Linear Algebra: Subspace proof

In summary, the conversation discusses proving that a set U is a vector subspace of a vector space V if and only if it satisfies the given conditions of closure under addition and scalar multiplication. The conversation also mentions using arbitrary definitions of addition and multiplication in proving this.
  • #1
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1. Homework Statement :

Prove: A set U [tex]\subset[/tex] V = (V, [tex]\oplus[/tex], [tex]\odot[/tex]) is a vector subspace of V if and only if ([tex]\forall[/tex]u1, u2 [tex]\in[/tex] U) (1/2 [tex]\odot[/tex] (u1 [tex]\oplus[/tex] u2) [tex]\in[/tex] U) and ([tex]\forall[/tex]u [tex]\in[/tex] U) ([tex]\forall[/tex]t [tex]\in \mathbb{R}[/tex]) (t [tex]\odot[/tex] u [tex]\in[/tex] U).

3. The Attempt at a Solution :

I don't have the first clue. To me, it seems that there is missing information. I know that for a subspace, it is sufficient to prove only closure under addition and scalar multiplication. Maybe he's defining a different sort of addition? Ugh, the whole proof thing is actually pretty new to me. I only started doing simple proofs last semester in Discrete Mathematics...
 
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  • #2
Maybe he's defining a different sort of addition?

He's using [tex]\oplus[/tex] and [tex]\odot[/tex] as representatives of addition and multiplication, respectively, just with other symbols. In this proof, you're working with an arbitrary vector space, so that addition and multiplication may not be defined in the sense that you're used to. In fact, they're both defined arbitrarily, and we can define addition and multiplication however we want to, as long as they satisfy the axioms of a vector space. However, even though the vector space operations are unknown, we do know how multiplication works in R.

In any case, here is what you must prove: for each t in R and for each u in U, [tex]t \odot u \in U[/tex] and for all [tex]u_1, u_2 \in U, \, u_1 \oplus u_2 \in U[/tex]. The first thing you need to prove has already been given to you. All you need to prove is closure under addition. I'll give you a hint, and then you're going to have to think a little bit about what to do. The hint is: [tex]2\in \mathbb{R}[/tex]. Now you're going to have to use the vector space axioms and the first given to figure out why closure under addition holds.
 
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  • #3
Thank you. Yeah, the next problem defines u [tex]\oplus[/tex] v (u, v in R3) as v X u. This one is simple in that v X u is not commutative and therefore R3 with this definition is not a vector space.
 

1. What is a subspace in linear algebra?

A subspace in linear algebra is a subset of a vector space that also satisfies the properties of a vector space. This means that it must contain the zero vector, be closed under vector addition and scalar multiplication, and must be non-empty.

2. How do you prove that a set is a subspace?

To prove that a set is a subspace, you must show that it satisfies the three properties of a vector space: closure under addition, closure under scalar multiplication, and containing the zero vector. You can also use the subspace test, which states that if you can find two vectors in the set that are both closed under addition and scalar multiplication, then the set is a subspace.

3. Can a subspace have more than one basis?

Yes, a subspace can have more than one basis. A basis is a set of linearly independent vectors that span the subspace. Since there can be multiple sets of linearly independent vectors that span the same subspace, there can be multiple bases for a subspace.

4. How do you prove that two subspaces are equal?

To prove that two subspaces are equal, you must show that they contain the same elements. This can be done by proving that one subspace is a subset of the other, and then proving that the other subspace is a subset of the first one. You can also show that the two subspaces have the same basis, which would also prove that they are equal.

5. Is the intersection of two subspaces always a subspace?

Yes, the intersection of two subspaces is always a subspace. This is because the intersection will contain all the elements that are in both subspaces, and it will also satisfy the three properties of a vector space. Therefore, the intersection will also be closed under addition, scalar multiplication, and contain the zero vector, making it a subspace.

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