A weird convolution (recurrence relation)

In summary, the conversation discusses the growth of a sum and integral involving variables B_n and A_n. While it was expected that A_n would grow like n^2, it was found that it actually grows like O(n^5). This is due to the integral including terms proportional to x^5.
  • #1
rsq_a
107
1
The answer doesn't seem obvious to me:

If I set up

[tex]B_0 = 1[/tex] and [tex]B_n = n^2[/tex]

Then let

[tex]A_n = \sum_{m=0}^n B_m B_{n-m} = 2B_0 B_{n} + 2B_1 B_{n-1} + \ldots[/tex]

Then I almost expected [tex]A_n[/tex] to grow like [tex]n^2[/tex]. Instead, I'm getting (numerically) that [tex]A_n \sim O(n^5)[/tex]! Why is that?

In general, it seems that if [tex]B_n \sim O(n^a)[/tex] then [tex]A_n \sim O(n^{2a+1})[/tex]
 
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  • #2
for big m replace the sum by an integral

[tex] \sum_{m=0}^{n}B_{m}B_{n-m}\sym \int_{0}^{x}dtB(t)B(x-t) [/tex]

the integral [tex] \int_{0}^{x}dt(x-t)^{2}t^{2} [/tex] using the Binomial theorem includes the integral of terms [tex]t^{4} [/tex] which is itself proportinal to [tex] x^{5} [/tex] this is the solution to your paradox
 
  • #3
zetafunction said:
for big m replace the sum by an integral

[tex] \sum_{m=0}^{n}B_{m}B_{n-m}\sym \int_{0}^{x}dtB(t)B(x-t) [/tex]

the integral [tex] \int_{0}^{x}dt(x-t)^{2}t^{2} [/tex] using the Binomial theorem includes the integral of terms [tex]t^{4} [/tex] which is itself proportinal to [tex] x^{5} [/tex] this is the solution to your paradox

Hmm...I sort of follow it. For large n, we can compare the growth of the sum to the growth of the integral, which is indeed [tex]O(n^5)[/tex].

It's difficult to see how this arises from the direct sum itself, though.
 

Related to A weird convolution (recurrence relation)

1. What is a convolution?

A convolution is a mathematical operation that combines two functions to produce a third function. It is a type of integral transform that describes how the shape of one function is modified by the other function as it is "slid" across it.

2. How is a convolution related to a recurrence relation?

A recurrence relation is a mathematical equation that defines a sequence of values based on previous values. A convolution can be used to solve a recurrence relation by transforming it into a differential equation, which can then be solved using standard mathematical techniques.

3. Can a convolution be used to solve any recurrence relation?

No, not all recurrence relations can be solved using a convolution. Only linear recurrence relations can be solved using a convolution. Non-linear recurrence relations require other methods for solving, such as iteration or substitution.

4. What are some real-world applications of convolutions?

Convolutions are widely used in signal processing, image processing, and probability theory. They are also used in physics, engineering, and economics for solving differential equations and modeling complex systems.

5. Are there any limitations or drawbacks to using a convolution to solve a recurrence relation?

Yes, convolutions can be computationally expensive and time-consuming for large sequences. They also require prior knowledge of the two functions involved in the convolution, which may not always be available. Additionally, they are only applicable to linear recurrence relations, so other methods must be used for non-linear ones.

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