Minimize integral using orthogonal basis

In summary, minimizing an integral using an orthogonal basis involves decomposing a function into a series of orthogonal components, which simplifies the process of finding the minimum value. By representing the function as a linear combination of basis functions, the integral can be reduced to optimizing coefficients that minimize the error in approximation. This approach leverages the properties of orthogonality, allowing for efficient calculations and clearer insights into the function's behavior.
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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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Sorry, I just realized now I forgot to use ##u_2=x-1## instead of ##v_1=x##.:doh:
 
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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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FAQ: Minimize integral using orthogonal basis

What is an orthogonal basis?

An orthogonal basis is a set of vectors in a vector space that are all orthogonal to each other. In other words, the inner product of any two distinct vectors in the set is zero. This concept is crucial in simplifying many mathematical problems, including the minimization of integrals.

How does an orthogonal basis help in minimizing integrals?

Using an orthogonal basis can simplify the process of minimizing integrals by transforming the problem into a more manageable form. Orthogonal functions can make it easier to decompose a function into simpler components, allowing for more straightforward integration and minimization.

What is the process to minimize an integral using an orthogonal basis?

The process typically involves expressing the function to be minimized as a linear combination of orthogonal basis functions. By leveraging the orthogonal properties, one can simplify the integral and find the coefficients that minimize the integral. This often involves solving a system of linear equations.

Can you provide an example of minimizing an integral using an orthogonal basis?

Sure! Consider the integral of a function \( f(x) \) over an interval [a, b]. If we use a set of orthogonal polynomials \( \{P_n(x)\} \) as the basis, we can express \( f(x) \) as \( f(x) = \sum_{n=0}^{\infty} c_n P_n(x) \). The coefficients \( c_n \) are found using the inner product \( c_n = \frac{\langle f, P_n \rangle}{\langle P_n, P_n \rangle} \). The minimized integral is then easier to evaluate using these coefficients.

What are some common orthogonal bases used in minimizing integrals?

Common orthogonal bases include Legendre polynomials, Chebyshev polynomials, and Fourier series. Each of these has specific properties that make them suitable for different types of problems. For example, Fourier series are particularly useful for periodic functions, while Legendre and Chebyshev polynomials are often used in approximations and numerical integration.

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