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  2. Cooley–Tukey FFT algorithm - Wikipedia

    en.wikipedia.org/wiki/Cooley–Tukey_FFT_algorithm

    The Cooley–Tukey algorithm, named after J. W. Cooley and John Tukey, is the most common fast Fourier transform (FFT) algorithm. It re-expresses the discrete Fourier transform (DFT) of an arbitrary composite size = in terms of N 1 smaller DFTs of sizes N 2, recursively, to reduce the computation time to O(N log N) for highly composite N (smooth numbers).

  3. Schönhage–Strassen algorithm - Wikipedia

    en.wikipedia.org/wiki/Schönhage–Strassen...

    The run-time bit complexity to multiply two n-digit numbers using the algorithm is (⁡ ⁡ ⁡) in big O notation. The Schönhage–Strassen algorithm was the asymptotically fastest multiplication method known from 1971 until 2007.

  4. Fast Fourier transform - Wikipedia

    en.wikipedia.org/wiki/Fast_Fourier_transform

    The development of fast algorithms for DFT can be traced to Carl Friedrich Gauss's unpublished 1805 work on the orbits of asteroids Pallas and Juno.Gauss wanted to interpolate the orbits from sample observations; [6] [7] his method was very similar to the one that would be published in 1965 by James Cooley and John Tukey, who are generally credited for the invention of the modern generic FFT ...

  5. Butterfly diagram - Wikipedia

    en.wikipedia.org/wiki/Butterfly_diagram

    Signal-flow graph connecting the inputs x (left) to the outputs y that depend on them (right) for a "butterfly" step of a radix-2 Cooley–Tukey FFT. This diagram resembles a butterfly (as in the morpho butterfly shown for comparison), hence the name, although in some countries it is also called the hourglass diagram.

  6. Split-radix FFT algorithm - Wikipedia

    en.wikipedia.org/wiki/Split-radix_FFT_algorithm

    The split-radix FFT is a fast Fourier transform (FFT) algorithm for computing the discrete Fourier transform (DFT), and was first described in an initially little-appreciated paper by R. Yavne (1968) and subsequently rediscovered simultaneously by various authors in 1984.

  7. Chirp spectrum - Wikipedia

    en.wikipedia.org/wiki/Chirp_spectrum

    The FFT process assumes the waveform is cyclic, so these 128 data points can be considered to be part of an endlessly repeating sequence in time. Linear chirp with TB=25 and N=128. By calculating the N-point FFT of this data, the discrete spectrum of the sequence is obtained.

  8. Bit-reversal permutation - Wikipedia

    en.wikipedia.org/wiki/Bit-reversal_permutation

    Bit-reversal permutations are often used in finding lower bounds on dynamic data structures. For example, subject to certain assumptions, the cost of looking up the integers between 0 {\displaystyle 0} and n − 1 {\displaystyle n-1} , inclusive, in any binary search tree holding those values, is Ω ( n log ⁡ n ) {\displaystyle \Omega (n\log ...

  9. Bruun's FFT algorithm - Wikipedia

    en.wikipedia.org/wiki/Bruun's_FFT_algorithm

    Bruun's algorithm is a fast Fourier transform (FFT) algorithm based on an unusual recursive polynomial-factorization approach, proposed for powers of two by G. Bruun in 1978 and generalized to arbitrary even composite sizes by H. Murakami in 1996.