Collection of Subspaces of a Vector Space

In summary, the proof provided is flawed and does not hold up to scrutiny. The assumptions made are not valid for all cases and the proof fails to consider important cases such as when the dimension of the vector space is 2. Further analysis and revisions are needed to make the proof valid.
  • #1
Sudharaka
Gold Member
MHB
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Hi everyone, :)

Here's a question I am struggling with recently. Hope you can give me some hints or ideas on how to solve this.

Question:

If the collection of subspaces of the \(K\)-vector space \(V\) satisfies either distributive law \(A+(B\cap C)=(A+B)\cap (A+C)\) or \(A\cap (B+C)=(A\cap B)+(A\cap C)\), show that \(\mbox{dim}_{k}V\leq 1\).
 
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  • #2
I think I have a solution. It would be nice if somebody could confirm its correctness. :)

It is trivial that \(\mbox{dim}_{K}V=0\) when \(V=\left\{0\right\}\). Take any two elements \(v_1\mbox{ and }v_2\in V\). Then consider the subspaces spanned by these elements. Let, \(A=<v_1+v_2>,\, B=<v_1>,\,C=<v_2>\)

Let us check whether these subspaces satisfy the first distributive law,

\[A+(B\cap C)=A\Rightarrow \mbox{dim}(A+(B\cap C))=1\]

and (note that all the pairwise intersections of these subspaces are trivial),

\begin{eqnarray}

\mbox{dim}\left[(A+B)\cap (A+C)\right]&=&\mbox{dim}(A+B)+\mbox{dim}(A+C)-\mbox{dim}(A+B+C)\\

&=&\mbox{dim}(A+B)+\mbox{dim}(A+C)-\mbox{dim}(B+C)\\

&=&\mbox{dim}(A)+\mbox{dim}(B)+\mbox{dim}(A)+\mbox{dim}(C)-\left(\mbox{dim}(B)+\mbox{dim}(C)\right)\\

\therefore \mbox{dim}\left[(A+B)\cap (A+C)\right]&=& 2

\end{eqnarray}

Hence these particular subspaces do not satisfy the first distributive law. Then they should satisfy the second distributive law.

\[A\cap(B+C)=A\]

and,

\[(A\cap B)+(A\cap C)=\{0\}\]

Therefore, \(A=\{0\}\) and this implies, \(v_1=-v_2\). Since we have chosen \(v_1\mbox{ and }v_2\) to be arbitrary elements this means that \(V\) is generated by one element. That is, \(\mbox{dim}_{K}V=1\).

\[\therefore \mbox{dim}_{K}V\leq 1\]
 
  • #3
There are a couple of problems with your proof:

1. There is no guarantee $v_1,v_2$ are distinct, non-zero, or linearly independent. For example, if $V = F_2$, there is only one non-zero vector.

2. I fail to see how you can assume the intersection of your 3 spaces is pairwise trivial. This certainly is NOT true if $\text{dim}_{K}(V) = 1$ and $v_1,v_2$ are non-zero.

3. Your argument that:

$\text{dim}[(A+B)\cap(A+C)] = 2$ seems severely flawed, and seems to rest on the assumption that $V$ is of dimension at least 2, with 2 linearly independent vectors $v_1,v_2$.

A similar problem exists with asserting that:

$(A \cap B) + (A \cap C) = \{0\}$.

If your conclusion (that $V$ is of dimension 1 or less) is indeed true, that will not be the case, unless both of $v_1,v_2$ are 0.
 
  • #4
Deveno said:
There are a couple of problems with your proof:

1. There is no guarantee $v_1,v_2$ are distinct, non-zero, or linearly independent. For example, if $V = F_2$, there is only one non-zero vector.

2. I fail to see how you can assume the intersection of your 3 spaces is pairwise trivial. This certainly is NOT true if $\text{dim}_{K}(V) = 1$ and $v_1,v_2$ are non-zero.

3. Your argument that:

$\text{dim}[(A+B)\cap(A+C)] = 2$ seems severely flawed, and seems to rest on the assumption that $V$ is of dimension at least 2, with 2 linearly independent vectors $v_1,v_2$.

A similar problem exists with asserting that:

$(A \cap B) + (A \cap C) = \{0\}$.

If your conclusion (that $V$ is of dimension 1 or less) is indeed true, that will not be the case, unless both of $v_1,v_2$ are 0.

Thank you so much for pointing out the flaws in my proof. Let me explain how I thought about it.

Yes, I basically assumed that \(V\) contains at least two distinct, non-zero, linearly independent elements. If \(V\) contains only the element \(0\) then it's dimension is obviously zero. If \(V\) contains just two elements as you suggested (0 and another non-zero element), it's dimension would be one. These are trivial cases and we know that the inequality is satisfied in all these cases. So I just wrote the line "It is trivial that \(\mbox{dim}_{K}V=0\) when \(V=\{0\}\)" to put these details under the carpet. :eek:
 
  • #5
My point is, $V$ could be a field, in which case we cannot pick 2 linearly independent vectors at all. This is NOT the same as saying $V = \{0\}$.

In fact, we might have that $V$ is the smallest field possible, the galois field of order 2, in which case $V$ contains only ONE non-zero element (and $V \neq \{0\})$, which turns out to be its multiplicative identity.

What I think you need to do, is show that if $\text{dim}_K(V) \leq 1$, there is nothing to prove. Then, by way of forcing a contradiction, assume that one of the distributive laws holds, and show that if this holds, and $\text{dim}_K(V) \geq 2$, we have a contradiction to a certain (linearly independent) choice of $v_1,v_2$.

Pay CAREFUL ATTENTION to the case $\text{dim}_K(V) = 2$.

In this case, what you need to do is show that:

$\langle v_1,v_2\rangle = \langle v_1+v_2,v_1\rangle = \langle v_1+v_2,v_2\rangle$.

This is what will allow you to get specific values for the dimensions of the subspaces you want.

If $V$ were assumed finite-dimensional, you could without loss of generality, take $V$ to be:

$K^{\text{dim}_K(V)}$ and choose $v_1 = e_1$, $v_2 = e_2$.

But generally, using the axiom of choice, we could take some basis of $V$ (we actually NEED the axiom of choice to assume we HAVE a basis, this is something of a subtle point for infinite-dimensional spaces), and pick any two distinct basis elements. If $V$ is not finite-dimensional, it might be better to argue that $V$ must have at least 2 linearly independent elements (or else $V$ only has dimension 1, and we have nothing to prove, as indicated above). This avoids having to invoke the axiom of choice, which is rather like using a sledgehammer to swat a fly, for this particular problem.

The "heart" of your proof DOES work, but you left a "hole" for the 1-dimensional case, and proofs shouldn't HAVE holes.
 

FAQ: Collection of Subspaces of a Vector Space

What is a subspace?

A subspace is a subset of a vector space that follows the same rules as the vector space. This means that a subspace must be closed under addition and scalar multiplication, and it must contain the zero vector.

How is a subspace different from a vector space?

A subspace is a subset of a vector space, while a vector space is a set of vectors that can be added and multiplied by scalars. A subspace must follow the same rules as the vector space, but it may not contain all of the elements of the vector space.

Can a subspace contain only one vector?

Yes, a subspace can contain only one vector. As long as that vector is closed under addition and scalar multiplication and contains the zero vector, it is considered a subspace of the vector space.

What is the intersection of two subspaces?

The intersection of two subspaces is the set of all elements that are contained in both subspaces. This means that the intersection of two subspaces is also a subspace, as it follows the same rules of closure and contains the zero vector.

How can I prove that a set is a subspace?

To prove that a set is a subspace, you must show that it follows the rules of closure under addition and scalar multiplication, and that it contains the zero vector. You can also prove a set is a subspace by showing that it is a span of a set of vectors from a vector space.

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