Vector Spaces, Dimension of Subspace

In summary, the dimension of the subspace spanned by the vectors u, v, w is 2 for case i) and 3 for case ii). To determine linear independence, you can put the vectors as rows in a matrix and use row reduction or look for a linear combination that results in the zero vector.
  • #1
ashnicholls
50
0
Find the dimension of the subspace spanned by the vectors u, v, w in each of the following cases:
i) u = (1,-1,2)^T v = (0,-1,1)^T w = (3,-2, 5)^T
ii) u = (0,1,1)^T v = (1,0,1)^T w = (1,1,0)^T

Right, how do I go about this, do I have to find the subspace first then do the dimension.

Can someone give me sum clues.

Cheers
 
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  • #2
Posting it in linear algebra might be more appropriate.

Anyway, just put the vectors as rows in a matrix, and put it in row echelon form - that tells you the dimension and the space. Just like you ought to have been told in class.Of course, clearly those span 2 or 3 dimensional vector subspaces, so you only need to verify linear dependence/independence, which might be easier for you if you're not familiar with row echelon form. The last three are clearly linearly independent by inspection.
 
  • #3
Sorry, did not know where to put it, an I did a search on vectors, and it showed up in this section, that's why I did it.

We are not taught anything is class, we are just given work books, and then an assignment, this is one of the final questions in my work book.

And I can not remember row echelon form as such. So what I find whether they are linearly independant or not and then what?

Thanks for your help.
 
  • #4
I've moved this to the "Linear and Abstract Algebra" forum.

The dimension of a vector space is the number of vectors in a basis. A basis must both span the space and be independent. Since each of these vectors spaces is spanned by three vectors, the dimension cannot be more than 3. Now determine whether or not these vectors are independent. there are many ways to do that but one, as matt grime said, to construct a matrix having the vectors as rows. The number of independent vectors is the number of non-zero rows you have left after row reducing and therefore the dimension of the space. I strongly recommend you review "row reduction"!
 
  • #5
the only way to ,show the first three are dependent would be to kill off the first entry among the two that have a first entry, so the only possible way is to multiply the first one by -3.

adding that multiple to the last one then does it, since it gives a multiple of the second one.

so the dimensions are 2,3.
 

Related to Vector Spaces, Dimension of Subspace

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects, called vectors, that can be added and multiplied by scalars (such as real or complex numbers). The set of vectors must satisfy certain properties, such as closure under addition and scalar multiplication, in order to be considered a vector space.

2. What is the dimension of a vector space?

The dimension of a vector space is the number of vectors in a basis for that space. A basis is a set of linearly independent vectors that span the entire space. The dimension is a measure of the number of independent directions in the space.

3. How is the dimension of a subspace related to the dimension of its vector space?

The dimension of a subspace is always less than or equal to the dimension of its vector space. This is because a subspace is a subset of the larger vector space and therefore cannot have a larger number of independent directions.

4. How do you find the dimension of a subspace?

To find the dimension of a subspace, you can use the dimension theorem, which states that the dimension of a subspace is equal to the number of vectors in any basis for that subspace. Alternatively, you can use the rank-nullity theorem, which states that the dimension of a subspace is equal to the difference between the dimension of the entire space and the dimension of its null space.

5. Why is the dimension of a subspace important?

The dimension of a subspace is important because it provides information about the structure and properties of the subspace. It can also be used to determine whether a set of vectors is linearly independent or not, which is crucial in many applications of vector spaces in science and engineering.

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