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

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In summary, this process gives an orthogonal basis for vectors that is different from the usual Gram-Schmidt process.
  • #1
Avatrin
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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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  • #2
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.
 
  • #3
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.
 

What is a Two Gram Schmidt process?

A Two Gram Schmidt process is a mathematical method used in linear algebra to orthonormalize a set of vectors in a vector space by creating a new set of vectors that are all perpendicular to each other.

Why is a Two Gram Schmidt process important?

A Two Gram Schmidt process is important because it allows for easier manipulation and calculation with vectors, making it a fundamental tool in many areas of mathematics and science, such as quantum mechanics and computer graphics.

How does a Two Gram Schmidt process work?

A Two Gram Schmidt process works by taking a set of linearly independent vectors and applying a series of orthogonalization and normalization steps to create a new set of orthonormal vectors. This process involves finding the projection of one vector onto another and subtracting it from the original vector to make it perpendicular to the other vector.

What are the applications of a Two Gram Schmidt process?

A Two Gram Schmidt process has many applications in mathematics and science, including solving systems of linear equations, performing least squares approximations, and creating orthonormal bases for vector spaces. It is also used in signal processing, data compression, and machine learning algorithms.

What are the limitations of a Two Gram Schmidt process?

A Two Gram Schmidt process can be computationally intensive and may introduce rounding errors, leading to a loss of orthogonality. It also cannot be applied to a set of linearly dependent vectors, and the resulting orthonormal vectors may not be unique. Additionally, the process may not work for complex vector spaces, as it relies on the concept of perpendicularity, which is not well-defined in higher dimensions.

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