Gram-schmidts orthonormalization

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In summary, to find an orthonormal basis for span(S) in the inner product space V where S={(1,i,0), (1-i,2,4i)} and x=(3+i,4i,-4), we can apply the Gram-Schmidt process to obtain an orthogonal basis and then normalize the vectors in this basis to obtain an orthonormal basis. The length of a vector in the complex space is calculated using the complex inner product, which takes into account the complex conjugate. To find the length of a complex vector, we can take the square root of the dot product of the vector with its complex conjugate.
  • #1
jbear12
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Apply Gram-Schmidts process to the sebust S of the inner product space V to obtain an orthogonal basis for span(S). Then normalize the vectors in this basis to obtain an orthognormal basis for span(S)

V=span(S) where S={(1,i,0), (1-i,2,4i)} and x=(3+i,4i,-4).

Isn't the length of (1,i,0) zero? I'm confused about finding orthonormal basis for a complex set. Can anyone solve this problem step by step for me?
Thank you very much!
 
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  • #2
jbear12 said:
Apply Gram-Schmidts process to the sebust S of the inner product space V to obtain an orthogonal basis for span(S). Then normalize the vectors in this basis to obtain an orthognormal basis for span(S)

V=span(S) where S={(1,i,0), (1-i,2,4i)} and x=(3+i,4i,-4).

Isn't the length of (1,i,0) zero? I'm confused about finding orthonormal basis for a complex set. Can anyone solve this problem step by step for me?
Thank you very much!
I won;t solve the problem for you, but am happy to have a look at your work

the length of a= (1,i,0) is not zero, it is usually given by the complex innner product, which in matrix notation, if a is a complex column vector this becomes
[tex] ||a||^2 = <a,a> = (a^*)^Ta[/[/tex]
where * denotes the complex conjugate

so just like a dot product, but with the complex conjugate of itself
 
  • #3
Thank you lanedance. I realized it after some searching on the internet. :)
I have another dumb question...:P
What's the length of (1-5i/2, 7-i/2,4i)? Is it the square root of (1-5i/2)squared+(7-i/2)squared+(4i)squared, where i2=-1?
 
  • #4
the length squred will be the following dot product
[tex] (1-5i/2, 7-i/2,4i)* \bullet (1-5i/2, 7-i/2,4i) [/tex]

so taking the congujate
[tex]= (1+5i/2, 7+i/2,-4i) \bullet (1-5i/2, 7-i/2,4i) [/tex]

then the multiply out as a normal dot product

if you're still confused, start with: what is the magnitude of the 1+5i/2 in the 1D complex space?
 
Last edited:
  • #5
I get it.
Thank you very much, lanedance. :)
 

1. What is Gram-schmidt's orthonormalization?

Gram-schmidt's orthonormalization is a mathematical process used to convert a set of linearly independent vectors into a set of orthonormal vectors. This process is often used in linear algebra and signal processing to simplify calculations and improve accuracy.

2. Why is Gram-schmidt's orthonormalization important?

Gram-schmidt's orthonormalization is important because it allows us to work with orthonormal bases, which have many useful properties. For example, orthonormal bases make it easier to calculate projections and determine distances between vectors.

3. How does Gram-schmidt's orthonormalization work?

The process of Gram-schmidt's orthonormalization involves taking a set of linearly independent vectors and applying a series of orthogonalization and normalization steps. This results in a set of vectors that are not only orthogonal to each other, but also have a length of 1 (making them orthonormal).

4. What are the benefits of using Gram-schmidt's orthonormalization?

There are several benefits of using Gram-schmidt's orthonormalization, including simplifying calculations, improving accuracy, and making it easier to work with vectors. Additionally, orthonormal bases have many useful properties that can be utilized in various mathematical and scientific applications.

5. Are there any limitations to Gram-schmidt's orthonormalization?

One limitation of Gram-schmidt's orthonormalization is that it only works for finite-dimensional vector spaces. Additionally, it can be computationally expensive for large sets of vectors. In some cases, other methods such as Householder reflections may be more efficient.

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