Minimize integral using orthogonal basis

  • #1
psie
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Homework Statement
Determine the polynomial ##p## of degree at most ##1## that minimizes ##\int_0^2|e^x-p(x)|^2 dx##. (Hint: first find an orthogonal basis for a suitably chosen space of polynomials of degree ##1##.)
Relevant Equations
##L^2## norm, inner product, least squares approximation.
I'm posting to inquire about a possible typo in the given answer in the back of the book, or if maybe I did something wrong, because my answer does not agree with the one stated.

So the exercise is about finding the least squares approximation. The norm is the ##L^2## norm and the corresponding inner product is $$\langle f, g\rangle=\int_0^2 f(x)g(x)dx.$$ I choose the polynomials ##v_1=1## and ##v_2=x## as a basis and make them orthogonal according to Gram-Schmidt, i.e. I find that ##u_1=1## and ##u_2=x-1## are orthogonal to each other under the inner product above. Put ##u=e^x##, then the orthogonal projection of ##u## on the subspace spanned by ##u_1,u_2## is given by $$P(u)=\frac{\langle u, u_1\rangle}{\langle u_1,u_1\rangle}u_1+\frac{\langle u, u_2\rangle}{\langle u_2,u_2\rangle}u_2.$$ Now, \begin{align}\langle u_1,u_1\rangle&=\int_0^2 dx=2 \nonumber \\ \langle u_2,u_2\rangle&=\int_0^2(x-1)^2dx=\int_0^2(x^2-2x+1)dx=\frac23, \nonumber\end{align}and\begin{align}
\langle u,u_1\rangle&=\int_0^2 e^xdx=e^2-1 \nonumber \\
\langle u,u_2\rangle&=\int_0^2(x-1)e^x dx=2. \nonumber
\end{align}
Plugging these into the equation for ##P(u)##, gives $$P(u)=3x+\frac12(e^2-1),$$but the answer given is ##3x+\frac12(e^2-7)##. Is there a way to check one's answer to know if you got the right one?
 
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  • #2
Sorry, I just realized now I forgot to use ##u_2=x-1## instead of ##v_1=x##.:doh:
 
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  • #3
psie said:
Sorry, I just realized now I forgot to use ##u_2=x-1## instead of ##v_1=x##.:doh:
... and you tell me this now that I have literally checked every single integral and step and was right about to answer where your typo was ... :devil: :biggrin:
 
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What is the purpose of minimizing an integral using an orthogonal basis?

Minimizing an integral using an orthogonal basis is a technique used to simplify the calculation of integrals by expressing the integrand as a linear combination of orthogonal basis functions. This makes the integration process more manageable and can lead to more accurate results.

How do orthogonal basis functions help in minimizing integrals?

Orthogonal basis functions help in minimizing integrals by providing a set of functions that are linearly independent and orthogonal to each other. This property allows us to express the integrand as a linear combination of these basis functions, making the integration process simpler and more efficient.

What are some common orthogonal basis functions used in minimizing integrals?

Some common orthogonal basis functions used in minimizing integrals include Legendre polynomials, Chebyshev polynomials, Hermite polynomials, and Fourier series. These basis functions have specific properties that make them well-suited for different types of integrals and can help simplify complex integrations.

How does minimizing an integral using an orthogonal basis improve computational efficiency?

Minimizing an integral using an orthogonal basis improves computational efficiency by reducing the complexity of the integration process. By expressing the integrand as a linear combination of orthogonal basis functions, we can simplify the integral calculation and potentially reduce the number of computations required to obtain the final result.

Can minimizing integrals using an orthogonal basis lead to more accurate results?

Yes, minimizing integrals using an orthogonal basis can lead to more accurate results compared to traditional integration methods. By using orthogonal basis functions, we can reduce errors and improve the precision of the integration process, resulting in more reliable and accurate numerical solutions.

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