Calculate dim(U) and company - linear algebra

In summary, the student is seeking help with a question involving calculating the dimension and finding a base for a given subspace. They have attempted to solve the problem using row reduction but have made a mistake in their calculations. The correct approach is to write the given vectors as rows and then row-reduce to find the basis vectors. The student is advised to try again and pay attention to signs in their calculations.
  • #1
inner08
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0

Homework Statement


Before I start, I should mention that I'm taking linear algebra in French so I hope I translated all the terms correctly. I'm studying for a test and here is a question I'm having a little trouble with...hope you can help!

U = span{[1 1 1 1]T, [-2 -2 1 1]T, [3 3 3 1]T, [0 0 1 0]T}

a) Calculate dim(U) and give a base of U.
b) Complete the base found in a) to obtain a base of R4.

Homework Equations





The Attempt at a Solution



a) I get a base of (-5/6, -1/3, -1/2, 1) so dim(U) has dim(U) = 1

b) This is where I don't know what to do. How to you complete a base? I don't need the answer, I just want to know how to complete a base. The only thought I had was to use the identity matrix but then again, I really don't know what to do or how to approach this. Any help is appreciated!
 
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  • #2
You seem to be misunderstanding the question since your answer to a) is not at all right. What you want to do is take the 4 vectors in your set and find a minimal set of linearly independent vectors with the same span. The usual technique to do this is called 'row reduction' (sorry I don't know the french!).
 
  • #3
The dimension is determined by the smallest amount of linearly independent vectors that you can find, which describe entirely U. To do so, reduction is the way out.

marlon
 
  • #4
hmm I thought that's what I did...

I reduced the matrix from:
1 -2 3 0
1 -2 3 0
1 1 3 1
1 1 1 0

to get..

1 0 0 5/6
0 1 0 1/3
0 0 1 1/2
0 0 0 0 <--- isn't this the "row reduction" you were talking about?
 
  • #5
That's sort of it. Though I usually write the vectors as rows. Since you've written them as columns and then (I think) row reduced, it's a little hard for me to interpret the results exactly - but I can clearly see that U has dimension 3. (How many obvious independent columns in your set?). Try the same exercise writing the vectors as rows.
 
  • #6
Yes, that is the "row reduction" but that does NOT mean that the last column gives you the only basis vector. In fact, since you have 3 non-zero rows left, that tells you that the dimension of this subspace is 3.

Use the given vectors as rows, not columns, and row-reduce. The remaining non-zero rows will be the basis vectors.
 
  • #7
The "row reduction" result doesn't look correct, unless I missed something. Go through it again.

Edit: actually, it's almost correct, you missed a sign (figure out which one!) So, you have shown that the vector (0, 0, 1, 0) can be represented as a linear combination of the other vectors, so the set without the vector is a span too, and linearly independent, which makes it a basis for U.
 
Last edited:

1. What is the purpose of calculating dim(U) in linear algebra?

The dimension of a vector space, denoted as dim(U), is the number of linearly independent vectors that span the vector space. In linear algebra, calculating dim(U) helps us understand the size and structure of a vector space, which is essential in solving systems of linear equations.

2. How do you calculate dim(U) for a given vector space?

To calculate dim(U), you can use the row-reduced echelon form of the matrix representing the vector space. The number of non-zero rows in the row-reduced echelon form is equal to the dimension of the vector space.

3. Can dim(U) be greater than the number of vectors in the vector space?

No, the dimension of a vector space cannot be greater than the number of vectors in the vector space. This is because the number of linearly independent vectors that can span a vector space is limited by the number of vectors in the space.

4. How does calculating dim(U) help in solving systems of linear equations?

Calculating dim(U) helps in solving systems of linear equations by providing information about the number of independent equations needed to solve the system. If the dimension of the vector space is equal to the number of variables in the system, then there is a unique solution to the system of equations.

5. Are there any real-life applications of calculating dim(U) in linear algebra?

Yes, calculating dim(U) has various real-life applications, such as in engineering, physics, and economics. It is used in analyzing and solving systems of linear equations in these fields, which are essential in understanding and predicting real-world phenomena.

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