Serious second mean value theorem for integration

In summary, the claim states that if f:[a,b]\to\mathbb{R} is integrable, and \phi:[a,b]\to\mathbb{R} is monotonic and continuous almost everywhere, then there exists a point \xi\in ]a,b[ such that a certain equation holds. This claim can be proven using a weaker formulation of the theorem, which states that if f:[a,b]\to\mathbb{R} is continuous, and \phi:[a,b]\to\mathbb{R} is differentiable with a non-negative derivative, then a similar equation holds for a point \xi\in [a,b]. This proof involves substitution, integration by parts, and the first
  • #1
jostpuur
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The claim:

If [itex]f:[a,b]\to\mathbb{R}[/itex] is integrable, and [itex]\phi:[a,b]\to\mathbb{R}[/itex] is monotonic (hence continuous almost everywhere), then there exists [itex]\xi\in ]a,b[[/itex] such that
[tex]
\int\limits_a^b f(x)\phi(x)dx \;=\; \big(\lim_{x\to a^+}\phi(x)\big) \int\limits_a^{\xi} f(x)dx \;+\; \big(\lim_{x\to b^-}\phi(x)\big) \int\limits_{\xi}^b f(x)dx
[/tex]

Who knows how to prove that?

Or who knows a serious book on calculus, that would cover this? Or a publication that could be found in university libraries?

I found the claim from Wikipedia: http://en.wikipedia.org/wiki/Mean_value_theorem But no proof.

I don't remember where, but somewhere some years ago I found a website, that gave a proof for a weaker formulation of this theorem. It goes like this:

If [itex]f:[a,b]\to\mathbb{R}[/itex] is continuous, and [itex]\phi:[a,b]\to\mathbb{R}[/itex] is differentiable such that [itex]\phi'\geq 0[/itex], then there exists [itex]\xi\in [a,b][/itex] such that
[tex]
\int\limits_a^b f(x)\phi(x)dx \;=\; \phi(a) \int\limits_a^{\xi} f(x)dx \;+\; \phi(b) \int\limits_{\xi}^b f(x)dx
[/tex]
This can be proven by first substituting
[tex]
f(x) = D_x\int\limits_a^x f(u)du
[/tex]
then integrating by parts, and then using the first mean value theorem.
 
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You first prove ##\displaystyle{\int_a^b }f(x)\phi(x)\,dx = f(\xi) \displaystyle{\int_a^b}\phi(x)\,dx## and apply partial integration and the fundamental theorem of calculus.
 

FAQ: Serious second mean value theorem for integration

1. What is the Serious Second Mean Value Theorem for Integration?

The Serious Second Mean Value Theorem for Integration is a mathematical theorem that states that if a function is continuous on a closed interval and its derivative is integrable on that interval, then there exists a point within that interval where the average value of the function is equal to the value of the definite integral of the derivative.

2. How is the Serious Second Mean Value Theorem different from the Mean Value Theorem?

The Serious Second Mean Value Theorem is a more general and powerful version of the Mean Value Theorem. While the Mean Value Theorem only applies to functions that are continuous on a closed interval and differentiable on the open interval, the Serious Second Mean Value Theorem applies to functions that are only continuous on a closed interval and integrable on that interval.

3. What is the significance of the Serious Second Mean Value Theorem for Integration?

The Serious Second Mean Value Theorem is an important tool in calculus and real analysis. It allows us to prove the existence of certain points within a function, which can be useful in proving other theorems or solving optimization problems.

4. Can the Serious Second Mean Value Theorem be extended to higher dimensions?

Yes, the Serious Second Mean Value Theorem can be extended to higher dimensions through the use of multivariable calculus. The theorem states that if a multivariable function is continuous on a closed and bounded region and its partial derivatives are integrable on that region, then there exists a point within the region where the average value of the function is equal to the value of the multiple integral of the partial derivatives.

5. How is the Serious Second Mean Value Theorem used in real-world applications?

The Serious Second Mean Value Theorem has many applications in various fields, such as physics, engineering, and economics. For example, it can be used to find the average speed of an object over a given time interval or to determine the average rate of change of a physical quantity. It can also be used in optimization problems to find the most efficient solution.

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