What is the orthonormal basis for P2(R) using Gram-Schmidt and T*?

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In summary, the author is trying to find the inverse of a matrix T using Gram-Schmidt orthonormalization, but is having trouble with the calculation.
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Homework Statement



Find an orthonormal basis for P2(R) using the Gram-Schmidt orthogonalization process, with the inner product defined by <f,g> = integral f(t)g(t) dt from 0 to 1. Then, if T(f) = f '' (1) + x*f (0), find T*(f).

Homework Equations



Given a basis a = {w1, w2, ... , wn}, we compute the orthonormal basis B = {v1, v2,...,vn} by Gram-Schmidt:

v1=w1

v2= w2 - (<w2, v1>/(<v1,v1>^2))*v1

v3 = w3 - (<w3, v1>/(<v1,v1>^2))*v1 - <w3, v2>/(<v2,v2>^2))*v2

The Attempt at a Solution



I just need someone to verify this and tell me if I'm right. I'm a bit confused at how this is so much more complex then the case when the integral is from -1 to 1 (that will just give you the Legendre polynomials and I'm able to compute them just fine). When I used the standard ordered basis {1,x,x^2} with Gram Schmidt, I got this:

B = {1, (3^1/2)*2*(x - 1/2), (12/1009)(x^2 - (9/4)x - 1/3)*(5045)^1/2}

which is REALLY ugly. :eek: I don't think this works, as I keep trying to take the inner product of v1 and v2, but I don't get zero... but I also don't see any mistakes in my work, so I don't know.

If I could just get this orthonormal basis, then I know I just need to get the matrix representation of T which would be really easy, and then take its transpose. From that point, I'm not sure how to get from the matrix [T*]B back into an expression T*(f).

Please help! Even if someone just knows what the correct orthonormal basis for this inner product is.
 
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  • #2
Your version of Gram-Schmidt doesn't look quite like I know it. Don't you subtract all of the projections of the previous vectors and then normalize the result (as in the wikipedia exposition)? The first and second vectors in your results look fine, but the third one is way off.
 
Last edited:
  • #3
aha, I see now, I was squaring a norm when I shouldn't have been... it seems to work fine now! Finally... thanks!
 
  • #4
As for finding T in matrix form instead of finding an orthonormal basis you could always just see how T acts on ax^2 +bx + c and then correlate that with a 3-tuple.

Then you just easily extrapolate a 3x3 matrix wrt the standard basis on R_3 (Which is orthonormal anyways).
 

1. What is the purpose of the Gram-Schmidt process?

The Gram-Schmidt process is used to transform a linearly independent set of vectors into an orthogonal set. This is useful in many areas of mathematics and engineering, including solving systems of linear equations and performing least squares approximations.

2. How does the Gram-Schmidt process work?

The process involves taking the first vector in the set and normalizing it to have a magnitude of 1. Then, each subsequent vector is projected onto the orthogonal complement of the previous vectors to make it orthogonal to all previous vectors. This process is repeated until all vectors have been transformed into an orthogonal set.

3. Can the Gram-Schmidt process be used for any set of vectors?

No, the Gram-Schmidt process can only be used for linearly independent sets of vectors. If the set is linearly dependent, the process will fail as it requires the vectors to be independent for the projection step to work.

4. What is the relationship between the Gram-Schmidt process and the T* transformation?

The T* transformation is a specific type of Gram-Schmidt process that also normalizes the vectors to be unit vectors. It is commonly used in linear algebra to transform a given set of vectors into an orthonormal basis, which is a set of orthogonal unit vectors. This makes it easier to perform calculations and solve problems involving these vectors.

5. Are there any limitations to using the Gram-Schmidt process?

One limitation is that the Gram-Schmidt process can sometimes introduce rounding errors, which can affect the accuracy of the results. Additionally, the process can become computationally intensive for large sets of vectors, so other methods may be preferred in those cases.

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