Show that the measure is equal to zero

In summary, if $\mu$ is a Borel measure on $\Bbb R$, then for any bounded open interval $I$, $\mu(I) \le [v(I)]^a$ for all bounded intervals $I$.
  • #1
mathmari
Gold Member
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Hey! :eek:

Let $\mu$ be a Borel measure in $\mathbb{R}$ such that $\mu(I)\leq v^a(I)$ for each bounded interval $I$, where $a>1$.

Show that $\mu=0$.

Could you give some hints how to show this??

Do we maybe use the identity that for each rectangle R the outer measure of R is equal to the volume of R. But in this case we don`t have an outer measure... :/
 
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  • #2
What does $v^a$ stand for, mathmari?
 
  • #3
Euge said:
What does $v^a$ stand for, mathmari?

$v$ stands for volume.
 
  • #4
So you're asking to show that if $\mu$ is a Borel measure on $\Bbb R$ such that for some $a > 1$, $\mu(I) \le [v(I)]^a$ for all bounded intervals $I$, then $\mu = 0$?
 
  • #5
Euge said:
So you're asking to show that if $\mu$ is a Borel measure on $\Bbb R$ such that for some $a > 1$, $\mu(I) \le [v(I)]^a$ for all bounded intervals $I$, then $\mu = 0$?

Yes! (Wondering)
 
  • #6
Ok, let's start by showing $\mu(I) = 0$ for every bounded open interval $I$. Take an arbitrary, bounded open interval $I = (c,d)$. Given $n\in \Bbb N$, partition $I$ into $n$ disjoint intervals of length $(d - c)/n$:

\(\displaystyle \bigl(c, c + \tfrac{d - c}{n}\bigr), \bigl[c + \tfrac{d - c}{n}, c + \tfrac{2(d - c)}{n}),\ldots, \bigl[c + \tfrac{(n-1)(d - c)}{n}, d\bigr)\)

By hypothesis and countable additivity of $\mu$,

\(\displaystyle \mu(I) = \mu\bigl(c, c + \tfrac{d - c}{n}\bigr) + \mu\bigl[c + \tfrac{d - c}{n}, c + \tfrac{2(d - c)}{n}\bigr) + \cdots + \mu\bigl[c + \tfrac{(n-1)(d - c)}{n}, d\bigr)\)

\(\displaystyle \le \Bigl(\frac{d - c}{n}\Bigr)^a + \Bigl(\frac{d - c}{n}\Bigr)^a + \cdots + \Bigl(\frac{d - c}{n}\Bigr)^a\; (\text{$n$ times})\)

\(\displaystyle = \frac{(d - c)^a}{n^{a-1}}.\)

Since $n$ is arbitrary and $a > 1$, taking the limit as $n\to \infty$ yields $\mu(I) \le 0$. Therefore $\mu(I) = 0$.

Next, let $I$ be any unbounded open interval. Then $I$ takes the form $(-\infty, x)$, $(x, \infty)$ (where $x\in \Bbb R$), or $(-\infty, \infty)$. In the first case, the sequence $\{(x - n, x)\}_{n = 1}^\infty$ increases to $(-\infty, x)$, and so $\mu(I) = \lim_{n\to \infty} \mu(x - n, x) = \lim_{n\to \infty} 0 = 0$. A similar argument applies to the second case. As for the last case,

\(\displaystyle \mu(I) = \mu\Bigl(\bigcup_{n = 1}^\infty (-n, n)\Bigr) \le \sum_{n = 1}^\infty \mu(-n,n) = \sum_{n = 1}^\infty 0 = 0,\)

hence $\mu(I) = 0$.

Now can you show that $\mu(G) = 0$ for every open set $G \subset \Bbb R$?
 

Related to Show that the measure is equal to zero

What does it mean to "show that the measure is equal to zero"?

To "show that the measure is equal to zero" means to provide evidence or a mathematical proof that the measure of a certain quantity or set is equal to zero. This can be done using various mathematical techniques and principles.

Why is it important to show that the measure is equal to zero?

Showing that the measure is equal to zero is important because it helps to prove certain properties or characteristics of a given system or quantity. It can also be used to simplify complex equations or to demonstrate that a particular condition is satisfied.

What are some common techniques for showing that the measure is equal to zero?

Some common techniques for showing that the measure is equal to zero include using the definition of the measure, applying mathematical theorems and laws, using algebraic manipulations, and performing mathematical operations such as integration or differentiation.

Can the measure ever be equal to zero?

Yes, the measure can be equal to zero. This means that the quantity being measured has no magnitude or size, or that the set being measured is empty. For example, the measure of an empty set is always equal to zero.

What are some real-world applications of showing that the measure is equal to zero?

Showing that the measure is equal to zero has many real-world applications, including in physics, where it can be used to prove conservation laws; in finance, where it can be used to calculate risk or determine the probability of certain events; and in computer science, where it can be used to optimize algorithms and solve problems efficiently.

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