Proof Linear Span Subsets: Proving L(S) is Smallest Subspace of V

In summary, the question asks to prove that if S is a subset of T and T is a subset of V and T is a subspace of V, then L(S) is a subspace of T. The proof provided only considers the definition of the span of S and does not use the fact that T is a subspace. It may be more substantial to also consider the subspace L(T) in the proof.
  • #1
Caroline Fields
1
0
This question asks me to prove a statement regarding linear spans. I have devised a proof but I am not sure if it is substantial enough to prove the statement.

1. Homework Statement

Let L(S) be the subspace spanned by a subset S of a linear space V. Prove that if S is a subset of T and T is a subset of V and if T is a subspace of V, then L(S) is a subspace of T. (This property is described by saying that L(S) is the smallest subspace of V which contains S).

Homework Equations


In part (a) of the question I proved that for S={u1, ..., un}, S is always a subset of L(S).

The Attempt at a Solution


From the definition of the span of S, we know that L(S) is the smallest subspace of V that contains S. Therefore, for any T that is a subset of V such that S is a subset of T, it is clear from the definition of span that L(S) is a subset of T.
Is this proof substantial enough?
 
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  • #2
Caroline Fields said:
This question asks me to prove a statement regarding linear spans. I have devised a proof but I am not sure if it is substantial enough to prove the statement.

1. Homework Statement

Let L(S) be the subspace spanned by a subset S of a linear space V. Prove that if S is a subset of T and T is a subset of V and if T is a subspace of V, then L(S) is a subspace of T. (This property is described by saying that L(S) is the smallest subspace of V which contains S).

Homework Equations


In part (a) of the question I proved that for S={u1, ..., un}, S is always a subset of L(S).

The Attempt at a Solution


From the definition of the span of S, we know that L(S) is the smallest subspace of V that contains S. Therefore, for any T that is a subset of V such that S is a subset of T, it is clear from the definition of span that L(S) is a subset of T.
Is this proof substantial enough?
No. Because you don't use the fact that T is a subspace.
You can use the fact that L(S) is the smallest subspace containing S or consider L(T).
 

What is a linear span?

A linear span, denoted by L(S), is the set of all possible linear combinations of vectors in a given set S. In other words, it is the space formed by taking all possible combinations of the vectors in S and adding them together using scalar multiplication and vector addition.

How do you prove that L(S) is the smallest subspace of V?

To prove that L(S) is the smallest subspace of V, you must show that it satisfies two properties: closure under addition and closure under scalar multiplication. This means that any combination of vectors in S must result in another vector in L(S), and any scalar multiple of a vector in L(S) must also be in L(S). This can be done by using the definition of a subspace and the fact that L(S) is the smallest possible subspace containing S.

What is the significance of proving that L(S) is the smallest subspace of V?

Proving that L(S) is the smallest subspace of V is important because it allows us to understand the full extent of the vector space V. It also helps us to identify the basis of V, which is a set of linearly independent vectors that span the entire space. This is useful in many areas of mathematics and science, including linear algebra, physics, and computer science.

Can L(S) ever be larger than V?

No, L(S) cannot be larger than V. This is because L(S) is formed by taking combinations of vectors in a given set S, which must be a subset of V. Therefore, L(S) can never contain more vectors than V and will always be a subspace of V.

How can we apply the concept of linear spans to real-world problems?

The concept of linear spans can be applied to many real-world problems, such as finding the best fit line for a set of data points, calculating the range of a projectile's motion, and solving systems of linear equations in physics and engineering. It is also used in data analysis, machine learning, and optimization problems in various industries.

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