Norms and orthogonal Polynomials

In summary, the author is trying to solve a problem that has been difficult for him. He has found a way to do it, but needs help to continue.
  • #1
Tomer
202
0

Homework Statement


Thanks very much for reading.
I actually have two problems, I hope it's ok to state both of the in the same thread.

1. Let Vn be the space of all functions having the n'th derivitve in the point x0.
I've been given the semi-norm (holds all the norm axioms other than ||v|| = 0 => v = 0) defined by:
||f|| = [itex]\sum^{n}_{k=0}\frac{1}{k!}|f^{(k)}(x_{0})|[/itex]

I need to show that [itex]||fg|| \leq ||f|| ||g||[/itex]

2. I need to prove that if the set of polynomials {Pi}, i=0,...,n is orthonormal on [a,b] in respect to the inner product defined by: [itex]<f,g> = \int ^{b}_{a}f(x)g(x)dx[/itex]
then they hold the recurrance relation:
Pk+1(x) = (akx + bk)Pk(x) + ckPk-1(x)

The Attempt at a Solution



1. This one's been a nightmare for me. I've tried proving it straightforward, and then using induction, but I just get lost in a sea of indexes. I'm thinking there might be a more elegant way, but can't see it.
I'm of course trying to use the triangle inequality, which works great for the cases n=1 and n=2, but I just cannot generlize it.
I could write the whole development I've done here, it's like one whole page, but it'll take me ages and therefore think it's rather unnescesarry. I'll greatly appreciate a hint. If there's no other way but an ugly induction I think I'll skip it :-)
(I've used the fact that [itex](fg)^{(k)}(x_{0}) = \sum^{k}_{i=0}(\frac{k!}{i!(k-i)!})f^{(k-i)}g^{(i)}(x_{0})[/itex]

2. Well, I've been thinking polynomial division. If I assume the set of given Pi's is of ascending degrees (0,1,2...), I can always divide Pk+1(x) in Pk(x) and write:
[itex]P_{k+1}(x) = (a_{k}x + b_{k})P_{k}(x) + L(x) [/itex]
Where deg(L(x)) < deg(Pk(x))
I don't know how to take it from here. How do I use the orthogonality? I would greatly appreciate hints :-)
 
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  • #2
OK, for (a). You've come to the conclusion already that

[tex]
\begin{eqnarray*}
\|fg\|
& = & \sum_{k=0}^n{\frac{1}{k!}\left|\sum_{i=0}^k\frac{k!}{i!(k-i)!}f^{(k-i)}(x_0)g^{(i)}(x_0)\right|}\\
& \leq & \sum_{k=0}^n{\sum_{i=0}^k\frac{1}{i!(k-i)!}|f^{(k-i)}(x_0)g^{(i)}(x_0)|}
\end{eqnarray*}
[/tex]

The thing to do now is to switch those two sums appropriately.

For (b). The convention for orthogonal polynomials is that [itex]P_k[/itex] always has degree k. So I shall follow that convention here. (I'm pretty sure the result is false if the convention is not followed).

Anyway. If you pick a suitable [itex]a_k[/itex], then [itex]P_{k+1}-a_kxP_k[/itex] has degree [itex]\leq k[/itex]. So we can write

[tex]P_{k+1}-a_k xP_k=b_kP_k+c_kP_{k-1}+\sum_{j=1}^{k-2}{d_jP_{k-2}}[/tex]

Our job is to prove [itex]d_j=0[/itex]. Do this by taking appropriate inner products.
 
  • #3
Thanks, I'll try working on that.
 

What are norms and how are they related to orthogonal polynomials?

Norms are mathematical concepts used to measure the size or length of a vector or function. They are related to orthogonal polynomials because these polynomials are defined as a set of mutually orthogonal functions, which means they have a special relationship with regard to their inner product and norm.

How are norms and orthogonal polynomials used in mathematical analysis?

Norms and orthogonal polynomials are used in mathematical analysis to study and understand functions and their properties. They are particularly useful in approximating functions and solving differential equations, as well as in numerical integration and statistics.

What are some examples of orthogonal polynomials?

Some commonly used orthogonal polynomials include Legendre polynomials, Chebyshev polynomials, and Hermite polynomials. These polynomials have different properties and are used for different purposes, but they all have the common property of being mutually orthogonal.

How are norms and orthogonal polynomials related to each other?

As mentioned before, orthogonal polynomials are defined in terms of their inner product and norm. The inner product of two polynomials is a measure of their similarity, while the norm is a measure of their size. The orthogonality of these polynomials ensures that the inner product is zero, and therefore the norm is also zero, for any two different polynomials in the set.

What are the applications of norms and orthogonal polynomials in other fields?

The concepts of norms and orthogonal polynomials have applications in various fields such as physics, engineering, and computer science. For example, orthogonal polynomials are used in signal processing, image reconstruction, and data compression. They are also used in quantum mechanics to solve problems involving wave functions, and in statistics for curve fitting and data analysis.

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