Proving Orthogonal Polynomials: A Weighted Integral

In summary, the given problem asks us to prove that for a set of orthogonal polynomials \{ \phi_0,\phi_1,...,\phi_n\} on an interval [a,b] and a weight function w(x), the integral of w(x) multiplied by any polynomial Q_k(x) of degree k<n is equal to 0. However, there is an implicit assumption that \phi_k(x) is of degree k, and if this is not the case, the result may fail.
  • #1
Amer
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Let [tex]\{ \phi_0,\phi_1,...,\phi_n\}[/tex] othogonal set of polynomials on [a,b] n>0, with a weight function w(x) prove that

[tex]\int_{a}^b w(x)\phi_n Q_k (x) \; dx = 0 [/tex]

for any polynomail [tex]Q_k(x) [/tex] of degree k<n ?

My work :

I think there is a problem in the question since if we take [tex]x^2,x^3 [/tex] on the interval [-1,1] they are orthogonal

but if we take x

[tex]\int_{-1}^{1} x(x^3 ) \; dx \neq 0 [/tex]
 
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  • #2
you haven't defined your weight function w(x), but let's assume it is the constant function 1. clearly $1,x$ are orthogonal, so we can start with a basis:

$B = \{1,x,\dots \}$

now let's look at what our third basis element $ax^2 + bx + c$ might be:

being orthogonal to 1 requires that $\int_{-1}^1ax^2 + bx + c\ dx = 0$. evaluating the integral, we find that:

$\frac{a}{3} + c - (\frac{-a}{3} + (-c)) = \frac{2a}{3} + 2c = 0$, and simplifying we get: $c = \frac{-a}{3}$.

so our second degree polynomial is of the form: $ax^2 + bx - \frac{a}{3}$.

since we must also have our second-degree polynomial orthogonal to x, this means that:

$\int_{-1}^1 ax^3 + bx^2 - \frac{ax}{3}\ dx = 0$, and evaluating THAT interval leads to $b = 0$.

traditionally, these polynomials are "normalized" so that $\phi_k(1) = 1$, doing so for:

$\phi_2(x) = ax^2 - \frac{a}{3}$ leads to: $a = \frac{3}{2}$, so that we get: $\phi_2(x) = \frac{1}{2}(3x^2 - 1)$.

the point is, there is no reason to assume that the "standard" basis: $\{1,x,x^2,x^3,\dots \}$ will be orthogonal with respect to the inner product defined by:

$\langle f,g \rangle = \int_{-1}^1 f(x)g(x)\ dx$ or the "weighted inner product" $\langle f,g \rangle = \int_{-1}^1 w(x)f(x)g(x)\ dx$

if you continue the process i started above (or by using gram-schmidt), you will get:

$\phi_3(x) = \frac{1}{2}(5x^3 - 3x)$ which can be verified to be orthogonal to $\phi_0, \phi_1,\phi_2$.
 
  • #3
thanks, I edited my post
can you check it again ?
 
  • #4
Amer said:
Let [tex]\{ \phi_0,\phi_1,...,\phi_n\}[/tex] othogonal set of polynomials on [a,b] n>0, with a weight function w(x) prove that

[tex]\int_{a}^b w(x)\phi_n Q_k (x) \; dx = 0 [/tex]

for any polynomail [tex]Q_k(x) [/tex] of degree k<n ?

My work :

I think there is a problem in the question since if we take [tex]x^2,x^3 [/tex] on the interval [-1,1] they are orthogonal

but if we take x

[tex]\int_{-1}^{1} x(x^3 ) \; dx \neq 0 [/tex]
Then why do you assert that they are orthogonal. In particular, what is your definition of "orthogonal"?
 
  • #5
Amer said:
Let [tex]\{ \phi_0,\phi_1,...,\phi_n\}[/tex] othogonal set of polynomials on [a,b] n>0, with a weight function w(x) prove that

[tex]\int_{a}^b w(x)\phi_n Q_k (x) \; dx = 0 [/tex]

for any polynomail [tex]Q_k(x) [/tex] of degree k<n ?

My work :

I think there is a problem in the question since if we take [tex]x^2,x^3 [/tex] on the interval [-1,1] they are orthogonal

but if we take x

[tex]\int_{-1}^{1} x(x^3 ) \; dx \neq 0 [/tex]

There is something missing from this, there seems to be an implicit assumption that \( \phi_k(x) \) is of degree \( k \) (or rather that \( \phi_n(x) \) is of degree \(n\) and every degree less than \(n\) is represented by one of the other \(\phi\)s ). If this is not the case then the result can fail.

CB
 
  • #6
CaptainBlack said:
There is something missing from this, there seems to be an implicit assumption that \( \phi_k(x) \) is of degree \( k \) (or rather that \( \phi_n(x) \) is of degree \(n\) and every degree less than \(n\) is represented by one of the other \(\phi\)s ). If this is not the case then the result can fail.

CB

it is true, our instructor fixed the question as what you said ([tex]\phi_k[/tex] is of order k )and i solved it
 

FAQ: Proving Orthogonal Polynomials: A Weighted Integral

What are orthogonal polynomials?

Orthogonal polynomials are a special type of mathematical function that satisfy a specific property called orthogonality. This means that when two different polynomials are multiplied together and integrated over a certain interval, the result is equal to 0. Examples of orthogonal polynomials include Legendre, Chebyshev, and Hermite polynomials.

Why is proving orthogonality important?

Proving that a set of polynomials is orthogonal is important because it allows us to use them in various mathematical applications, such as numerical integration, solving differential equations, and approximating functions. It also allows us to simplify complex mathematical problems by decomposing them into a series of simpler orthogonal polynomials.

What is the weighted integral in the proof of orthogonal polynomials?

The weighted integral is a way of assigning weights to the different terms in the polynomial to ensure that the orthogonality property holds. It is often denoted by a function, w(x), and is multiplied with each term in the polynomial before integrating. The choice of the weight function w(x) depends on the specific set of orthogonal polynomials being studied.

How do you prove that a set of polynomials is orthogonal?

The proof of orthogonality involves showing that the inner product of any two different polynomials in the set is equal to 0. This is typically done by using the weighted integral and manipulating the terms to show that they cancel out to 0. The proof may also involve using properties of the specific set of orthogonal polynomials, such as recurrence relations or differential equations.

Are there any applications of orthogonal polynomials?

Yes, orthogonal polynomials have many applications in mathematics, physics, and engineering. They are commonly used in numerical analysis for approximating functions and solving differential equations. They also have applications in signal processing, statistics, and quantum mechanics. In addition, orthogonal polynomials are used in the construction of mathematical functions, such as wavelets and B-splines.

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