Proving M_1^2 ≤ 4M_0M_2 in Real Analysis

In summary, the conversation discusses the proof of M_1^2 <= 4M_0M_2 for a twice-differentiable real function on (a, infinity) with given least upper bounds for its absolute value, first derivative, and second derivative. The conversation mentions using Taylor's Theorem and a hint provided in Rudin's textbook to show this inequality.
  • #1
ehrenfest
2,020
1

Homework Statement


Suppose [itex]a \in \mathbb{R}[/itex], f is a twice-differentiable real function on (a, \infinty) and M_0,M_1,M_2 are the least upper bound of [itex]|f(x)|,|f'(x)|,|f''(x)|[/itex], respectively on (a,\infinity). Prove that

[tex]M_1^2\leq 4 M_0 M_2[/tex]

Homework Equations


The Attempt at a Solution


That is equivalent to showing that M_2 x^2 +M_1 x +M_0=0 has a real solution.

I was trying to use Taylor's Theorem which says that if \alpha and \beta are distinct points in (a,\infinity) then there exists x between \alpha and \beta that makes the following equation true:

[tex]f(\beta) = f(\alpha) + f'(\alpha)(\beta-\alpha) + f''(x) (\beta-\alpha)^2/2[/tex]

I could take the absolute value of both sides and then use triangle inequality but I did not see how to get anywhere with that.
 
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  • #2
Did you look at the hint given in rudin?
 
  • #3
How was I supposed to know there was a hint on the next page? That is a serious question.
 
  • #4
OK. The hint says:

If h>0, Taylor's theorem show that

[tex]f'(x) = \frac{1}{2h}[f(x+2h)-f(x)]-hf''(\xi)[/tex]

for some [itex]\xi \in (x,x+2h)[/itex]. Hence

[tex]|f'(x)| \leq hM_2+M_0/h[/tex]

I don't see how the last inequality is useful (even if you square it).
 
  • #5
Square it and remember that it holds for ANY h > 0. There is a particular choice of h that will yield the result.
 

1. What does the inequality M_1^2 ≤ 4M_0M_2 mean?

The inequality M_1^2 ≤ 4M_0M_2 is a mathematical statement commonly used in Real Analysis to compare the first moment (M_1) of a probability distribution to the product of the zeroth moment (M_0) and second moment (M_2). It is often used to determine the spread or variability of a distribution.

2. How is this inequality proved in Real Analysis?

The proof of M_1^2 ≤ 4M_0M_2 involves using properties of integrals and moments, as well as algebraic manipulations. It can also be proven using moment generating functions or characteristic functions. The specific method used may vary depending on the context and the specific distribution being analyzed.

3. What implications does this inequality have in Real Analysis?

The inequality M_1^2 ≤ 4M_0M_2 has important implications in Real Analysis, particularly in statistics and probability theory. It is often used to determine the skewness and kurtosis of a distribution, as well as to establish bounds on the moments of a distribution. It is also used in various statistical tests and estimators.

4. Are there any exceptions to this inequality?

While the inequality M_1^2 ≤ 4M_0M_2 holds for most distributions, there are some exceptions. For example, it may not hold for distributions with heavy tails or those that are highly skewed. In these cases, alternative measures of spread and variability may be used.

5. How is this inequality used in practical applications?

The inequality M_1^2 ≤ 4M_0M_2 has many practical applications in various fields, including finance, engineering, and social sciences. It is used to analyze and compare data sets, make predictions, and evaluate the performance of statistical models. It also plays a role in risk assessment and decision making.

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