How Do You Prove the Dimension of a Direct Sum Equals the Sum of Dimensions?

Click For Summary

Discussion Overview

The discussion revolves around proving that the dimension of the direct sum of subspaces of a vector space V equals the sum of the dimensions of the individual subspaces. Participants explore the conditions under which this statement holds, particularly focusing on the requirement that the intersection of the subspaces must be trivial (only containing the zero vector).

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant suggests starting by choosing a basis for each subspace and combining them into a list that spans V, aiming to show that this list is linearly independent.
  • Another participant proposes that if a vector in the combined list is linearly dependent on the others, it must belong to the span of the previous vectors, leading to a contradiction if the intersection of the subspaces is trivial.
  • Some participants argue that the statement cannot be proven without the condition that the only vector contained in both subspaces is the zero vector, asserting that this condition is essential for the proof.
  • There is a contention regarding the validity of the statement itself, with some asserting it is not true without the specified condition, while others maintain that it is true under the correct assumptions.

Areas of Agreement / Disagreement

Participants disagree on whether the statement can be proven without the condition that the intersection of the subspaces is trivial. Some assert that the proof relies on this condition, while others challenge the validity of the statement itself without it.

Contextual Notes

The discussion highlights the importance of the intersection condition in the context of direct sums and the implications for linear independence in the proof process. There is an acknowledgment that the proof's validity is contingent on this condition being met.

jecharla
Messages
24
Reaction score
0
Exercise #17 in Linear Algebra done right is to prove that the dimension of the direct sum of subspaces of V is equal to the sum of the dimensions of the individual subspaces. I have been trying to figure this out for a few days now and I'm really stuck. Here's what I have got so far:

Choose a base of each of the subspaces and combine them in one list which clearly spans V and has the length we are looking for. Now we just have to show that this list is linearly independent.

If this list is not linearly independent than one of the vectors is in the span of the previous vectors. If I could show that this vector must be in the span of one of the other bases, then that would prove the result but I can't quite figure out how to get there. Any help would be greatly appreciated.
 
Physics news on Phys.org
jecharla said:
Exercise #17 in Linear Algebra done right is to prove that the dimension of the direct sum of subspaces of V is equal to the sum of the dimensions of the individual subspaces. I have been trying to figure this out for a few days now and I'm really stuck. Here's what I have got so far:

Choose a base of each of the subspaces and combine them in one list which clearly spans V and has the length we are looking for. Now we just have to show that this list is linearly independent.

If this list is not linearly independent than one of the vectors is in the span of the previous vectors. If I could show that this vector must be in the span of one of the other bases, then that would prove the result but I can't quite figure out how to get there. Any help would be greatly appreciated.



Let's do it for two subspaces as this summarizes the logical argument: suppose [itex]A:=\{v_1,...,v_k\}\,\,,\,\,B:=\{w_1,...,w_r\}\,[/itex] are basis resp. of subspaces [itex]\,U,W\leq V\,\,,\,\,with\,\,\,U\cap W=\{0\}[/itex].

Suppose one of the vectors in [itex]\,\{v_1,...,v_k,w_1,...,w_r\}\,[/itex] depends linearly on the preceeding ones. Then this must be

one of the [itex]\,w_i\,[/itex]'s (why?) , so [tex]\,w_i\in Span\{v_1,...,v_k,w_1,...,w_{i-1}\}\,\Longrightarrow w_i=a_1v_1+...+a_kv_k+b_1w_1+...+b_{i-1}w_{i-1}[/tex] We then get [tex]a_1v_1+...+a_kv_k=-b_1w_1-...-b_{i-1}w_{i-1}+w_i\in V\cap W=\{0\}[/tex]

From here, [itex]\,a_1v_1+...+a_kv_k=0=-b_1w_1-...-b_{i-1}w_{i-1}+w_i\,[/itex] , from where we reach at once our contradiction.

DonAntonio
 
jecharla said:
Exercise #17 in Linear Algebra done right is to prove that the dimension of the direct sum of subspaces of V is equal to the sum of the dimensions of the individual subspaces.
As stated, you can't prove this, it isn't true. It is true and you can prove it if you add the condition that the only vector contained in both subspaces is the 0 vector. Of course, your proof will have to use that condition.

I have been trying to figure this out for a few days now and I'm really stuck. Here's what I have got so far:

Choose a base of each of the subspaces and combine them in one list which clearly spans V and has the length we are looking for. Now we just have to show that this list is linearly independent.

If this list is not linearly independent than one of the vectors is in the span of the previous vectors. If I could show that this vector must be in the span of one of the other bases, then that would prove the result but I can't quite figure out how to get there. Any help would be greatly appreciated.
If they were not you could construct a non-zero vector that is in both subspaces.
 
HallsofIvy said:
As stated, you can't prove this, it isn't true. It is true and you can prove it if you add the condition that the only vector contained in both subspaces is the 0 vector. Of course, your proof will have to use that condition.



This condition is implicit in the direct sum thing: cero must be the intersection of any space and the sum of all the others, otherwise the sum is not direct.

Donantonio

If they were not you could construct a non-zero vector that is in both subspaces.
 

Similar threads

  • · Replies 3 ·
Replies
3
Views
5K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 6 ·
Replies
6
Views
4K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 7 ·
Replies
7
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K
Replies
2
Views
2K
  • · Replies 23 ·
Replies
23
Views
2K
Replies
5
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K