Finding an orthonormal basis

You're on the right track with your two vectors. After applying G-S, you should end up with two orthonormal vectors that span the subspace. Remember that to normalize a vector, you divide by its length (or magnitude).
  • #1
DWill
70
0

Homework Statement


Find an orthonormal basis for the subspace of R^3 consisting of all vectors (a, b, c) such that a + b + c = 0.


Homework Equations





The Attempt at a Solution


I know how to find an orthonormal basis just for R^3 by taking the standard basis vectors (1, 0, 0), (0, 1, 0), and (0, 0, 1) and applying the Gram-Schmidt process to make them orthonormal. The condition that a + b + c must equal 0 is throwing me off, however. Can anyone give me any suggestions for how to approach problems like these? thanks!
 
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  • #2
If you know Gram-Schmidt, that's the hard part. What's so hard about about finding two vectors (a,b,c) such that a+b+c=0 to do Gram-Schmidt with? (1,-1,0) sounds like a good start. Give me another one.
 
  • #3
I think I need 3 vectors, right? such that a + b + c = 0. Can I start with any vector? Or is there a reason why (1, -1, 0) is a good choice? If it can be any 3 vectors that add to 0 it should be simpler, but then I can't be sure they are a basis?
 
  • #4
Why do you need 3 vectors? That's a subspace of R^3. It looks to me like it's two dimensional.
 
  • #5
Ohh.. sorry I was being confused, I thought a, b, and c referred to vectors and not the components.

So I can choose (1, -1, 0) like you said and also (1, 0, -1)? I would still have to check if the 2 vectors I choose are actually a basis (span R^3 and are linearly independent) right?
 
  • #6
Sure, exactly. That's a good choice. There aren't too many dumb choices. (1,-1,0) and (-1,1,0) would have been a dumb choice because they're linearly dependent. But you didn't make the dumb choice. Now do Gram-Schmidt.
 
  • #7
I know how to find an orthonormal basis just for R^3 by taking the standard basis vectors (1, 0, 0), (0, 1, 0), and (0, 0, 1) and applying the Gram-Schmidt process to make them orthonormal.
If you apply Gram-Schmidt to these vectors, you'll just get the same ones. They are already orthogonal and have length 1.
 
  • #8
DWill said:
Ohh.. sorry I was being confused, I thought a, b, and c referred to vectors and not the components.

So I can choose (1, -1, 0) like you said and also (1, 0, -1)? I would still have to check if the 2 vectors I choose are actually a basis (span R^3 and are linearly independent) right?

I'm not sure it's clear to you where Dick got the vectors he did. Start with your equation:
a + b + c = 0

If you solve for a, you get
Code:
a = -b - c, where b and c are arbitrary
b =  b
c =      c

That last two equations are obviously true.
To make things even more explicit,
Code:
a = -1b - 1c
b =  1b + 0c
c =  0b  +1 c

or,
[tex]
\left[
\begin{array}{ c }
a \\
b \\
c
\end{array} \right] = b\left[
\begin{array}{ c }
-1 \\
1 \\
0
\end{array} \right] + c\left[
\begin{array}{ c }
-1 \\
0 \\
1
\end{array} \right][/tex]
Voila, there's your basis.
 
  • #9
DWill said:
Ohh.. sorry I was being confused, I thought a, b, and c referred to vectors and not the components.

So I can choose (1, -1, 0) like you said and also (1, 0, -1)? I would still have to check if the 2 vectors I choose are actually a basis (span R^3 and are linearly independent) right?

You need to review basic definitions. Those two vectors CAN'T be a basis for R^3 and can't span R^3 because that requires THREE vectors. You must have misread the original problem. As Dick said, the set of all (a, b, c) such that a+ b+ c= 0 is a two dimensional subspace of R^3. NO set of such vectors can be a basis for R^3. (1, -1, 0) and (1, 0, -1) form a basis for that two dimensional subspace.
 
  • #10
I'm currently doing the same problem so I figured I'd revive this old thread.

Once I have my two vectors,
u1=<-1,1,0> and u2=<-1,0,1>

I then go through the Gram-Schmidt process to find the normalized basis?
 
  • #11
If you already have a basis, you don't need Gram-Schmidt to find a normalized basis.

Do you know what the term "normalized" means?
 
  • #12
Mark44 said:
If you already have a basis, you don't need Gram-Schmidt to find a normalized basis.

Do you know what the term "normalized" means?

Sorry, I meant a orthonormal basis.
 
  • #13
OK, in that case you need to use G-S.
 

1. What is an orthonormal basis?

An orthonormal basis is a set of vectors in a vector space that are both orthogonal (perpendicular) to each other and have a length of 1.

2. Why is finding an orthonormal basis important?

Finding an orthonormal basis is important because it simplifies many mathematical calculations and makes it easier to work with vectors in a vector space.

3. How do you find an orthonormal basis?

To find an orthonormal basis, you can use the Gram-Schmidt process, which involves taking a set of linearly independent vectors and transforming them into a set of orthogonal vectors by subtracting their projections onto each other.

4. Can an orthonormal basis exist in any vector space?

No, an orthonormal basis can only exist in a vector space that has an inner product defined, such as Euclidean space.

5. What are some real-world applications of orthonormal bases?

Orthonormal bases have many applications in fields such as physics, engineering, and computer graphics. They are used to represent rotations and transformations, as well as in data compression and signal processing.

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