Polynomials, Kernels and Derivatives

In summary, the conversation discusses a method for differentiating an expression, Y(x), which involves an integral with a polynomial term. The method involves using a binomial expansion and considering the coefficients of the terms in the expansion. By using Leibnitz's rule, the resulting expression is simplified to the desired form.
  • #1
psholtz
136
0
Is there a simple way to show that when we differentiate the following expression (call this equation 1):

[tex]Y(x) = \frac{1}{n!} \int_0^x (x-t)^n f(t)dt[/tex]

that we will get the following expression (call this equation 2):

[tex]Y'(x) = \frac{1}{(n-1)!}\int_0^x (x-t)^{n-1}f(t)dt[/tex]

It's simple enough to prove "by hand", for low-order polynomials. For instance, for n=1 we would have:

[tex]Y(x) = \int_0^x (x-t)f(t)dt = x\int_0^x f(t)dt - \int_0^x tf(t)dt[/tex]

[tex]Y'(x) = \int_0^x f(t)dt + xf(x) - xf(x) = \int_0^x f(t)dt[/tex]

and it can be proved just as easily for n=2, 3, etc.. But is there a straightforward way to prove it for general n? By induction perhaps? I'm reading a text where the author just goes from Equation 1 to Equation 2, and keeps rolling, w/o giving much in the way of explanation.
 
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  • #2
OK, I think I answered my own question.

Do a binomial expansion on the polynomial term. The kth term in this expansion is given by:

[tex](x-t)^n = \left(\array{c}n \\ k\endarray\right)x^{n-k}(-t)^k[/tex]

so the function Y can be rewritten as the sum:

[tex]Y(x) = \frac{1}{n!}\int_0^x \left[\sum_{k=0}^n \left(\array{c}n \\ k\endarray\right)x^{n-k}(-t)^k\right] \cdot f(t)dt[/tex]

Take the kth term in this sum:

[tex]Y_k(x) = \frac{(-1)^k}{n!}\left(\array{c}n \\ k \endarray\right)x^{n-k}\int_0^x t^kf(t)dt[/tex]

Differentiating w.r.t. x:

[tex]Y_k'(x) = \frac{(-1)^k}{n!}\left(\array{c}n \\k \endarray\right)(n-k)x^{(n-1)-k}\int_0^x t^kf(t)dt + \frac{(-1)^k}{n!}\left(\array{c}n \\ k\endarray\right)x^nf(x)[/tex]

Consider the second term. It can be written as:

[tex]\frac{x^nf(x)}{n!}\left(\array{c}n \\ k\endarray\right)\left(-1\right)^k[/tex]

and consider the sum of these terms, from 0 to n:

[tex]S_{n} = \frac{x^nf(x)}{n!}\sum_{k=0}^n \left(\array{c}n\\k\endarray\right)(-1)^k[/tex]

Since we can write:

[tex]\sum_{k=0}^n \left(\array{c}n\\k \endarray\right)(-1)^k
= \sum_{k=0}^n \left(\array{c}n \\ k \endarray\right)(1)^{n-k}(-1)^k = (1-1)^n = 0
[/tex]

Then clearly the "second term" in Y_k'(x) drops out when we sum the components up.

Consider now the first term in Y_k'(x):

[tex]\frac{n-k}{n!}\left(\array{c}n\\k\endarray\right)\int_0^x x^{(n-1)-k} (-t)^kf(t)dt[/tex]

and specifically, the coefficient on this term:

[tex]\frac{n-k}{n!}\left(\array{c}n\\k\endarray\right) = \frac{n-k}{n!} \cdot \frac{n!}{k!(n-k)!}[/tex]

[tex]= \frac{1}{n\cdot (n-1)!} \cdot \frac{n \cdot (n-1)!}{k!((n-1)-k)!}[/tex]

[tex]= \frac{1}{(n-1)!} \cdot \frac{(n-1)!}{k! ((n-1)-k)!}[/tex]

[tex]= \frac{1}{(n-1)!} \cdot \left(\array{c}n-1\\k\endarray\right)[/tex]

So that, ignoring the "second" term on Y_k'(x), which we already showed sums to 0, we can write Y_k'(x) as:

[tex]Y_k'(x) = \frac{1}{(n-1)!}\int_0^x \left(\array{c}n-1 \\ k\endarray\right)x^{(n-1)-k}(-t)^kf(t)dt[/tex]

so now summing over 0 to n-1, to get the full expression for Y'(x), we get:

[tex]Y'(x) = \sum_{k=0}^{n-1} Y_k'(x) = \frac{1}{(n-1)!}\int_0^x (x-t)^{n-1}f(t)dt[/tex]

which was to be proved..
 
  • #3
To differentiate

[tex]
Y(x) = \frac{1}{n!} \int_0^x (x-t)^n f(t)dt
[/tex]

with respect to x, just use the appropriate version of Leibnitz's rule:

[tex]\frac d {dx}\int_0^x f(x,t)\, dt = f(x,x) + \int_0^x f_x(x,t)\, dt[/tex]

This gives your result immediately.
 

1. What are polynomials and how are they used in mathematics?

Polynomials are mathematical expressions that consist of variables, coefficients, and exponents. They are used to represent and solve various types of equations and are an essential tool in algebra, calculus, and other branches of mathematics.

2. What is a kernel function and why is it important?

A kernel function is a mathematical function that is used to transform data into a higher dimensional space, making it easier to classify or separate. It is used in machine learning algorithms, particularly in support vector machines, to create decision boundaries between data points. Kernel functions are important because they allow for more complex and accurate models to be built.

3. How are derivatives used in calculus?

Derivatives are used in calculus to calculate the rate of change of a function at a specific point. They are an essential tool for finding maximum and minimum values, determining the slope of a tangent line, and solving optimization problems. Derivatives also have many real-world applications, such as in physics, economics, and engineering.

4. Can you explain the difference between a polynomial and a monomial?

A polynomial is an expression with one or more terms, each consisting of a variable raised to a non-negative integer power and multiplied by a coefficient. A monomial, on the other hand, is a polynomial with only one term. In other words, all monomials are polynomials, but not all polynomials are monomials.

5. How do you find the derivative of a polynomial function?

To find the derivative of a polynomial function, you can use the power rule, which states that the derivative of a term with a variable raised to a power is equal to the coefficient multiplied by the power and the variable raised to one less power. You can also use the sum and constant multiple rules to find the derivative of a polynomial with multiple terms.

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