Is the other Gram-Schmidt process truly different from the usual one?

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SUMMARY

The discussion centers on the differences between the traditional Gram-Schmidt process and an alternative version presented in "Matrices and Linear Transformations" by Charles Cullen. The alternative method does not include the normalization factor in the projection formula, which raises questions about whether it produces an orthogonal basis. Participants clarify that the alternative process can yield an orthonormal basis only if the basis vectors are normalized after the initial orthogonalization steps. The algebraic approach to the alternative process is emphasized as being clearer than visual explanations.

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Hi

I already know the "usual" Gram-Schmidt Process:

uk = vk - Ʃj=1k-1projujvk

But, then I discovered another Gram-Schmidt process that gives an orthogonal basis (in Matrices and Linear transformations by Charles Cullen):

uk = vk - Ʃj=1k-1(vk*uj)uj

Essentially, it is the same except you do not divide by <uj,uj>. Cullen calls this the Gram-Schmidt process, yet it is different. Does it give a different basis?

The explanations for the usual Gram-Schmidt process are usually visual while the explanation for this one was algebraic. I cannot make sense of it in any of the visual explanations of the Gram-Schmidt process, yet it makes perfect sense algebraically;

Starting with a basis {u1,u2...,uk-1} and a vector vk that is not in its span to create a vector uk that is orthogonal to all ui:

uk*ui = vk*ui + uiƩj=1k-1ajuj = vk*ui + ai = 0

Thus, ai = -vk*ui for uk to be orthogonal to all ui. This gives us what I wrote above.

How are these processes different? Or, if they aren't, why?
 
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Avatrin said:
Hi

I already know the "usual" Gram-Schmidt Process:



But, then I discovered another Gram-Schmidt process that gives an orthogonal basis (in Matrices and Linear transformations by Charles Cullen):



Essentially, it is the same except you do not divide by <uj,uj>. Cullen calls this the Gram-Schmidt process, yet it is different. Does it give a different basis?

That definition does not give an orthogonal basis unless ##\langle u_j,u_j\rangle =1 ## for ##j=1,\ldots k-1##. I am not familiar with the text that you cite, but it is probably the case that there is an additional steps where we normalize the basis vectors. For example we can start with

$$u'_1 = v_1 , ~~~~ u_1 = \frac{u'_1 }{||u_1'||}.$$

Then we can construct

$$ u'_2 = v_2 - \langle v_2,u_1 \rangle u_1$$

which is orthogonal to ##u_1##, since

$$ \langle u_1, u'_2 \rangle = \langle u_1,v_2 \rangle - \langle v_2,u_1 \rangle \langle u_1, u_1\rangle =0.$$

We will have to similarly normalize ##u_2 = u_1'/||u_2'||## if we want to construct ##u_3## with a similar formula that omits the factor of ##\langle u_j,u_j\rangle##.

The algebraic computation seems clear and also indicates precisely why we need the factor of ##\langle u_j,u_j\rangle## in the formula for the projection operator.
 
I had to reread the text and redo the computations to see that, but yes, it does indeed have to be an orthonormal basis. I just didn't pick up the part that said orthonormal instead of orthogonal.
 

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