Is this Series Convergent?

  • Thread starter ryeager
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    Convergence
In summary, the conversation is discussing whether the series from n=2 to infinity of 1/[(lnx)^lnx] is convergent or divergent. The attempted solution involved using the comparison test and trying to compare it with 1/n^2, but the results were inconclusive. Another approach suggested was to use a theorem stating that if a series is monotonically decreasing, then it is convergent if and only if a related series is convergent.
  • #1
ryeager
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Homework Statement


Determine whether the series is convergent or divergent.

Sum from n=2 to n = infinity of

1/[(lnx)^lnx]

Homework Equations





The Attempt at a Solution



So far I've tried the comparison test, but all I can reach is that the series is less than the harmonic series, which of course doesn't help. Any help would be appreciated greatly, thanks.
 
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  • #2
did you mean here

[tex]\sum_{n=2}^{\infty}\frac{1}{[ln(n)]^{ln(n)}}[/tex]
 
  • #3
Let's try comparing it with 1/n^2. Remember, you can discard any finite number of terms when you do a comparison test. Try to figure out the limit n->infinity n^2/(log(n)^log(n)). Hint: take the log of the ratio.
 
  • #4
There's a theorem said,
if a1>=a2>=...>=0, then [tex]\sum^{\infty}_{n=1}a_{n}[/tex] converges if and only if [tex]\sum^{\infty}_{k=0} 2^{k}a_{2^{k}}[/tex] converges.
This will make the original series to a better-looking one
 

1. What is a tough convergence problem?

A tough convergence problem refers to a situation in which a mathematical or scientific model is unable to reach a stable or consistent solution. This can occur when there are multiple variables or factors at play, making it difficult for the model to accurately predict an outcome.

2. How do you approach a tough convergence problem?

The approach to solving a tough convergence problem can vary depending on the specific situation. However, some common strategies include adjusting the parameters or assumptions of the model, incorporating additional data or factors, and using more advanced mathematical techniques such as optimization algorithms.

3. What are some common causes of tough convergence problems?

There are several factors that can contribute to a tough convergence problem. These include a lack of data or incomplete data, complex or nonlinear relationships between variables, and the presence of outliers or extreme values. In some cases, the model itself may also be too simplistic or unable to account for all relevant variables.

4. Can a tough convergence problem be completely solved?

In most cases, a tough convergence problem cannot be completely solved as it is often a result of inherent complexities or limitations in the data or model. However, through continuous refinement and improvement of the model, it is possible to achieve a satisfactory level of convergence that produces reliable results.

5. How important is solving a tough convergence problem in scientific research?

Solving tough convergence problems is crucial in scientific research as it allows for more accurate and reliable predictions and conclusions. A failure to address convergence issues can lead to flawed or misleading results, which can have significant implications in various fields such as medicine, economics, and climate science.

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