Gram-Schmidt for 1, x, x^2 Must find orthonormal basis

In summary, the problem was to find an orthonormal basis for the functions 1, x, and x^2 from -1 to 1 using the Gram-Schmidt equations. The attempt at a solution involved using the Gram-Schmidt process, but the final answer needed to be normalized again to obtain the correct result.
  • #1
shreddinglicks
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Homework Statement


Find orthonormal basis for 1, x, x^2 from -1 to 1.

Homework Equations


Gram-Schmidt equations

The Attempt at a Solution


I did the problem. My attempt is attached. Can someone review and explain where I went wrong? It would be much appreciated.
 

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  • #2
It looks like it might be mostly correct, but you got a little sloppy at the end, and your answer for ## v_3 ## should be of the form ## v_3= Ax^2+Bx+C ## where ## A , B ##, and ## C ## are also closed form expressions, and not a long decimal number. ## \\ ## Edit: And the denominator is unnecessary on the last two terms of ## v_3 ## because the functions ## v_1 ## and ## v_2 ## are normalized. ## \\ ## Additional edit: I think you got it right, but the last term you need to leave it as ## C=-\frac{\sqrt{10}}{6} ##, instead of -.52704.. which I'm sure started out as ##-\frac{\sqrt{10}}{6} ##. ## \\ ## Additional edit: I think it is necessary to normalize this final ## v_3 ## one more time. Also, I think it is not necessary to normalize the vector you are working with before you subtract out the other projections of the previous unit vectors. It makes for an extra step.
 
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  • #3
shreddinglicks said:

Homework Statement


Find orthonormal basis for 1, x, x^2 from -1 to 1.

Homework Equations


Gram-Schmidt equations

The Attempt at a Solution


I did the problem. My attempt is attached. Can someone review and explain where I went wrong? It would be much appreciated.

Most helpers will skip your question and not even attempt to read your posted image. If you are serious about wanting help you ought to type out your work--not nearly as horrible a procedure as you might think. See the Guidelines for more information on this issue.
 
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  • #4
Charles Link said:
It looks like it might be mostly correct, but you got a little sloppy at the end, and your answer for ## v_3 ## should be of the form ## v_3= Ax^2+Bx+C ## where ## A , B ##, and ## C ## are also closed form expressions, and not a long decimal number. ## \\ ## Edit: And the denominator is unnecessary on the last two terms of ## v_3 ## because the functions ## v_1 ## and ## v_2 ## are normalized. ## \\ ## Additional edit: I think you got it right, but the last term you need to leave it as ## C=-\frac{\sqrt{10}}{6} ##, instead of -.52704.. which I'm sure started out as ##-\frac{\sqrt{10}}{6} ##. ## \\ ## Additional edit: I think it is necessary to normalize this final ## v_3 ## one more time. Also, I think it is not necessary to normalize the vector you are working with before you subtract out the other projections of the previous unit vectors. It makes for an extra step.

I went through the problem again. It seems if I normalize it after I use the Gram-Schmidt process I get the correct answer. I'm not sure why though.
upload_2018-8-31_21-34-14.png
 

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  • #5
This way is quicker. In your first paper, you still need to normalize your answer of ## v_3=\frac{\sqrt{10}}{2}x^2-\frac{\sqrt{10}}{6} ##. If you work with that, it gives you a denominator of 2/3 as what will make it a unit vector, so that the answer for ## v_3 ## in your first paper needs to be multiplied by 3/2 to make it a unit vector. It then gives the same correct answer as your second paper.
 
  • #6
Charles Link said:
This way is quicker. In your first paper, you still need to normalize your answer of ## v_3=\frac{\sqrt{10}}{2}x^2-\frac{\sqrt{10}}{6} ##. If you work with that, it gives you a denominator of 2/3 as what will make it a unit vector, so that the answer in your first paper needs to be multiplied by 3/2. It then gives the same correct answer as your second paper.

I see what you mean. Thanks a bunch!
 
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1. What is the Gram-Schmidt process?

The Gram-Schmidt process is a mathematical procedure used to find an orthonormal basis for a set of vectors in a vector space. It involves a series of orthogonalization and normalization steps to generate a set of vectors that are mutually perpendicular and have a length of 1.

2. Why is the Gram-Schmidt process important?

The Gram-Schmidt process is important because it allows us to find an orthonormal basis for a given set of vectors. This can be useful in many applications, such as solving systems of linear equations, finding eigenvalues and eigenvectors, and performing orthogonal projections.

3. What is an orthonormal basis?

An orthonormal basis is a set of vectors that are mutually perpendicular and have a length of 1. This means that the vectors are linearly independent and can be used to span the entire vector space. This is useful in applications where we need to find a unique representation of a vector in terms of a set of basis vectors.

4. How is the Gram-Schmidt process applied to 1, x, and x^2?

The Gram-Schmidt process for 1, x, and x^2 involves finding an orthonormal basis for the vector space spanned by these three vectors. This is done by first orthogonalizing the vectors by subtracting the projection of each vector onto the previous ones. Then, the resulting orthogonal vectors are normalized to have a length of 1, creating an orthonormal basis.

5. Can the Gram-Schmidt process be applied to any set of vectors?

Yes, the Gram-Schmidt process can be applied to any set of vectors in a vector space. However, it is important to note that the resulting orthonormal basis may not be unique, as it depends on the order in which the vectors are orthogonalized. Additionally, the Gram-Schmidt process may not work for some special cases, such as when the vectors are linearly dependent.

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