An inequality between the integral Remainder of a function and the function.

In summary, the conversation discusses the function $f(x)$ which is infinitely differentiable in the neighborhood of $x_0$, with $f^{(k)}(x) \ge 0$ for each $k=0,1,2,3,\ldots$ for all $x$ in this neighborhood. It also introduces the function $R_n(x)$ and its integral form of Taylor's theorem, and aims to show that $R_n(b) \le f(b)$.
  • #1
Alone
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Suppose we have a function $f(x)$ which is infinitely differentiable in the neighborhood of $x_0$, and that: $f^{(k)}(x) \ge 0$ for each $k=0,1,2,3,\ldots$ for all $x$ in this neighborhood.

Let $R_n(x)=\frac{1}{n!}\int_a^x f^{(n+1)}(t)(x-t)^n dt$ where $x_0-\epsilon <a<x<b<x_0+\epsilon$;

I want to show that $R_n(b) \le f(b)$, how to show this?

Thanks.
 
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  • #2
Hi Alan,

The integral form of Taylor's theorem yields
$$f(x) = f(a) + f'(a)(x - a) + \cdots + \frac{f^{(n)}(a)}{n!}(x - a)^n + R_n(x)$$
for all $x$ in your neighborhood of $x_0$. If $x$ is in that neighborhood, $\dfrac{f^{(k)}(a)}{k!} (x - a)^k$ is nonnegative for all $k$. Thus $f(b) \ge R_n(b)$.
 
  • #3
Yes, easy.

Thanks Euge!
 

FAQ: An inequality between the integral Remainder of a function and the function.

What is an integral remainder?

An integral remainder is the difference between the exact value of an integral and its numerical approximation. It is the amount by which the numerical approximation deviates from the true value of the integral.

How is an integral remainder calculated?

The integral remainder can be approximated using various mathematical techniques, such as the midpoint rule, trapezoidal rule, or Simpson's rule. These methods involve dividing the interval into smaller subintervals and using the values of the function at specific points within each subinterval to approximate the integral. The difference between the exact value of the integral and the approximation is the integral remainder.

What is the significance of an inequality between the integral remainder and the function?

The inequality between the integral remainder and the function is an indication of the accuracy of the numerical approximation of the integral. If the integral remainder is small in comparison to the function, it means that the approximation is close to the true value of the integral. However, if the integral remainder is large, it indicates that the approximation is not accurate and may need to be improved through the use of a different method or a smaller subinterval size.

How can we use the inequality between the integral remainder and the function in practical applications?

The inequality between the integral remainder and the function is useful in determining the error in numerical approximations of integrals. This can be applied in various fields such as physics, engineering, and economics, where integrals are used to model real-world phenomena. By analyzing the integral remainder, we can assess the accuracy of the numerical results and make necessary adjustments to improve the approximation.

Are there any techniques to minimize the integral remainder?

Yes, there are various techniques for minimizing the integral remainder, such as using higher-order numerical methods, decreasing the size of the subintervals, or increasing the number of subintervals. It is also important to choose an appropriate method based on the characteristics of the function being integrated, as some methods may be more accurate for certain types of functions. Additionally, using computer software or calculators with advanced numerical integration capabilities can also help minimize the integral remainder.

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