Proving a statement about inclusion of subspaces

  • Thread starter JD_PM
  • Start date
  • Tags
    Subspaces
In summary, the conversation discusses the theorem and proof found on MSE regarding subspaces in a vector space. The theorem states that if there are more than n+1 subspaces, there must be an index i<r for which the subspaces are equal. The conversation also mentions a counterexample where the proposition does not hold. The main point of discussion is whether the subspaces must be strictly contained or if they can be equal.
  • #1
JD_PM
1,131
158
Summary:: I want to understand the following theorem and its proof (which can be found in MSE, link below): Let ##V## be a ##n##-dimensional vector space, let ##U_i \subseteq V## be subspaces of ##V## for ##i = 1,2,\dots,r## where $$U_1 \subseteq U_2 \subseteq \dots \subseteq U_r$$
If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}=\dots=U_r##

I understand that if the subspaces were to be strictly contained (i.e. no possibility of equality between them) then, when going from ##U_i## to ##U_{i+1}##, the dimension of the subspace would increase by one. Hence, when reaching ##U_r## the dimension would be greater than ##n+1##. However, this is not possible since ##U_r## is a subspace of ##V## and thus has dimension at most ##n##.

My issues are

1) I do not see why ##\dim U_r>\dim U_i+r-1## follows from the above argument

2) This is related to 1). I do not see why MSE user egreg here assumes non-equality of the subspaces

judehzufdhezufiheiuhzefuihfze.png


I appreciate your guidance! :biggrin:
 
Physics news on Phys.org
  • #2
Looks like nonsense to me. ##U_1 = U_2 = \dots = U_{r-1} = \emptyset## and ##U_r = V## is a clear counterexample.

In general, the equalities and strict inclusions could be in any order.
 
  • #3
We need some strict inclusions somewhere, e.g. ##U_r\subsetneq V.##

The idea is that you cannot build chains that are longer than the finite dimension of ##V##. If ##U_i\subseteq U_{i+1}## then they are either equal or ##\dim U_{i+1}> \dim U_i##. But we can only use at most ##\dim V## steps. But if everything can be equal, then we could get endless chains with equalities.

One could say: Every chain ##U_1\subseteq \ldots \subseteq U_r\subseteq \ldots## becomes stationary, i.e. there is an index ##r## such that ##U_p=U_r## for all ##p>r.##
 
Last edited:
  • Like
Likes JD_PM
  • #4
fresh_42 said:
The idea is that you cannot build chains that are longer than the finite dimension of ##V##. If ##U_i\subseteq U_{i+1}## then they are either equal or ##\dim U_{i+1}> \dim U_i##. But we can only use at most ##\dim V## steps. But if everything can be equal, then we could get endless chains with equalities.

Oh, I see. So if we were to deal with strict inclusions, ##U_i\subsetneq U_{i+1}##, we would need ##\dim U_{i+1}\ge \dim U_i+1## (i.e. we need to add the +1 difference between dimensions of consecutive strict subspaces).

fresh_42 said:
We need some strict inclusions somewhere, e.g. ##U_r\subsetneq V.##

Would you prove it as MSE user egreg or would you approach it differently?

jdqsiodqodjqsjdqssqpdqjdiqs.png


I think I understand his proof but I am really opened to learn other ways.
 
  • #5
JD_PM said:
Oh, I see. So if we were to deal with strict inclusions, ##U_i\subsetneq U_{i+1}##, we would need ##\dim U_{i+1}\ge \dim U_i+1## (i.e. we need to add the +1 difference between dimensions of consecutive strict subspaces).
Would you prove it as MSE user egreg or would you approach it differently?

View attachment 286249

I think I understand his proof but I am really opened to learn other ways.
If you think that's a proof, then you have the thorny matter of my counterexample to deal with!
 
  • #6
PeroK said:
If you think that's a proof, then you have the thorny matter of my counterexample to deal with!

I am afraid I do not understand your counterexample. Might you please add more details? Thank you.
 
  • #7
JD_PM said:
I am afraid I do not understand your counterexample. Might you please add more details? Thank you.
##V = \mathbb{R^3}, \ U_1 = U_2 = U_3 = \mathbb{R}, U_4 = \mathbb{R^2}, \ U_5 = \mathbb{R^3}##

So: ##n = 3, r = 5## and your proposition fails, as there is no ##i < r## for which ##U_i = U_r##.

The "proof" is nonsense, as the proposition is clearly false.
 
  • Like
Likes JD_PM
  • #8
PeroK said:
##V = \mathbb{R^3}, \ U_1 = U_2 = U_3 = \mathbb{R}, U_4 = \mathbb{R^2}, \ U_5 = \mathbb{R^3}##

So: ##n = 3, r = 5## and your proposition fails, as there is no ##i < r## for which ##U_i = U_r##.

The "proof" is nonsense, as the proposition is clearly false.
##U_1\varsubsetneq U_2## means that the inclussion is proper, the two spaces cannot be equal.
 
  • #9
martinbn said:
##U_1\varsubsetneq U_2## means that the inclussion is proper, the two spaces cannot be equal.
Well, then you cannot have:

JD_PM said:
If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}=\dots=U_r##
More to the point, you cannot have more than ##n + 1## subspaces in the first place.
 
  • #10
PeroK said:
Well, then you cannot have:More to the point, you cannot have more than ##n + 1## subspaces in the first place.
But that's the point, isn't it?
 
  • #11
martinbn said:
But that's the point, isn't it?
I don't see that as the stated proposition anywhere. The stated proposition is that the subspaces are eventually equal.
 
  • #12
PeroK said:
I don't see that as the stated proposition anywhere. The stated proposition is that the subspaces are eventually equal.
I read the accual proposition agian, yes, you are right. It is so strange I had missread it.
 
  • #13
PeroK said:
##V = \mathbb{R^3}, \ U_1 = U_2 = U_3 = \mathbb{R}, U_4 = \mathbb{R^2}, \ U_5 = \mathbb{R^3}##

So: ##n = 3, r = 5## and your proposition fails, as there is no ##i < r## for which ##U_i = U_r##.

The "proof" is nonsense, as the proposition is clearly false.

Ahhh I see the misunderstanding! :) I should have written ##U_i = U_{i+1}## instead of ##U_i = U_{i+1}=\dots=U_r## in the original statement. For the sake of clarity, let me state the theorem again

Let ##V## be a ##n##-dimensional vector space, let ##U_i \subset V## be subspaces of ##V## for ##i = 1,2,\dots,r## where $$U_1 \subset U_2 \subset \dots \subset U_r$$
If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}##

Your counterexample does not apply now as , for instance, we have ##U_2 = U_3##.

If you agree, we could proceed to discuss its proof.
 
  • #14
JD_PM said:
Ahhh I see the misunderstanding! :) I should have written ##U_i = U_{i+1}## instead of ##U_i = U_{i+1}=\dots=U_r## in the original statement. For the sake of clarity, let me state the theorem again

Let ##V## be a ##n##-dimensional vector space, let ##U_i \subset V## be subspaces of ##V## for ##i = 1,2,\dots,r## where $$U_1 \subset U_2 \subset \dots \subset U_r$$
If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}##

Your counterexample does not apply now as , for instance, we have ##U_2 = U_3##.

If you agree, we could proceed to discuss its proof.
The proof would be simplified, it seems to me, by first showing that ##d_1 \le d_2 \dots \le d_r##, where ##d_i## is the dimension of ##U_i##.

Then, given that there are only ##n+1## integers from ##0## to ##n## inclusive, if ##r > n+1##, then we cannot have strict inequalities all along the line (do you really need to prove this formally?). So, we must have at least one ##i < r##, such that ##d_i = d_{i+1}##.

Then, show that this implies ##U_i = U_{i+1}##.
 
  • #15
PeroK said:
The proof would be simplified, it seems to me, by first showing that ##d_1 \le d_2 \dots \le d_r##, where ##d_i## is the dimension of ##U_i##.

Then, given that there are only ##n+1## integers from ##0## to ##n## inclusive, if ##r > n+1##, then we cannot have strict inequalities all along the line (do you really need to prove this formally?). So, we must have at least one ##i < r##, such that ##d_i = d_{i+1}##.

Then, show that this implies ##U_i = U_{i+1}##.

It is not necessary to prove it formally but it might be enlightening to learn it that way so, why not? :)

I'll think more about it and come back.
 
  • #16
JD_PM said:
It is not necessary to prove it formally but it might be enlightening to learn it that way so, why not? :)

I'll think more about it and come back.
If you roll a normal (six-sided) die seven times, can all the outcomes be different? The problem, I find, if you start trying to prove those things is that the things you have to assume to do anything seem no more elementary than the result itself.

Assuming you are not going down into the foundations of mathematics, I think it's safe to assume that you cannot have ##r## distinct numbers in a set of ##n+1## numbers, where ##r > n+1##.

If that's not obvious to you, then I don't don't really know how to explain why it's obvious to me!
 
  • #17
JD_PM said:
I'll think more about it and come back.
To deal with possible strict inclusions, and not strict inclusions is a nightmare as @PeroK's examples show. There are too many possibilities that provide possible counterexamples. Prove my claim in post #3 instead. It is short and clear. Firstly think about which kind of proof looks most promising, and then start from one conclusion to the next, do not jump.

Every chain ##U_1\subseteq \ldots \subseteq U_r\subseteq \ldots## becomes stationary, i.e. there is an index ##r## such that ##U_p=U_r## for all ##p>r.##

(https://www.physicsforums.com/insights/how-most-proofs-are-structured-and-how-to-write-them/
is a short essay about proofs in general. Maybe it can help you a bit.)
 
  • Like
Likes JD_PM and PeroK
  • #18
I think I understand it! :cool:

Please be critic :biggrin:

fresh_42 said:
Every chain ##U_1\subseteq \ldots \subseteq U_r\subseteq \ldots## becomes stationary, i.e. there is an index ##r## such that ##U_p=U_r## for all ##p>r.##

What we want to prove: If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}##.

Approach to prove it: contradiction. We assume that if ##r > n+1## then ##\forall i < r## we have ##U_i \neq U_{i + 1}## (in other words, we assume all inclusions to be strict).

As I was taught during lectures, we define the trivial vectorspace ##\{ 0 \}## to have dimension zero. This implies that ##\dim U_1 \geq 0##.

Let us look at ##U_1\subsetneq U_2##.

During lectures I learned a theorem stating that given a ##n-##dimensional vectorspace ##(\Bbb R, V, +)## and a finite subspace ##U## it holds that ##\dim U \leq \dim V##. Hence, taking into account that we deal with strict inclusions, it follows that ##\dim U_2 > \dim U_1 \geq 0##. Actually ##U_2## contains (at least) a non-trivial natural number i.e. any ##x \in \mathbb{N} \setminus \{ 0\} ##. It follows that ##\dim U_2 \geq 1##.

It follows that for ##U_2\subsetneq U_3## we get ##\dim U_3 \geq 2 = 3 - 1## and the pattern yields ##\dim U_r \geq r - 1##. We are given that ##r > n+1## so it follows that ##\dim U_r \geq r - 1 > n + 1 - 1 = n ## i.e. ##\dim U_r > n ##

"AJÁ", CONTRADICTION! Based on the theorem I stated above, we require the dimension of the vectorspace ##V## and the subspace ##U## to satisfy ##\dim U \leq \dim V##

PS: Honestly, this is one of the math problems I've had more fun with in recent times! If you are aware of more problems like this (linear algebra with "dimension analysis" (I am sure there is a better name to be used here but I am a physicist not a mathematician :-p) involved, please let me know :) ).
 
  • #19
JD_PM said:
I think I understand it! :cool:

Please be critic :biggrin:
What we want to prove: If ##r>n+1## then there exists an ##i<r## for which ##U_i = U_{i+1}##.
This is not what I have stated!
JD_PM said:
Approach to prove it: contradiction. We assume that if ##r > n+1## then ##\forall i < r## we have ##U_i \neq U_{i + 1}## (in other words, we assume all inclusions to be strict).
As I was taught during lectures, we define the trivial vectorspace ##\{ 0 \}## to have dimension zero. This implies that ##\dim U_1 \geq 0##.

Let us look at ##U_1\subsetneq U_2##.

During lectures I learned a theorem stating that given a ##n-##dimensional vectorspace ##(\Bbb R, V, +)## and a finite subspace ##U## it holds that ##\dim U \leq \dim V##. Hence, taking into account that we deal with strict inclusions, it follows that ##\dim U_2 > \dim U_1 \geq 0##. Actually ##U_2## contains (at least) a non-trivial natural number i.e. any ##x \in \mathbb{N} \setminus \{ 0\} ##. It follows that ##\dim U_2 \geq 1##.

It follows that for ##U_2\subsetneq U_3## we get ##\dim U_3 \geq 2 = 3 - 1## and the pattern yields ##\dim U_r \geq r - 1##. We are given that ##r > n+1## so it follows that ##\dim U_r \geq r - 1 > n + 1 - 1 = n ## i.e. ##\dim U_r > n ##

"AJÁ", CONTRADICTION! Based on the theorem I stated above, we require the dimension of the vectorspace ##V## and the subspace ##U## to satisfy ##\dim U \leq \dim V##

PS: Honestly, this is one of the math problems I've had more fun with in recent times! If you are aware of more problems like this (linear algebra with "dimension analysis" (I am sure there is a better name to be used here but I am a physicist not a mathematician :-p) involved, please let me know :) ).
This is correct, except that it is a different statement. You have proven that there cannot be more than ##n+1## strict inclusions in a chain of length ##n+1##.

I have claimed, that regardless of how many equalities we have, or do not have, there will be some index form which on there are only equalities left. And note, that I haven't used any dimension in the statement, nor that the chain ends with ##V.##

However, you can prove my statement with yours.
 
Last edited:

1. What is the definition of a subspace?

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

2. How do you prove that one subspace is included in another subspace?

To prove that one subspace is included in another subspace, you must show that all elements of the first subspace are also elements of the second subspace. This can be done by showing that the three properties of a subspace are satisfied for all elements in the first subspace.

3. Can a subspace be included in itself?

Yes, a subspace can be included in itself because all elements of a subspace are also elements of itself. This is known as the reflexive property.

4. What is the difference between inclusion and equality of subspaces?

Inclusion of subspaces means that one subspace is a subset of another subspace, while equality of subspaces means that the two subspaces are exactly the same. Inclusion is a weaker condition than equality.

5. Are there any shortcuts or tricks for proving inclusion of subspaces?

There are no shortcuts or tricks for proving inclusion of subspaces. Each proof must be done systematically by showing that all elements of one subspace are also elements of the other subspace.

Similar threads

  • Math Proof Training and Practice
Replies
1
Views
2K
  • Linear and Abstract Algebra
Replies
7
Views
2K
  • Calculus and Beyond Homework Help
Replies
34
Views
2K
  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Linear and Abstract Algebra
Replies
1
Views
843
  • Linear and Abstract Algebra
Replies
2
Views
971
  • Linear and Abstract Algebra
Replies
3
Views
3K
  • Linear and Abstract Algebra
Replies
1
Views
1K
  • Linear and Abstract Algebra
Replies
3
Views
1K
Back
Top