Skip to content
Surf Wiki
Save to docs
general/finite-differences

From Surf Wiki (app.surf) — the open knowledge base

Mean value theorem (divided differences)


In mathematical analysis, the mean value theorem for divided differences generalizes the mean value theorem to higher derivatives.

Statement of the theorem

For any n + 1 pairwise distinct points x0, ..., x**n in the domain of an n-times differentiable function f there exists an interior point

: \xi \in (\min{x_0,\dots,x_n},\max{x_0,\dots,x_n}) ,

where the nth derivative of f equals n ! times the nth divided difference at these points:

: f[x_0,\dots,x_n] = \frac{f^{(n)}(\xi)}{n!}.

For n = 1, that is two function points, one obtains the simple mean value theorem.

Proof

Let P be the Lagrange interpolation polynomial for f at x0, ..., x**n. Then it follows from the Newton form of P that the highest order term of P is f[x_0,\dots,x_n]x^n.

Let g be the remainder of the interpolation, defined by g = f - P. Then g has n+1 zeros: x0, ..., x**n. By applying Rolle's theorem first to g, then to g', and so on until g^{(n-1)}, we find that g^{(n)} has a zero \xi. This means that

: 0 = g^{(n)}(\xi) = f^{(n)}(\xi) - f[x_0,\dots,x_n] n!, : f[x_0,\dots,x_n] = \frac{f^{(n)}(\xi)}{n!}.

Applications

The theorem can be used to generalise the Stolarsky mean to more than two variables.

References

References

  1. de Boor, C.. (2005). "Divided differences". Surv. Approx. Theory.
Wikipedia Source

This article was imported from Wikipedia and is available under the Creative Commons Attribution-ShareAlike 4.0 License. Content has been adapted to SurfDoc format. Original contributors can be found on the article history page.

Want to explore this topic further?

Ask Mako anything about Mean value theorem (divided differences) — get instant answers, deeper analysis, and related topics.

Research with Mako

Free with your Surf account

Content sourced from Wikipedia, available under CC BY-SA 4.0.

This content may have been generated or modified by AI. CloudSurf Software LLC is not responsible for the accuracy, completeness, or reliability of AI-generated content. Always verify important information from primary sources.

Report