Finding an upper-estimate for a sequence.

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In summary, the conversation is discussing ways to find a tighter upper estimate for a particular sequence that converges to 1. Some possible techniques mentioned include using the sandwich theorem and l'Hopital's Rule, as well as a direct approach. The conversation ends with the person deciding to try the sandwiching method first.
  • #1
Seydlitz
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Hi guys,

I'm on the verge of sandwiching this particular sequence but I need rather tight upper estimate to trap the limit to 1. I can only manage to get the sequence that converges to ##e## as the current upper estimate. Is it possible to get tighter bound than that?

[tex]
\\
1 + \frac{1}{n^2} \le \left(1+\frac{1}{n^3}\right)^{n} \le \left(1+\frac{1}{n}\right)^n
[/tex]

Alternatively, I can maybe show that the sequence is decreasing and that the limit infimum of the middle sequence is 1. But it might not be as simple as using sandwich theorem.

Thank You

P.S: In general, is there any inequality that shows the upper-estimate of ##(1+x)^n## in contrast to the Bernoulli's inequality that shows the lower-estimate of it?
 
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  • #2
You can compare it to ##\left(1+\frac{1}{mn}\right)^n## for every natural number m.
 
  • #3
Seydlitz said:
Hi guys,

I'm on the verge of sandwiching this particular sequence but I need rather tight upper estimate to trap the limit to 1. I can only manage to get the sequence that converges to ##e## as the current upper estimate. Is it possible to get tighter bound than that?

[tex]
\\
1 + \frac{1}{n^2} \le \left(1+\frac{1}{n^3}\right)^{n} \le \left(1+\frac{1}{n}\right)^n
[/tex]

Alternatively, I can maybe show that the sequence is decreasing and that the limit infimum of the middle sequence is 1. But it might not be as simple as using sandwich theorem.

Alternatively, you can show by l'Hopital's Rule that [tex]\lim_{n \to \infty} n\log\left(1 + \frac1{n^3}\right) = \lim_{x \to 0^{+}} \frac{\log(1 + x^3)}{x} = 0.[/tex]
 
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  • #4
If all you want is to show that

##\lim_{n\to \infty} (1+\frac 1{n^3})^n=1##,

then I wouldn't use sandwiching and estimates, but a direct approach:

##(1+\frac 1{n^3})^n=((1+\frac 1{n^3})^{n^3})^{1/n^2}\to e^0=1##, as ##n\to\infty##.
 
  • #5
Ok guys thanks for the input. I'll try the sandwiching the guys first.
 

FAQ: Finding an upper-estimate for a sequence.

1. What is an upper-estimate for a sequence?

An upper-estimate for a sequence is the largest possible value that the terms in the sequence could reach. It is used to bound the sequence from above and can help in predicting the behavior of the sequence.

2. How do you find an upper-estimate for a sequence?

To find an upper-estimate for a sequence, you can look for patterns in the terms and use mathematical techniques such as induction, comparison with other known sequences, or using the formula for the general term of the sequence. It is also important to understand the behavior of the sequence and consider its limits.

3. Why is finding an upper-estimate for a sequence important?

Finding an upper-estimate for a sequence is important because it can help in determining the behavior and limits of the sequence. It can also be useful in making predictions about the sequence, such as its convergence or divergence, and can provide insights into the larger context of the sequence.

4. Can an upper-estimate be greater than the actual value of the sequence?

Yes, an upper-estimate can be greater than the actual value of the sequence. This is because an upper-estimate is simply a bound or approximation and may not always accurately reflect the exact value of the sequence. It is important to keep this in mind when using an upper-estimate in calculations or predictions.

5. Is there a way to improve the accuracy of an upper-estimate for a sequence?

Yes, there are ways to improve the accuracy of an upper-estimate for a sequence. One way is to use more advanced mathematical techniques, such as the squeeze theorem or the limit comparison test, to determine a tighter bound for the sequence. Additionally, continually refining and adjusting the upper-estimate based on new information or patterns in the sequence can lead to a more accurate estimate.

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