Really quick question on linear spans

  • Thread starter NeonVomitt
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In summary, to determine if the span of two sets of vectors is equal, you can find their reduced row echelon form and see if they match. If they do, it means that the two sets of vectors have the same span.
  • #1
NeonVomitt
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If I want to find if

span ([4, 0, -3], [2,2,1]) = span ([2,-2,-4], [0,1,5]) do I first find their reduced row echelon form, and then see if they match? For instance, if I found both matrices to reduce to:
[ 1 0]
[ 0 1]
[ 0 0]

does that mean that they equal each other? Or do I have to do something else?

Also what is the vector space? Is it R2 in this case?
 
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  • #2
Essentially, if two matrices are equivalent, then augmenting them with the same vector will give the same solution set. Another way of saying this is that if A ~ B, then in Ax = b and Bx = b, where b can be anything, if x satisfies the first equality, then it satisfies the second, and vice-versa. Also note that when there are no solutions in one, there are no solutions in the other.

If the rref of A is C and the rref of B is also C, then A ~ C and B ~ C. So we have that Ax = b, Cx = b, Bx = b, have the same solutions for x given any vector b (again if there are no solutions for particular b, none of the equations will have a solution). More elaborately, equivalence is transitive and reflexive, so A ~ C and B ~ C implies A ~ B.

That tells us that a vector b that has a solution in Ax = b, also has a solution in Bx = b. And vice-versa. Finding a linear combination of a set {v1, v2, ..., vn} of vectors equal to a vector b, amounts to solving [v1 v2 v3 ... vn]x = b. If {w1, w2, ..., wn} is another set of vectors, we would solve [w1 w2 ... wn]x = b. If [v1 v2 v3 ... vn] ~ C and [w1 w2 ... wn] ~ C then, by the above, one matrix has a solution when the other does. This means when there's a linear combination in {v1, v2, ..., vn} equal to a vector b, there's also one in {w1, w2, ... , wn} and vice-versa. This shows you that {v1, v2, ..., vn} and {w1, w2, ... , wn} have the same span.
 
  • #3
NeonVomitt said:
If I want to find if

span ([4, 0, -3], [2,2,1]) = span ([2,-2,-4], [0,1,5]) do I first find their reduced row echelon form, and then see if they match? For instance, if I found both matrices to reduce to:
[ 1 0]
[ 0 1]
[ 0 0]

does that mean that they equal each other? Or do I have to do something else?

Remember that row operations transform the row vectors to an equivalent basis, so you look at the rref of

[4, 0, -3]
[2, 2, 1]

etc.
 

1. What is a linear span?

A linear span is the set of all possible linear combinations of a given set of vectors. It represents the space that can be spanned or covered by the given set of vectors.

2. How is linear span related to linear independence?

Linear span and linear independence are closely related concepts. A set of vectors is linearly independent if none of the vectors in the set can be expressed as a linear combination of the other vectors. In other words, the vectors in a linearly independent set span a larger space than the vectors in a linearly dependent set.

3. Can a set of vectors have more than one linear span?

Yes, a set of vectors can have multiple linear spans. This can happen when the set of vectors contains redundant or linearly dependent vectors. In this case, the same space can be spanned by a smaller subset of the vectors.

4. How can linear span be used in practical applications?

Linear span is a fundamental concept in linear algebra and is used in many practical applications. It is used in data analysis, signal processing, and machine learning, to name a few. For example, in machine learning, linear span can be used to determine the dimensionality of a dataset and to identify linearly independent features.

5. Can the dimension of a linear span be larger than the number of vectors in the set?

Yes, the dimension of a linear span can be larger than the number of vectors in the set. This can happen when the vectors in the set are not linearly independent, and therefore, the space spanned by the vectors is not fully utilized. In this case, the dimension of the linear span is equal to the number of linearly independent vectors in the set.

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