Gibbs Phenomenon:The maxima get smaller,the minima get bigger

In summary, the conversation discusses the Gibbs Phenomenon for the function $f(x)=sgn(x)$ and its Fourier series. It is shown that the series converges to the constant function $1$ on the interval $[\delta, \pi/2]$ as the number of terms increases, but on the whole interval $[0, \pi/2]$ there is a phenomenon where the first maximum gets higher as the number of terms increases.
  • #1
mathmari
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Hey! :eek:

I am looking at the Gibbs Phenomenon for the function $\displaystyle{f(x)=sgn(x), x \in [-\pi, \pi]}$.

The Fourier series of this function is:

$$sgn(x) \sim s(x)=\sum_{k=1}^{\infty} \frac{4}{(2k-1) \pi} \sin{((2k-1)x)}$$

Since $f$ is odd, it's sufficient to look its behaviour at $[0, \pi]$.

$$s(x)=\frac{4}{\pi}[\sin{(x)}+\frac{\sin{(3x)}}{3}+\frac{\sin{(5x)}}{5}+ \dots]$$

$s(x)$ is even as for $x=\frac{\pi}{2}$, so it's sufficient to look at $[0, \frac{\pi}{2}]$.

$$s_{2n-1}(x)=\frac{4}{\pi}[\sin{(x)}+\frac{\sin{(3x)}}{3}+ \dots +\frac{\sin{((2n-1)x)}}{2n-1}]$$

After calculations, we have that
$$s_{2n-1}'(x)=\frac{2}{\pi} \frac{\sin{(2nx)}}{\sin{(x)}}$$
$$s_{2n-1}'(x)=0 \Rightarrow \sin{(2nx)}=0 \Rightarrow 2nx=m \pi \Rightarrow \\ x=\frac{m \pi}{2n}, m=1, \dots, 2n-1 \ \ \ \text{ : points of extrema }$$

$s_{2n-1}$ has $2n-1$ extrema at $(0, \pi]$

Going from $0$ to $\frac{\pi}{2}$ the maxima get smaller and the minima get bigger.

We can show that $\forall \delta: 0< \delta< \frac{\pi}{2}$, $s_{2n-1}(x)$ converges uniformly to $1$.Could you explain me the last two sentences?? (Wondering)
 
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  • #2
mathmari said:
Going from $0$ to $\frac{\pi}{2}$ the maxima get smaller and the minima get bigger.
The Fourier series has a number of maxima, indicated by the blue dots in the picture, and a number of minima, indicated by the green dots. As $x$ goes from $0$ to $\pi/2$, the blue dots get lower and the green dots get higher. That is what is meant by the maxima getting smaller and the minima getting bigger.

mathmari said:
We can show that $\forall \delta: 0< \delta< \frac{\pi}{2}$, $s_{2n-1}(x)$ converges uniformly to $1$.
As you increase the number of terms in the Fourier series, the first maximum (the highest blue dot) gets higher. When there are very many terms in the Fourier series, it will be about $1.18$. That is what constitutes the Gibbs phenomenon. But if you exclude that initial maximum by looking at the interval $[\delta,\pi/2]$ instead of the whole interval $[0,\pi/2]$, then on that slightly shorter interval the Fourier series converges uniformly to the constant function $1$ as the number of terms in the series increases.
 

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FAQ: Gibbs Phenomenon:The maxima get smaller,the minima get bigger

What is Gibbs Phenomenon?

Gibbs Phenomenon is a mathematical phenomenon that occurs in the Fourier series approximation of a discontinuous function. It is characterized by the oscillations or ripples that appear near the discontinuity, even as the number of terms in the series increases.

Why do the maxima get smaller and the minima get bigger in Gibbs Phenomenon?

This occurs because the Fourier series approximates a discontinuous function with a series of continuous functions, resulting in overshoots and undershoots near the discontinuity. As the number of terms in the series increases, the overshoots and undershoots decrease, resulting in smaller maxima and larger minima.

Is Gibbs Phenomenon a real-world phenomenon?

No, Gibbs Phenomenon is a mathematical phenomenon that occurs in the approximation of discontinuous functions. It does not have any real-world applications or implications.

Can Gibbs Phenomenon be eliminated?

No, Gibbs Phenomenon is a fundamental property of the Fourier series approximation. It cannot be eliminated, but its effects can be reduced by using other approximation methods or by increasing the number of terms in the series.

How does Gibbs Phenomenon affect the accuracy of the Fourier series approximation?

Gibbs Phenomenon can lead to errors in the approximation of a discontinuous function, as the oscillations near the discontinuity do not accurately represent the behavior of the original function. However, as the number of terms in the series increases, the approximation becomes more accurate overall.

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