Quick convergent series question.

In summary, the series 2^n + n!/4^n diverges due to the vanishing condition and the ratio test, even though one part of the series converges. This is because the existence of a convergent part does not affect the divergence of the overall series.
  • #1
binbagsss
1,277
11
2^n + n!/4^n ?
so by the 'vanishing condition' as n!/4^n does not ---> 0 as n --> infinity, this part of the series diverges.
however (e.g via the ratio test 2^n/4^n converges).
My book concludes that due to part a, the entire series divegres. However I am struggling to see how this justifies that the whole series will diverge - I thought that if part of a series diverges and part diverges , than what the whole series does depends upon the exact convergence and divergence - i.e could diverge or converge?
 
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  • #2
The whole series will diverge by comparison

n!/4^n<(2^n + n!)/4^n

If the parts were of opposite sign there would be the possibility of convergence.
 
  • #3
binbagsss said:
2^n + n!/4^n ?
so by the 'vanishing condition' as n!/4^n does not ---> 0 as n --> infinity, this part of the series diverges.
however (e.g via the ratio test 2^n/4^n converges).
My book concludes that due to part a, the entire series divegres. However I am struggling to see how this justifies that the whole series will diverge - I thought that if part of a series diverges and part diverges , than what the whole series does depends upon the exact convergence and divergence - i.e could diverge or converge?

Looking at what you wrote, which is
[tex] 2^n + \frac{n!}{4^n},[/tex]
we have, of course, that both parts diverge. However, assuming you meant
[tex] \frac{2^n + n!}{4^n}, [/tex]
which, for some reason you did not want to write as (2^n + n!)/4^n, then you are correct: one "part" converges and the other diverges. However, that *automatically* means that the total diverges. Suppose we have two parts are ##t_1(n)## and ##t_2(n)## and that##\sum t_1(n)## converges while ##\sum t_2(n)## diverges. We have, for finite N:
[tex] S(N) = \sum_{n=1}^N (t_1(n) + t_2(n)) = S_1(N) + S_2(N), [/tex] where the ##S_i(N)## are the partial sums, and our assumptions are that
[tex] \lim_{N \to \infty} S_1(N) = s_1 [/tex] exists and is finite, while
[tex] \lim_{N \to \infty} S_2(N) [/tex] does not exist (that is, it is either ##\pm \infty## or else does not exist at all, due to oscillations, etc). These two statements imply that S(N) does not have a finite limit as N → ∞, so the total series diverges.
 

FAQ: Quick convergent series question.

1. What is a quick convergent series?

A quick convergent series is a mathematical series where the terms of the series approach a finite limit quickly.

2. How do you determine if a series is quickly convergent?

A series is considered quickly convergent if it satisfies the Cauchy criterion, which states that for any positive number ε, there exists a positive integer N such that the absolute value of the difference between the sum of the terms up to n and the sum of the terms up to m is less than ε for all n and m greater than N.

3. What is the significance of quick convergence in mathematics?

Quick convergence is important in mathematics because it allows us to determine the sum of an infinite series with a high degree of accuracy using a finite number of terms. This can be useful in various real-world applications, such as computing probabilities and approximating functions.

4. Can a series be quickly convergent and divergent at the same time?

No, a series cannot be both quickly convergent and divergent. A quickly convergent series has a finite limit, while a divergent series does not. However, a series can be quickly convergent and conditionally convergent at the same time.

5. Are there any applications of quick convergence outside of mathematics?

Yes, quick convergence has applications in various fields such as physics, engineering, and economics. In physics, it is used to analyze the behavior of electric circuits and to approximate solutions to differential equations. In engineering, it is used in signal processing and control systems. In economics, it is used to analyze the stability of economic systems and to approximate solutions to economic models.

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