How to Prove $n^{\alpha} > \ln(n)$ for $\alpha>0$ and n is Sufficiently Large?

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In summary, the conversation is discussing how to prove the inequality $n^{\alpha} > \ln(n)$ for $\alpha > 0$. The variables n and $\alpha$ are defined as n being a natural number and $\alpha$ being a real number. The conversation also includes a counter-example for the inequality and a discussion on the conditions for which it holds true. There is a disagreement on whether the inequality should hold for specific n and all $\alpha$'s or if it's only true for large n given a certain $\alpha$. Clarification on the actual problem is needed.
  • #1
Lisa91
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How to prove that [tex] n^{\alpha} > \ln(n) [/tex] for [tex] \alpha>0 [/tex]?
 
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  • #2
How are the variables defined?

For instance if $\alpha\in\mathbb{R}$ and $n\in\mathbb{N}$ then one example of a counter-example to your inequality occurs for:

$n=3,\,\alpha=0.01$

This leads me to believe there in some missing information.
 
  • #3
we have $n^{\alpha}> \ln(n) $ take ln to both sides :

I will separate the problem in two steps :

1-For 0<n<1 this is trivial since the right hand will be always negtative while
and the left hand side is always positive , also for n=1 the inequality holds .

2-Now for n>1 :

${\alpha}\ln(n)> \ln(\ln(n)) \Rightarrow \,\, \alpha> \frac{\ln(\ln(n))}{\ln(n) } $

Now this is only true iff $\frac{\ln(\ln(n))}{\ln(n) } \leq 0$

which holds iff $0<\ln(n)\leq 1\,\, \Rightarrow \,\, 1< n \leq e $

The inequality is true for all $\alpha $ iff $0<n \leq e$
 
  • #4
ZaidAlyafey said:
2-Now for n>1 :

${\alpha}\ln(n)> \ln(\ln(n)) \Rightarrow \,\, \alpha> \frac{\ln(\ln(n))}{\ln(n) } $

Since the right hand side approaches zero for large n, this means that for any $\alpha>0$ there is a number N such that the inequality is true for any n > N.Hey Lisa91!

Can it be there is a condition missing from your problem?
The extra condition that it holds for any n > N for some N?
 
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  • #5
ILikeSerena said:
Since the right hand side approaches zero for large n, this means that for any $\alpha>0$ there is a number N such that the inequality is true for any n > N.

since $\alpha $ is an independent variable of n I can choose it as small as possible so that
it becomes lesser than the right-hand side .
Can you give a counter example for $\alpha$ and n that disproves my argument ?
 
  • #6
ZaidAlyafey said:
since $\alpha $ is an independent variable of n I can choose it as small as possible so that
it becomes lesser than the right-hand side .
Can you give a counter example for $\alpha$ and n that disproves my argument ?

Your argument is flawless. ;)
It's just that you have assumed that the inequality should hold for specific n and all $\alpha$'s.
Whereas I have assumed it's not for all n.

In other words, you have solved:
Find n such that $n^\alpha > \ln n$ for all $\alpha > 0$.

Whereas I have use the first half of your argument to follow up with:
Prove that $n^\alpha > \ln n$ for $\alpha > 0$ if n is big enough given a certain $\alpha$.
​​
That's why we need clarification on what the actual problem is.
 
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FAQ: How to Prove $n^{\alpha} > \ln(n)$ for $\alpha>0$ and n is Sufficiently Large?

What does the statement "n^α > ln(n) for α>0" mean?

This statement is an inequality that compares two mathematical expressions: n raised to the power of α and the natural logarithm of n. It states that for any positive value of α, the value of n raised to that power will always be greater than the natural logarithm of n.

Why is it important to prove this inequality?

This inequality is important because it helps us understand the relationship between exponential and logarithmic functions. It also has many practical applications in fields such as economics, physics, and computer science.

How can we prove that n^α > ln(n) for α>0?

To prove this inequality, we can use mathematical induction, which is a method of proof that involves showing that a statement is true for a base case and then showing that if the statement is true for n, it must also be true for n+1. Using this method, we can prove that the inequality holds for all values of n and α greater than 0.

Are there any exceptions to this inequality?

No, this inequality holds true for all positive values of n and α. It is a fundamental property of exponential and logarithmic functions.

What are some real-life examples of this inequality?

One example is compound interest, where the value of an investment grows exponentially over time. Another example is population growth, where the size of a population increases exponentially. In both cases, the growth rate is greater than the natural logarithm of the initial value. This inequality also has practical applications in analyzing algorithms and their time complexities in computer science.

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