Linearly Independent Sets and Bases

In summary: So if we take any vector in V and write it as a linear combination of elements in B, we’ve already used up all the vectors in B, right? Yes, this is correct.
  • #1
pollytree
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0

Homework Statement



V is a subspace of Rn and S={v1,...,vk} is a set of linearly independent vector in V. I have to prove that any list of linearly independent vectors can be extended to a basis for V.

Homework Equations



None that I can think of.

The Attempt at a Solution



So to be a basis, the vectors must be linearly independent (which is given) and span V. I think we also have to show that the number of elements in the basis is equal to the dimension of V. We have a hint that says we can prove the statement above by adding vectors which are not in span S one at a time until we span all of V and arguing that the result is linearly independent.

But I'm not really sure where to begin on this question. Any help would be great! Thanks :D
 
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  • #2
Let {e1,...,en} be a standard basis. Then set {e1,...,en, v1} is linearly dependent, so
[tex]\sum \alpha_i e_i+\beta v_1=0[/tex]
Cetrainly [tex]\beta\neq 0[/tex], and [tex]\alpha_i\neq 0[/tex] for some [tex]i[/tex]. Show that {e1,...,ei-1, ei+1,..., en, v1} is a base. Applying repeatedly this procedure, we may "inject" all the v's into the basis. Show that in each step we can discard one of the e's (and not the previously added v's), that's where linear independence is needed.
Good luck.
 
  • #3
I would show that any S could be extended to be a basis for V, then show that a basis for V can be extended to be a basis for Rn.

Let B be a basis for S, what would the minimal subset of B look like that spans V? Ask your self a similar question to go from V to Rn
 
  • #4
JonF said:
I would show that any S could be extended to be a basis for V, then show that a basis for V can be extended to be a basis for Rn.
Sorry, but this makes no sense to me. The problem was to show that any such S could be extended to a basis for V and there is no mention of Rn.

Let B be a basis for S, what would the minimal subset of B look like that spans V?
This also makes no sense. S is a set of vectors, not a vector space and so has no "basis". Even if S were a subspace of V, any basis for V would have to be a superset of a basis for S, not a subset.

Ask your self a similar question to go from V to Rn
Unnecessary- the problem says nothing about Rn.
 
  • #5
Sorry I miss read the problem, I thought it was to prove that S could be extended to be a basis for Rn. But as you stated my first step is his actual question.

Here is what I was thinking, if it's wrong please let me know!

V has a basis since it’s a subspace, and this means every vector in V can be expressed as a linear combination of the elements of that basis.

Let B be the minimal set of basis vectors of V needed to compose a linear combination of all vectors in S (we know this set exist since S is a subset of vector space V, and since V is a subspace of Rn, it's basis has to be countable).

The basis of V - B union S should be a basis for V right?
 
Last edited:

What is the definition of a linearly independent set?

A linearly independent set is a set of vectors that cannot be written as a linear combination of other vectors in the set. This means that none of the vectors in the set are redundant or unnecessary, and each vector contributes unique information to the set.

How do you determine if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the determinant method. This involves creating a matrix with the vectors as columns, and then finding the determinant of the matrix. If the determinant is non-zero, then the set is linearly independent. Another method is to solve the system of equations formed by setting the linear combination of the vectors equal to zero. If the only solution is the trivial solution (all coefficients equal to zero), then the set is linearly independent.

What is the difference between a linearly independent set and a basis?

A basis is a linearly independent set that spans the entire vector space. This means that any vector in the vector space can be written as a linear combination of the basis vectors. A linearly independent set, on the other hand, may not span the entire vector space but still has the property that none of the vectors can be written as a linear combination of the others.

Can a set with more than n vectors be linearly independent in an n-dimensional vector space?

No, a set with more than n vectors cannot be linearly independent in an n-dimensional vector space. This is because there are only n dimensions in the vector space, so any set with more than n vectors will have at least one redundant vector, making it linearly dependent.

Can a linearly dependent set be a basis?

No, a linearly dependent set cannot be a basis. This is because a basis must be linearly independent, and a linearly dependent set, by definition, has at least one redundant vector which makes it not linearly independent.

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