and closed under addition and scalar multiplication, thus satisfying the subspace criteria. Then, using the rank-nullity theorem, we determined the dimension of the subspace to be ##n - rank(y)##.
  • #1
fishturtle1
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Homework Statement
Prove that if ##y## is a linear functional on an ##n##-dimensional vector space ##V##, then the set of all those vectors ##x## such that ##[x, y] = 0## is a subspace of ##V##; what is the dimension of this subspace?
Relevant Equations
A function ##y : V \rightarrow \mathbb{R}## is a linear functional on ##V## if for all ##u, v \in V## and ##\alpha, \beta \in F##, we have ##[\alpha u + \beta v , y] = \alpha [u, y] + \beta [v, y]##.

We have the notation ##[v, y]## to mean ##y(v)##.
I am stuck on finding the dimension of the subspace. Here's what I have so far.

Proof: Let ##W = \lbrace x \in V : [x, y] = 0\rbrace##. We see ##[0, y] = 0##, so ##W## is non empty. Let ##u, v \in W## and ##\alpha, \beta## be scalars. Then ##[\alpha u + \beta v, y] = \alpha [u, y] + \beta [v, y] = \alpha \cdot 0 + \beta \cdot 0 = 0##. So, ##\alpha u + \beta v \in W##. This shows ##W## is a subspace of ##V##.

Next, we will find the dimension of ##W##. Let ##x_1, x_2, \dots, x_n## be a basis of ##V##. We can find a maximal subset of this basis ##z_1, \dots, z_k## such that ##[z_i, y] = 0## for all ##i##. We claim this is a basis for ##W##. Since ##u \in V##, there exists scalars ##\alpha_1, \dots, \alpha_n## such that ##u = \alpha_1 z_1 + \dots + \alpha_k z_k + \alpha_{k+1}x_{k+1} + \dots + \alpha_n x_n##. Then,
$$[u, y] = [\alpha_1 z_1 + \dots + \alpha_k z_k + \alpha_{k+1}x_{k+1} + \dots + \alpha_n x_n, y] = \alpha_{k+1} [x_{k+1}, y] + \dots + \alpha_n [x_n, y] = 0$$.
We want to somehow conclude ##\alpha_{k+1} = \dots = \alpha_n = 0##, but I'm not sure how to proceed. Can I have a hint, please?
 
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  • #2
I don't see what you are trying to do with the dimension proof. Do you know the rank formula for linear functions ##\varphi : V\longrightarrow W##?
 
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  • #3
fishturtle1 said:
Homework Statement:: Prove that if ##y## is a linear functional on an ##n##-dimensional vector space ##V##, then the set of all those vectors ##x## such that ##[x, y] = 0## is a subspace of ##V##; what is the dimension of this subspace?
Relevant Equations:: A function ##y : V \rightarrow \mathbb{R}## is a linear functional on ##V## if for all ##u, v \in V## and ##\alpha, \beta \in F##, we have ##[\alpha u + \beta v , y] = \alpha [u, y] + \beta [v, y]##.

We have the notation ##[v, y]## to mean ##y(v)##.
Why not use a more standard notation?
I.e., ##f: V \rightarrow \mathbb{R}##
and ##f(v)## instead of [v, y]
fishturtle1 said:
I am stuck on finding the dimension of the subspace. Here's what I have so far.
I don't think the work below is going to get you anywhere. You're not going to be able to find the dimension of the nullspace directly, because you don't know how many vectors are in a basis for the nullspace. A better option is to use the rank-nullity theorem - see Rank–nullity theorem - Wikipedia .
fishturtle1 said:
Proof: Let ##W = \lbrace x \in V : [x, y] = 0\rbrace##. We see ##[0, y] = 0##, so ##W## is non empty. Let ##u, v \in W## and ##\alpha, \beta## be scalars. Then ##[\alpha u + \beta v, y] = \alpha [u, y] + \beta [v, y] = \alpha \cdot 0 + \beta \cdot 0 = 0##. So, ##\alpha u + \beta v \in W##. This shows ##W## is a subspace of ##V##.

Next, we will find the dimension of ##W##. Let ##x_1, x_2, \dots, x_n## be a basis of ##V##. We can find a maximal subset of this basis ##z_1, \dots, z_k## such that ##[z_i, y] = 0## for all ##i##. We claim this is a basis for ##W##. Since ##u \in V##, there exists scalars ##\alpha_1, \dots, \alpha_n## such that ##u = \alpha_1 z_1 + \dots + \alpha_k z_k + \alpha_{k+1}x_{k+1} + \dots + \alpha_n x_n##. Then,
$$[u, y] = [\alpha_1 z_1 + \dots + \alpha_k z_k + \alpha_{k+1}x_{k+1} + \dots + \alpha_n x_n, y] = \alpha_{k+1} [x_{k+1}, y] + \dots + \alpha_n [x_n, y] = 0$$.
We want to somehow conclude ##\alpha_{k+1} = \dots = \alpha_n = 0##, but I'm not sure how to proceed. Can I have a hint, please?
 
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  • #4
Thank you for your replies. Yes, I think we can use Rank-Nullity theorem, I will try it.

And yeah, I'm happy to use a more standard notation if that's better; I was trying to mimic the textbook.
 
  • #5
fishturtle1 said:
Thank you for your replies. Yes, I think we can use Rank-Nullity theorem, I will try it.

And yeah, I'm happy to use a more standard notation if that's better; I was trying to mimic the textbook.
You could also use ##\langle y, .\rangle\, : \,v\longmapsto \langle y,v\rangle## in case it is an inner product space.
 
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  • #6
fresh_42 said:
You could also use ##\langle y, .\rangle\, : \,v\longmapsto \langle y,v\rangle## in case it is an inner product space.
I don't think that's applicable here, about an inner product space. My objection to the notation [x, y] was that it hinted at being an inner product, but wasn't.
 
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  • #7
We know ##y : V \rightarrow \mathbb{R}## is a linear transformation. By Rank-Nullity theorem we have ##\dim V = rank(y) + null(y)##. We note that ##null(y) = \dim W##. If ##rank(y) = 0##, then ##\dim V = \dim W## and so ##\dim W = n##.

If ##rank(y) \neq 0##, then ##rank(y) = 1##. And so ##\dim W = n - 1##.
 
  • #8
Mark44 said:
I don't think that's applicable here, about an inner product space. My objection to the notation [x, y] was that it hinted at being an inner product, but wasn't.
In case we have finite dimensional vector space, there is a one-to-one correspondence between ##V## and ##V^*##, and if it is a real vector space, then any ##v\in V^*## can be written as some ##\langle y_v,-\rangle##, i.e. the bijection can be realized by the inner product. This should even hold for other fields of characteristic zero.

However, you were right that the brackets were problematic. They are usually used for commutators, in group as well as in algebra theory.
 
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1. What is a subspace?

A subspace is a subset of a vector space that satisfies the properties of a vector space, such as closure under addition and scalar multiplication.

2. How do you find the dimension of a subspace?

To find the dimension of a subspace, you can use the rank-nullity theorem which states that the dimension of a subspace is equal to the number of linearly independent vectors in that subspace.

3. Can a subspace have a dimension of 0?

Yes, a subspace can have a dimension of 0 if it only contains the zero vector. This is because the zero vector is considered linearly independent from all other vectors.

4. Can a subspace have a negative dimension?

No, a subspace cannot have a negative dimension. The dimension of a subspace is always a non-negative integer.

5. How does the dimension of a subspace relate to the dimension of the vector space it is a part of?

The dimension of a subspace is always less than or equal to the dimension of the vector space it is a part of. This is because a subspace is a subset of a vector space and therefore cannot have a larger dimension than the vector space itself.

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