Linear Algebra: Spans and Dimensions

In summary, the problem is asking to show that the dimensions of W and U are either equal or differ by 1. By reducing the generating sets for both U and W to bases, we can consider two cases: v is in U or v is not in U. If v is in U, the generating set for W is linearly dependent and removing v results in a linearly independent set, therefore dim W = dim U. If v is not in U, the generating set for W is already linearly independent and adding v increases the dimension by 1, making dim W = 1 + dim U.
  • #1
Millacol88
86
0

Homework Statement


Given v1, v2 ... vk and v, let U = span {v1, v2 ... vk} and W = span {v1, v2 ... vk, v}. Show that either dim W = dim U or dim W = 1 + dim U.

The Attempt at a Solution


I'm not really sure where to start. If I knew that {v1, v2 ... vk} was linearly independent, then it would be easy to prove. But I'm not given anything about linear independence so I'm at a loss.
 
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  • #2
Millacol88 said:

Homework Statement


Given v1, v2 ... vk and v, let U = span {v1, v2 ... vk} and W = span {v1, v2 ... vk, v}. Show that either dim W = dim U or dim W = 1 + dim U.

The Attempt at a Solution


I'm not really sure where to start. If I knew that {v1, v2 ... vk} was linearly independent, then it would be easy to prove. But I'm not given anything about linear independence so I'm at a loss.

What if you reduce ##\{v_1,v_2,...,v_k\}## to a basis set for U to work with?
 
  • #3
Okay, so if I reduced the generating set for U to a basis, I would know its linearly independent, but then where would I go with it?
 
  • #4
Millacol88 said:
Okay, so if I reduced the generating set for U to a basis, I would know its linearly independent, but then where would I go with it?

Do the same for W. Note, W is also spanned by v which may or may not be represented as a linear combination of the other vectors (this is why there are two possible cases for the dimensions).
 
  • #5
Consider two cases:
1) v is in U.
2) v is not in U.
 
  • #6
Ok, so assuming we reduce the set in U to a basis: then if v is in U the given generating set for W is linearly dependent. Then v would be removed from this set, making it linearly independent. This implies that U and V share a basis, and thus their dimensions are the same. If v is not in U, then the given generating set is linearly independent, and thus is a basis for W. Therefore dim W = dim U + 1. Does this make sense?
 
  • #7
I would think it was obvious that if v is in U then adding v to U doesn't change the span: span(U)= span(V) so their dimensions are the same. It does NOT follow that "If v is not in U, then the given generating set is linearly independent" but it is not necessary. What ever the dimension of U, if v is not in U, adding it increases the dimension by 1.
 

1. What are spans in linear algebra?

Spans in linear algebra refer to the set of all possible linear combinations of a given set of vectors. In other words, a span is the set of all possible solutions to a linear combination equation.

2. How do you determine the dimension of a vector space?

The dimension of a vector space is determined by the number of linearly independent vectors that span the space. This means that the dimension is equal to the minimum number of vectors needed to create any vector in the space.

3. What is the difference between a basis and a spanning set?

A basis is a set of linearly independent vectors that span the entire vector space. A spanning set, on the other hand, may contain linearly dependent vectors and may not be the minimum number of vectors required to span the space.

4. Can a vector be in more than one span?

Yes, a vector can be in more than one span. This is because a span is the set of all possible linear combinations of a given set of vectors, so a single vector can be created by different combinations of those vectors.

5. How does the concept of spans and dimensions relate to solving systems of linear equations?

The concept of spans and dimensions is closely related to solving systems of linear equations because the solutions to a system of equations form a vector space. The dimension of this vector space is equal to the number of variables in the system, and the solutions are the vectors that span this space.

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