Does Subset Inclusion Imply Span Inclusion in Vector Spaces?

In summary, the proof shows that if E is a subset of a vector space V, then the span of E, denoted as L(E), is also a subset of V. This is because any element x in L(E) can be written as a linear combination of elements in E, and since E is a subset of V, the elements in E also belong to V. Therefore, x must also belong to V, making L(E) a subset of V.
  • #1
TranscendArcu
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Homework Statement


Suppose V is a vector space. Let E,F be subsets of V. Show [itex]E \subseteq F \Leftrightarrow L(E) \subseteq L(F)[/itex]

The Attempt at a Solution


Let [itex] x \in L(E)[/itex], there are scalars [itex]q_i[/itex] such that [itex]x = \sum_{i} q_i p_i[/itex] where [itex]p_i \in E[/itex]. [itex]p_i \in F[/itex] because [itex]E \subseteq F[/itex]. Thus it is shown that [itex]x \in L(F)[/itex]. From this result, L(E) is a subset of L(F).

First of all, I'm not sure if this is a convincing proof or if I have notated it correctly. Second, I don't think I understand the proof very well. For example, why is it (if this proof is true) that [itex]p_i \in E[/itex]? Similarly, is [itex]q_i \in E[/itex]? If so, how is this known? Mostly, what I think I'd like to see is a less mathy and more wordy explanation of what's going on here.

Thanks!
 
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  • #2
TranscendArcu said:

Homework Statement


Suppose V is a vector space. Let E,F be subsets of V. Show [itex]E \subseteq F \Leftrightarrow L(E) \subseteq L(F)[/itex]
Other notation I've seen is Span(E). I believe this is what you mean when you write L(F), the set of all linear combinations of vectors in F.
TranscendArcu said:

The Attempt at a Solution


Let [itex] x \in L(E)[/itex], there are scalars [itex]q_i[/itex] such that [itex]x = \sum_{i} q_i p_i[/itex] where [itex]p_i \in E[/itex]. [itex]p_i \in F[/itex] because [itex]E \subseteq F[/itex]. Thus it is shown that [itex]x \in L(F)[/itex]. From this result, L(E) is a subset of L(F).
Your notation is a bit on the hard side to read, partly because there is a lot you're not saying. For example, I assume you mean that the pi's are a basis for E. Also, your notation makes it difficult to tell scalars from vectors. Making the vectors bold would help alleviate that difficulty.

I would also recommend using different letters for the vectors in the two sets. Instead of x and pi, I would use e as a vector in E, and e1, ..., en as basis vectors, and maybe c1, ..., cn for the scalars.
TranscendArcu said:
First of all, I'm not sure if this is a convincing proof or if I have notated it correctly. Second, I don't think I understand the proof very well. For example, why is it (if this proof is true) that [itex]p_i \in E[/itex]?
It's possible that pi does not belong to E, such as if E = {0}.
TranscendArcu said:
Similarly, is [itex]q_i \in E[/itex]?
No. The qi's are scalars, so they belong to some field, not to a vector space. That's what I meant about your notation being confusing - you have managed to confuse yourself.
TranscendArcu said:
If so, how is this known? Mostly, what I think I'd like to see is a less mathy and more wordy explanation of what's going on here.

Thanks!
Don't forget that this is an if and only if proof, so you need to go the other way, as well.
 
  • #3
Mark44 said:
I assume you mean that the pi's are a basis for E.

[itex] p_i [/itex] are not bases for [itex] E [/itex](they can be though) since [itex] E [/itex] is not a vector space, it's a SUBSET of [itex] V [/itex]. [itex] L(E) [/itex] it's a vector space on the other hand but the [itex] p_i [/itex] might be or might be not bases for [itex] L(E) [/itex]

For examples.

[itex] V = ℝ^2 [/itex]

[itex] E = \left\{ (1,0),(0,1),(1,1),(2,1) \right\} \subseteq V[/itex]

[itex] L(E) = ℝ^2 [/itex] but [itex] E [/itex] is not a basis for[itex] ℝ^2 [/itex] since [itex] E [/itex] is not linearly independent.

Why is it true that [itex] p_i \in E [/itex] ? Well that comes from the definition of span of a subset ( L(E ), which is:

[itex] L(E) [/itex] is the set containing all the linear combinations of the elements of E.
in formula [itex] L(E) = \left\{ r_1e_1 + r_2e_2 + ... + r_ie_i | e_i \in E , r_i \in R \right\} [/itex]

or more generally if you know what a field K is

[itex] L(E) = \left\{ r_1e_1 + r_2e_2 + ... + r_ie_i | e_i \in E , r_i \in K \right\} [/itex]

you can rewrite this in a more compact form, and using [itex] p_i [/itex] instead of [itex] e_i [/itex] and [itex] q_i [/itex] insted of [itex] r_i [/itex][itex] L(E) = \left\{ \sum q_ip_i| p_i \in E , q_i \in K \right\} [/itex]

so saying that [itex] x \in L(E) [/itex] means that there exist some [itex] p_i \in E, q_i \in K[/itex] for which [itex] x = \sum q_ip_i [/itex]. Does this answer your question?
 
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FAQ: Does Subset Inclusion Imply Span Inclusion in Vector Spaces?

What is a span in linear algebra?

A span in linear algebra is the set of all possible linear combinations of vectors in a vector space. It represents the space that can be created by combining the given vectors through multiplication by scalars and addition.

Why is the span important in linear algebra?

The span is important in linear algebra because it provides a way to determine the dimension of a vector space and to understand the relationships between vectors within that space. It also allows us to solve systems of equations and understand linear transformations.

How do you find the span of a set of vectors?

To find the span of a set of vectors, you can use the span formula, which involves determining the coefficients that will produce a linear combination of the vectors that equals the desired vector. You can also use Gaussian elimination to reduce the vectors to their row-echelon form and then determine the span from the pivot columns.

Can the span of a set of vectors be a line or a plane?

Yes, the span of a set of vectors can be a line or a plane. In general, the span of n vectors in n-dimensional space will create a hyperplane, but if the vectors are chosen carefully, they can span a lower-dimensional space such as a line or a plane.

How is the span related to linear independence?

The span and linear independence are closely related concepts. A set of vectors is linearly independent if none of the vectors can be written as a linear combination of the others. This means that the span of a set of linearly independent vectors will be the entire vector space. On the other hand, if the vectors are linearly dependent, the span will be a subspace of the vector space.

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