Finding Orthonormal Set q1, q2, q3 for Column Space of A

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SUMMARY

The discussion centers on finding an orthonormal set q1, q2, q3 that spans the column space of matrix A, defined as A = [1 1; 2 -1; -2 4]. The Gram-Schmidt method is recommended for this purpose, despite initial confusion regarding the independence of the column vectors. It is confirmed that the two columns of A are independent, and the application of Gaussian elimination can help in identifying independent columns. Additionally, the vector cross product is suggested as a useful technique for constructing an orthonormal set in R3.

PREREQUISITES
  • Understanding of Gram-Schmidt orthogonalization
  • Familiarity with Gaussian elimination
  • Knowledge of vector independence and orthogonality
  • Basic concepts of linear algebra
NEXT STEPS
  • Study the Gram-Schmidt process in detail
  • Learn about Gaussian elimination techniques
  • Explore the properties of vector independence and orthogonality
  • Investigate the vector cross product and its applications in R3
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Students and professionals in mathematics, particularly those studying linear algebra, as well as educators teaching concepts related to vector spaces and orthonormal bases.

EvLer
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I just need a hint.
Problem:
find an orthonormal set q1, q2, q3 for which q1, q2 span the column space of A, where
A =
[1 1]
[2 -1]
[-2 4]

of course I should apply the Gram-Schmidt method, but the problem is that the column vectors are not independent and Gram-Schmidt starts with independent vectors. I can arrive at independent columns by Gaussian elimination that would span the subspace of the column space, so is that what I should use for finding orthonormal qs?
Thanks.
 
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The two columns look independent to me.
 
but if I take inner product i don't end up with 0...
 
EvLer said:
but if I take inner product i don't end up with 0...
Then they aren't orthogonal.
 
EvLer said:
but if I take inner product i don't end up with 0...

Of course they aren't! You are asked to find an orthonormal basis. You would hardly expect them to give you vectors that are already orthogonal!

The two vectors you are given definitely are independent- the only way two vectors can be dependent is if one is a multiple of the other and that clearly is not the case here.

Apply "Gram-Schmidt" to them!
 
Since A is 2x3, you can also use a trick that works in R3 only. The vector cross product is very useful for constructing an orthonormal set.
 
oh, ok, I know where I got confused: othogonal is always independent, independent may not be orthogonal... thanks
 

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