What Is Required to Prove a Subset is a Vector Space?

In summary, Some references say that to show that V is a vector space, we need to show that: 1) V is not empty 2) V is closed under scalar multiplication 3) V contains the 0 vector. Other references show that in order to show that V is a vector space, we need to show that: 1) V is not empty 2) V is closed under + 3) V is closed under scalar multiplication.
  • #1
Yankel
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Hello all,

I have a theoretical question regarding subspaces.

If V is a subset of a vector space, and we wish to show that V is a vector space itself, we need to show 3 things.

Some references say we need to show: a) V is not empty b) V is closed under + c) V is closed under scalar multiplication. Other references are similar, with (a) being that V contains the 0 vector in it.

What I don't understand is:

1) Why being non empty and having the 0 vector is the same thing ?
2) I can't think of any example in which a set is closed under + and scalar multiplication, but does not contain the 0 vector. If this case exist, it ain't a subspace, but if the set isn't empty, it is ??

I am confused...
 
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  • #2
Yankel said:
Hello all,

I have a theoretical question regarding subspaces.

If V is a subset of a vector space, and we wish to show that V is a vector space itself, we need to show 3 things.

Some references say we need to show: a) V is not empty b) V is closed under + c) V is closed under scalar multiplication. Other references are similar, with (a) being that V contains the 0 vector in it.

What I don't understand is:

1) Why being non empty and having the 0 vector is the same thing ?
2) I can't think of any example in which a set is closed under + and scalar multiplication, but does not contain the 0 vector. If this case exist, it ain't a subspace, but if the set isn't empty, it is ??

I am confused...

ad 1) I understand that you assume b) and c) and then want to show that the two formulations of condition a) are equivalent.

If $V$ is non-empty, then it contains a vector $x$. Now $V$ is closed under scalar multiplication, so $0\cdot x = \mathbf{0} \in V$, where $0$ is a scalar and $\mathbf{0}$ is the zero vector.

Conversely, if $V$ contains $\mathbf{0}$ then it is of course non-empty.

ad 2) You are right, the only such example would be the empty set: It is trivially closed under vector addition and scalar multiplication, but it does not contain the zero vector.
 
  • #3
"Being non-empty" and "containing the 0 vector" are not the same thing, alone! However, with the other condirion, that the set is closed under scalar multiplication, they are.

Clearly, if a set "contains the 0 vector" then it "is non-empty" so that way is trivial. If a set "is non-empty" then it contains some, possibly non-zero, vector v. If the set is also "closed under scalar multiplication", then the set also contains 0(v)= 0, the 0 vector.
 
  • #4
Thank you both.

Great explanations, all clear now ! :D
 

FAQ: What Is Required to Prove a Subset is a Vector Space?

What is a subspace of a vector space?

A subspace of a vector space is a subset of the vector space that satisfies the three conditions: it contains the zero vector, it is closed under vector addition, and it is closed under scalar multiplication.

How do you determine if a subset is a subspace of a vector space?

To determine if a subset is a subspace of a vector space, you need to check if it satisfies the three conditions mentioned above: it contains the zero vector, it is closed under vector addition, and it is closed under scalar multiplication. If all three conditions are met, then the subset is a subspace of the vector space.

What is the difference between a subspace and a vector space?

A vector space is a set of vectors that can be added and multiplied by scalars, while a subspace is a subset of a vector space that satisfies the three conditions mentioned above. A vector space can have multiple subspaces, but a subspace cannot have subspaces.

Can a subspace of a vector space contain vectors that are not in the original vector space?

No, a subspace of a vector space can only contain vectors that are in the original vector space. If a vector is not in the original vector space, it cannot satisfy the three conditions and therefore cannot be a part of the subspace.

What is the importance of understanding subspaces in linear algebra?

Understanding subspaces is important in linear algebra because it allows for the simplification and generalization of problems. It also helps in understanding the relationship between different vector spaces and their subspaces, and how they can be used to solve complex problems in mathematics, physics, and engineering.

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