Paper 4, Section II, E

Numerical Analysis | Part II, 2018

The inverse discrete Fourier transform Fn1:RnRn\mathcal{F}_{n}^{-1}: \mathbb{R}^{n} \rightarrow \mathbb{R}^{n} is given by the formula

x=Fn1y, where x=j=0n1ωnjyj,=0,,n1\boldsymbol{x}=\mathcal{F}_{n}^{-1} \boldsymbol{y}, \quad \text { where } \quad x_{\ell}=\sum_{j=0}^{n-1} \omega_{n}^{j \ell} y_{j}, \quad \ell=0, \ldots, n-1

Here, ωn=exp2πin\omega_{n}=\exp \frac{2 \pi i}{n} is the primitive root of unity of degree nn and n=2p,p=1,2,n=2^{p}, p=1,2, \ldots

(a) Show how to assemble x=F2m1y\boldsymbol{x}=\mathcal{F}_{2 m}^{-1} \boldsymbol{y} in a small number of operations if the Fourier transforms of the even and odd parts of y\boldsymbol{y},

x(E)=Fm1y(E),x(O)=Fm1y(O)\boldsymbol{x}^{(\mathrm{E})}=\mathcal{F}_{m}^{-1} \boldsymbol{y}^{(\mathrm{E})}, \quad \boldsymbol{x}^{(\mathrm{O})}=\mathcal{F}_{m}^{-1} \boldsymbol{y}^{(\mathrm{O})}

are already known.

(b) Describe the Fast Fourier Transform (FFT) method for evaluating x\boldsymbol{x}, and draw a diagram to illustrate the method for n=8n=8.

(c) Find the cost of the FFT method for n=2pn=2^{p} (only multiplications count).

(d) For n=4n=4 use the FFT method to find x=Fn1y\boldsymbol{x}=\mathcal{F}_{n}^{-1} \boldsymbol{y} when: (i) y=(1,1,1,1)\boldsymbol{y}=(1,-1,1,-1), (ii) y=(1,1,1,1)\boldsymbol{y}=(1,1,-1,-1).

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