Proving Summation: $\sum_{n=1}^{\infty}n^{-2}=\frac{\pi^2}{6}

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SUMMARY

The summation $\sum_{n=1}^{\infty}n^{-2}=\frac{\pi^2}{6}$ can be proven using Fourier series, particularly through the expansion of a saw-tooth wave. The mean squared value of the function $y(x) = \pi x$ is calculated directly and via its Fourier series representation, leading to the conclusion that the two methods yield the same result. This establishes the significance of $\pi$ in the context of the Riemann zeta function at 2, known as zeta(2). For further exploration, Robin Chapman’s collection of proofs provides additional insights.

PREREQUISITES
  • Understanding of Fourier series and their applications
  • Familiarity with mean squared value calculations
  • Basic knowledge of the Riemann zeta function
  • Concept of orthogonality in function spaces
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  • Study the derivation of the Fourier series for periodic functions
  • Explore the properties of the Riemann zeta function, particularly zeta(2)
  • Learn about the significance of $\pi$ in mathematical series and integrals
  • Review Robin Chapman's collection of proofs for zeta(2) for diverse methodologies
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[tex]\sum_{n=1}^{\infty}n^{-2}=\frac{\pi^2}{6}[/tex]

I'd like to know how to prove this summation. And if possible, what is the significance of having [itex]\pi[/itex] in the answer?
 
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If you're familar with Fourier series then one nice method is to consider the expansion of a "saw-tooth" wave as follows.

Let [itex]y(x) = \pi x[/itex] : [itex]-0.5 \leq x \leq 0.5[/itex]

Now make it periodic as per [itex]y(x) = y(x-k)[/itex] : [itex]-0.5+k \leq x \leq 0.5+k[/itex], for all integer k.

It's fairly easy to show that the Fourier series expansion is,

[tex]y = \sin(2 \pi x) - \frac{1}{2} \sin(4 \pi x) \, ... \, + \frac{(-1)^{k+1}}{k} \sin(2k \pi x) + \, ...[/tex]

Consider the mean squared value of y, calculated two different ways. Firstly calculate directly from y(x),

[tex]MS(y) = \int_{x=-0.5}^{+0.5} (\pi x)^2 dx = \frac {\pi^2}{12}[/tex]

Now repeating the calculation but this time using the Fourier series (and making use of the fact that the terms are orthagonal) we get,

[tex]MS(y) =0.5 ( 1 + 1/4 + 1/9 + ... 1/k^2 + ... )[/tex]

Equating these two expressions for the mean squared value gives the required sum.
 
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