Bruun's FFT Algorithm - A Polynomial Approach To The DFT

A Polynomial Approach To The DFT

Recall that the DFT is defined by the formula:

X_k = \sum_{n=0}^{N-1} x_n e^{-\frac{2\pi i}{N} nk } \qquad k = 0,\dots,N-1.

For convenience, let us denote the N roots of unity by ωNn (n = 0, ..., N − 1):

and define the polynomial x(z) whose coefficients are xn:

The DFT can then be understood as a reduction of this polynomial; that is, Xk is given by:

where mod denotes the polynomial remainder operation. The key to fast algorithms like Bruun's or Cooley–Tukey comes from the fact that one can perform this set of N remainder operations in recursive stages.

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