How is the remainder for Taylor polynomials calculated?

In summary, the conversation discusses calculating the Taylor polynomial and remainder for a given function. The formula for the remainder is derived and it is determined that it converges for all values of $\xi$ in the interval $(0,x)$. The next question asks to calculate the value of $\xi$ using the geometric series and it is confirmed that this is the correct approach. The conversation ends with the confirmation that the calculated value of $\xi$ does indeed make the remainder term converge to $0$ as $n$ approaches infinity.
  • #1
mathmari
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Hey! :eek:

I want to calculate the Taylor polynomial of order $n$ for the funktion $ f(x) = \frac{1}{ 1−x}$ for $x_0=0$ and $0 < x < 1$ and the remainder $R_n$.

We have that \begin{equation*}f^{(k)}(x)=\frac{k!}{(1-x)^{k+1}}\end{equation*}
I have calculated that \begin{equation*}P_{0,n}(x)=\sum_{k=0}^n\frac{f^{(k)}(0)\cdot x^k}{k!}=\sum_{k=0}^n x^k \end{equation*}

Is the remainder \begin{equation*}R_n=\frac{\frac{(n+1)!}{(1-\xi)^{n+2}}}{(n+1)!}\cdot (x-0)^{n+1}=\frac{x^{n+1}}{(1-\xi)^{n+2}}\end{equation*} ? Or do we have to use an other formula? (Wondering) In some notes I read that in general the remainder doesn't converge to $0$ for all $\xi\in (0,x)$. Can you give me an example for this? (Wondering)
 
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  • #2
mathmari said:
Is the remainder \begin{equation*}R_n=\frac{\frac{(n+1)!}{(1-\xi)^{n+2}}}{(n+1)!}\cdot (x-0)^{n+1}=\frac{x^{n+1}}{(1-\xi)^{n+2}}\end{equation*} ? Or do we have to use an other formula?

Hey mathmari! (Smile)

Yep. We can use that remainder.
There are also other formulas for the remainder, but this one should do fine.

mathmari said:
In some notes I read that in general the remainder doesn't converge to $0$ for all $\xi\in (0,x)$. Can you give me an example for this?

To converge, the remainder term has to be well-defined, doesn't it?
For which $\xi$ is it ill-defined, and therefore not convergent? (Wondering)

Furthermore, for the remainder term to converge to zero, we also need a condition on $x$ don't we?
And with that condition it becomes impossible to pick a $\xi$ so that the remainder doesn't converge. (Thinking)
 
  • #3
I like Serena said:
To converge, the remainder term has to be well-defined, doesn't it?
For which $\xi$ is it ill-defined, and therefore not convergent? (Wondering)

It is ill-defined if $\xi=1$, right? (Wondering)
I like Serena said:
Furthermore, for the remainder term to converge to zero, we also need a condition on $x$ don't we?

So that the remainder converges to $0$ it must hold that $x<1$, right? (Wondering)
 
  • #4
mathmari said:
It is ill-defined if $\xi=1$, right?

So that the remainder converges to $0$ it must hold that $x<1$, right?

Yep. (Nod)
And it must also hold that $x>-1$.

Anyway, since it is given that $0<x<1$, the remainder converges for all $\xi \in (0,x)$. (Nerd)
 
  • #5
I like Serena said:
Yep. (Nod)
And it must also hold that $x>-1$.

Anyway, since it is given that $0<x<1$, the remainder converges for all $\xi \in (0,x)$. (Nerd)

So, that means that for all $\xi \in (0,x)$ the remainder converges to $0$, right?
 
  • #6
mathmari said:
So, that means that for all $\xi \in (0,x)$ the remainder converges to $0$, right?

Yes. (Nod)
 
  • #7
At the next subquestion it says:

Using the geometric series we can calculate $\xi$ as a function of $n$ and $x$. Do that and show that it really holds that $R_n\rightarrow 0$ for $n\rightarrow \infty$.

Do we have to set equal $R_n=f(x)-P_n$ and calculate the value of $\xi$ ? Or what do I have to do here? (Wondering)
 
  • #8
mathmari said:
At the next subquestion it says:

Using the geometric series we can calculate $\xi$ as a function of $n$ and $x$. Do that and show that it really holds that $R_n\rightarrow 0$ for $n\rightarrow \infty$.

Do we have to set equal $R_n=f(x)-P_n$ and calculate the value of $\xi$ ? Or what do I have to do here?

Yes. I believe that is what we need to do.
 
  • #9
I like Serena said:
Yes. I believe that is what we need to do.

We have that $$R_n=\frac{1}{1-x}-\sum_{k=0}^n x^k =\frac{1}{1-x}-\frac{x^{n+1}-1}{x-1}=\frac{1}{1-x}+\frac{x^{n+1}-1}{1-x}=\frac{x^{n+1}}{1-x} \\ \Rightarrow \frac{x^{n+1}}{(1-\xi)^{n+2}}=\frac{x^{n+1}}{1-x} \Rightarrow \frac{1}{(1-\xi)^{n+2}}=\frac{1}{1-x} \Rightarrow (1-\xi)^{n+2}=1-x \\ \Rightarrow 1-\xi=\sqrt[n+2]{1-x}\Rightarrow \xi=1-\sqrt[n+2]{1-x}$$

If we substitute this in the remainder we get $$R_n=\frac{x^{n+1}}{1-x}\rightarrow 0$$ or not? (Wondering)
 
  • #10
Yep. All correct. (Nod)
 
  • #11
I like Serena said:
Yep. All correct. (Nod)

Great! Thank you so much! :-)
 

FAQ: How is the remainder for Taylor polynomials calculated?

What is the purpose of a Taylor polynomial?

A Taylor polynomial is used to approximate a function using a finite number of terms. It allows us to estimate the value of a function at a specific point by using the function's derivatives at that point.

How is the Taylor polynomial calculated?

The Taylor polynomial is calculated using the Taylor series, which is an infinite sum of terms involving the function's derivatives evaluated at a specific point. The polynomial is created by truncating the series after a certain number of terms.

What is the remainder in a Taylor polynomial?

The remainder in a Taylor polynomial refers to the difference between the actual function value and the value calculated using the polynomial. It represents the error in the approximation and gets smaller as the degree of the polynomial increases.

How do we know if the Taylor polynomial is a good approximation?

The accuracy of the Taylor polynomial can be determined by calculating the remainder term. If the remainder term is small, then the polynomial is a good approximation. Additionally, the polynomial will be a better approximation for values closer to the center point used in the calculation.

Can the Taylor polynomial be used for any function?

The Taylor polynomial can only be used for functions that are infinitely differentiable. This means that the function must have derivatives of all orders at the point where the polynomial is being calculated. Additionally, the closer the function is to the center point, the better the approximation will be.

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