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# Fast Fourier transform

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 Title: Fast Fourier transform Author: World Heritage Encyclopedia Language: English Subject: Collection: Publisher: World Heritage Encyclopedia Publication Date:

### Fast Fourier transform

A fast Fourier transform (FFT) algorithm computes the discrete Fourier transform (DFT) of a sequence, or its inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. A FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. As a result, it manages to reduce the complexity of computing the DFT from O(n^2), which arises if one simply applies the definition of DFT, to O(n \log n), where n is the data size.

Fast Fourier transforms are widely used for many applications in engineering, science, and mathematics. The basic ideas were popularized in 1965, but some algorithms had been derived as early as 1805. In 1994 Gilbert Strang described the FFT as "the most important numerical algorithm of our lifetime" and it was included in Top 10 Algorithms of 20th Century by the IEEE journal Computing in Science & Engineering.

## Contents

• Overview 1
• History 2
• Definition and speed 3
• Algorithms 4
• Cooley–Tukey algorithm 4.1
• Other FFT algorithms 4.2
• FFT algorithms specialized for real and/or symmetric data 5
• Computational issues 6
• Bounds on complexity and operation counts 6.1
• Approximations 6.2
• Accuracy 6.3
• Multidimensional FFTs 7
• Other generalizations 8
• Applications 9
• Time-Frequency Relationships 10
• Time Domain 10.1
• Frequency Domain 10.2
• Interrelationships between time and frequency domains 10.3
• Research Areas 11
• Implementation with C++ 12
• Meaning of 'negative frequencies' from fast Fourier transforming real valued data 13
• References 15

## Overview

There are many different FFT algorithms involving a wide range of mathematics, from simple complex-number arithmetic to group theory and number theory; this article gives an overview of the available techniques and some of their general properties, while the specific algorithms are described in subsidiary articles linked below.

The DFT is obtained by decomposing a sequence of values into components of different frequencies. This operation is useful in many fields (see discrete Fourier transform for properties and applications of the transform) but computing it directly from the definition is often too slow to be practical. An FFT is a way to compute the same result more quickly: computing the DFT of N points in the naive way, using the definition, takes O(N2) arithmetical operations, while an FFT can compute the same DFT in only O(N log N) operations. The difference in speed can be enormous, especially for long data sets where N may be in the thousands or millions. In practice, the computation time can be reduced by several orders of magnitude in such cases, and the improvement is roughly proportional to N / log(N). This huge improvement made the calculation of the DFT practical; FFTs are of great importance to a wide variety of applications, from digital signal processing and solving partial differential equations to algorithms for quick multiplication of large integers.

The best-known FFT algorithms depend upon the factorization of N, but there are FFTs with O(N log N) complexity for all N, even for prime N. Many FFT algorithms only depend on the fact that e^{-{2\pi i \over N}} is an N-th primitive root of unity, and thus can be applied to analogous transforms over any finite field, such as number-theoretic transforms. Since the inverse DFT is the same as the DFT, but with the opposite sign in the exponent and a 1/N factor, any FFT algorithm can easily be adapted for it.

## History

The development of fast algorithms for DFT can be traced to Gauss's unpublished work in 1805 when he needed it to interpolate the orbit of asteroids Pallas and Juno from sample observations. His method was very similar to the one published in 1965 by Cooley and Tukey, who are generally credited for the invention of the modern generic FFT algorithm. While Gauss's work predated even Fourier's results in 1822, he did not analyze the computation time and eventually used other methods to achieve his goal.

Between 1805 and 1965, some versions of FFT were published by other authors. Yates in 1932 published his version called interaction algorithm, which provided efficient computation of Hadamard and Walsh transforms. Yates' algorithm is still used in the field of statistical design and analysis of experiments. In 1942, Danielson and Lanczos published their version to compute DFT for x-ray crystallography, a field where calculation of Fourier transforms presented a formidable bottleneck. While many methods in the past had focused on reducing the constant factor for O(n^2) computation by taking advantage of symmetries, Danielson and Lanczos realized that one could use the periodicity and apply a "doubling trick" to get O(n \log n) runtime.

Cooley and Tukey published a more general version of FFT in 1965 that is applicable when N is composite and not necessarily a power of 2. Tukey came up with the idea during a meeting of President Kennedy’s Science Advisory Committee where a discussion topic involved detecting nuclear tests by the Soviet Union by setting up sensors to surround the country from outside. To analyze the output of these sensors, a fast Fourier transform algorithm would be needed. Tukey's idea was taken by Richard Garwin and given to Cooley (both worked at IBM's Watson labs) for implementation while hiding the original purpose from him for security reasons. The pair published the paper in a relatively short six months. As Tukey didn't work at IBM, the patentability of the idea was doubted and the algorithm went into the public domain, which, through the computing revolution of the next decade, made FFT one of the indispensable algorithms in digital signal processing.

## Definition and speed

An FFT computes the DFT and produces exactly the same result as evaluating the DFT definition directly; the most important difference is that an FFT is much faster. (In the presence of round-off error, many FFT algorithms are also much more accurate than evaluating the DFT definition directly, as discussed below.)

Let x0, ...., xN-1 be complex numbers. The DFT is defined by the formula

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

Evaluating this definition directly requires O(N2) operations: there are N outputs Xk, and each output requires a sum of N terms. An FFT is any method to compute the same results in O(N log N) operations. More precisely, all known FFT algorithms require Θ(N log N) operations (technically, O only denotes an upper bound), although there is no known proof that a lower complexity score is impossible.(Johnson and Frigo, 2007)

To illustrate the savings of an FFT, consider the count of complex multiplications and additions. Evaluating the DFT's sums directly involves N2 complex multiplications and N(N−1) complex additions [of which O(N) operations can be saved by eliminating trivial operations such as multiplications by 1]. The well-known radix-2 Cooley–Tukey algorithm, for N a power of 2, can compute the same result with only (N/2)log2(N) complex multiplications (again, ignoring simplifications of multiplications by 1 and similar) and Nlog2(N) complex additions. In practice, actual performance on modern computers is usually dominated by factors other than the speed of arithmetic operations and the analysis is a complicated subject (see, e.g., Frigo & Johnson, 2005), but the overall improvement from O(N2) to O(N log N) remains.

## Algorithms

### Cooley–Tukey algorithm

By far the most commonly used FFT is the Cooley–Tukey algorithm. This is a divide and conquer algorithm that recursively breaks down a DFT of any composite size N = N1N2 into many smaller DFTs of sizes N1 and N2, along with O(N) multiplications by complex roots of unity traditionally called twiddle factors (after Gentleman and Sande, 1966).

This method (and the general idea of an FFT) was popularized by a publication of J. W. Cooley and J. W. Tukey in 1965, but it was later discovered that those two authors had independently re-invented an algorithm known to Carl Friedrich Gauss around 1805 (and subsequently rediscovered several times in limited forms).

The best known use of the Cooley–Tukey algorithm is to divide the transform into two pieces of size N/2 at each step, and is therefore limited to power-of-two sizes, but any factorization can be used in general (as was known to both Gauss and Cooley/Tukey). These are called the radix-2 and mixed-radix cases, respectively (and other variants such as the split-radix FFT have their own names as well). Although the basic idea is recursive, most traditional implementations rearrange the algorithm to avoid explicit recursion. Also, because the Cooley–Tukey algorithm breaks the DFT into smaller DFTs, it can be combined arbitrarily with any other algorithm for the DFT, such as those described below.

### Other FFT algorithms

There are other FFT algorithms distinct from Cooley–Tukey.

Cornelius Lanczos did pioneering work on the FFS and FFT with G.C. Danielson (1940).

For N = N1N2 with coprime N1 and N2, one can use the Prime-Factor (Good-Thomas) algorithm (PFA), based on the Chinese Remainder Theorem, to factorize the DFT similarly to Cooley–Tukey but without the twiddle factors. The Rader-Brenner algorithm (1976) is a Cooley–Tukey-like factorization but with purely imaginary twiddle factors, reducing multiplications at the cost of increased additions and reduced numerical stability; it was later superseded by the split-radix variant of Cooley–Tukey (which achieves the same multiplication count but with fewer additions and without sacrificing accuracy). Algorithms that recursively factorize the DFT into smaller operations other than DFTs include the Bruun and QFT algorithms. (The Rader-Brenner and QFT algorithms were proposed for power-of-two sizes, but it is possible that they could be adapted to general composite n. Bruun's algorithm applies to arbitrary even composite sizes.) Bruun's algorithm, in particular, is based on interpreting the FFT as a recursive factorization of the polynomial zN−1, here into real-coefficient polynomials of the form zM−1 and z2M + azM + 1.

Another polynomial viewpoint is exploited by the Winograd algorithm, which factorizes zN−1 into cyclotomic polynomials—these often have coefficients of 1, 0, or −1, and therefore require few (if any) multiplications, so Winograd can be used to obtain minimal-multiplication FFTs and is often used to find efficient algorithms for small factors. Indeed, Winograd showed that the DFT can be computed with only O(N) irrational multiplications, leading to a proven achievable lower bound on the number of multiplications for power-of-two sizes; unfortunately, this comes at the cost of many more additions, a tradeoff no longer favorable on modern processors with hardware multipliers. In particular, Winograd also makes use of the PFA as well as an algorithm by Rader for FFTs of prime sizes.

Rader's algorithm, exploiting the existence of a generator for the multiplicative group modulo prime N, expresses a DFT of prime size n as a cyclic convolution of (composite) size N−1, which can then be computed by a pair of ordinary FFTs via the convolution theorem (although Winograd uses other convolution methods). Another prime-size FFT is due to L. I. Bluestein, and is sometimes called the chirp-z algorithm; it also re-expresses a DFT as a convolution, but this time of the same size (which can be zero-padded to a power of two and evaluated by radix-2 Cooley–Tukey FFTs, for example), via the identity nk = -(k-n)^2/2 + n^2/2 + k^2/2.

## FFT algorithms specialized for real and/or symmetric data

In many applications, the input data for the DFT are purely real, in which case the outputs satisfy the symmetry

X_{N-k} = X_k^*

and efficient FFT algorithms have been designed for this situation (see e.g. Sorensen, 1987). One approach consists of taking an ordinary algorithm (e.g. Cooley–Tukey) and removing the redundant parts of the computation, saving roughly a factor of two in time and memory. Alternatively, it is possible to express an even-length real-input DFT as a complex DFT of half the length (whose real and imaginary parts are the even/odd elements of the original real data), followed by O(N) post-processing operations.

It was once believed that real-input DFTs could be more efficiently computed by means of the discrete Hartley transform (DHT), but it was subsequently argued that a specialized real-input DFT algorithm (FFT) can typically be found that requires fewer operations than the corresponding DHT algorithm (FHT) for the same number of inputs. Bruun's algorithm (above) is another method that was initially proposed to take advantage of real inputs, but it has not proved popular.

There are further FFT specializations for the cases of real data that have even/odd symmetry, in which case one can gain another factor of (roughly) two in time and memory and the DFT becomes the discrete cosine/sine transform(s) (DCT/DST). Instead of directly modifying an FFT algorithm for these cases, DCTs/DSTs can also be computed via FFTs of real data combined with O(N) pre/post processing.

## Computational issues

### Bounds on complexity and operation counts Open problem in computer science: What is the lower bound on the complexity of fast Fourier transform algorithms? Can they be faster than Θ(N log N)? (more open problems in computer science)

A fundamental question of longstanding theoretical interest is to prove lower bounds on the complexity and exact operation counts of fast Fourier transforms, and many open problems remain. It is not even rigorously proved whether DFTs truly require Ω(N log(N)) (i.e., order N log(N) or greater) operations, even for the simple case of power of two sizes, although no algorithms with lower complexity are known. In particular, the count of arithmetic operations is usually the focus of such questions, although actual performance on modern-day computers is determined by many other factors such as cache or CPU pipeline optimization.

Following pioneering work by Winograd (1978), a tight Θ(N) lower bound is known for the number of real multiplications required by an FFT. It can be shown that only 4N-2\log_2^{2}N-2\log_2 N-4 irrational real multiplications are required to compute a DFT of power-of-two length N=2^m. Moreover, explicit algorithms that achieve this count are known (Heideman & Burrus, 1986; Duhamel, 1990). Unfortunately, these algorithms require too many additions to be practical, at least on modern computers with hardware multipliers (Duhamel, 1990; Frigo & Johnson, 2005).

A tight lower bound is not known on the number of required additions, although lower bounds have been proved under some restrictive assumptions on the algorithms. In 1973, Morgenstern proved an Ω(N log(N)) lower bound on the addition count for algorithms where the multiplicative constants have bounded magnitudes (which is true for most but not all FFT algorithms). This result, however, applies only to the unnormalized Fourier transform (which is a scaling of a unitary matrix by a factor of \sqrt N), and does not explain why the Fourier matrix is harder to compute than any other unitary matrix (including the identity matrix) under the same scaling. Pan (1986) proved an Ω(N log(N)) lower bound assuming a bound on a measure of the FFT algorithm's "asynchronicity", but the generality of this assumption is unclear. For the case of power-of-two N, Papadimitriou (1979) argued that the number N \log_2 N of complex-number additions achieved by Cooley–Tukey algorithms is optimal under certain assumptions on the graph of the algorithm (his assumptions imply, among other things, that no additive identities in the roots of unity are exploited). (This argument would imply that at least 2 N \log_2 N real additions are required, although this is not a tight bound because extra additions are required as part of complex-number multiplications.) Thus far, no published FFT algorithm has achieved fewer than N \log_2 N complex-number additions (or their equivalent) for power-of-two N.

A third problem is to minimize the total number of real multiplications and additions, sometimes called the "arithmetic complexity" (although in this context it is the exact count and not the asymptotic complexity that is being considered). Again, no tight lower bound has been proven. Since 1968, however, the lowest published count for power-of-two N was long achieved by the split-radix FFT algorithm, which requires 4N\log_2 N-6N+8 real multiplications and additions for N > 1. This was recently reduced to \sim \frac{34}{9} N \log_2 N (Johnson and Frigo, 2007; Lundy and Van Buskirk, 2007). A slightly larger count (but still better than split radix for N≥256) was shown to be provably optimal for N≤512 under additional restrictions on the possible algorithms (split-radix-like flowgraphs with unit-modulus multiplicative factors), by reduction to a Satisfiability Modulo Theories problem solvable by brute force (Haynal & Haynal, 2011).

Most of the attempts to lower or prove the complexity of FFT algorithms have focused on the ordinary complex-data case, because it is the simplest. However, complex-data FFTs are so closely related to algorithms for related problems such as real-data FFTs, discrete cosine transforms, discrete Hartley transforms, and so on, that any improvement in one of these would immediately lead to improvements in the others (Duhamel & Vetterli, 1990).

### Approximations

All of the FFT algorithms discussed above compute the DFT exactly (in exact arithmetic, i.e. neglecting floating-point errors). A few "FFT" algorithms have been proposed, however, that compute the DFT approximately, with an error that can be made arbitrarily small at the expense of increased computations. Such algorithms trade the approximation error for increased speed or other properties. For example, an approximate FFT algorithm by Edelman et al. (1999) achieves lower communication requirements for parallel computing with the help of a fast multipole method. A wavelet-based approximate FFT by Guo and Burrus (1996) takes sparse inputs/outputs (time/frequency localization) into account more efficiently than is possible with an exact FFT. Another algorithm for approximate computation of a subset of the DFT outputs is due to Shentov et al. (1995). The Edelman algorithm works equally well for sparse and non-sparse data, since it is based on the compressibility (rank deficiency) of the Fourier matrix itself rather than the compressibility (sparsity) of the data. Conversely, if the data are sparse—that is, if only K out of N Fourier coefficients are nonzero—then the complexity can be reduced to O(Klog(N)log(N/K)), and this has been demonstrated to lead to practical speedups compared to an ordinary FFT for N/K>32 in a large-N example (N=222) using a probabilistic approximate algorithm (which estimates the largest K coefficients to several decimal places).

### Accuracy

Even the "exact" FFT algorithms have errors when finite-precision floating-point arithmetic is used, but these errors are typically quite small; most FFT algorithms, e.g. Cooley–Tukey, have excellent numerical properties as a consequence of the pairwise summation structure of the algorithms. The upper bound on the relative error for the Cooley–Tukey algorithm is O(ε log N), compared to O(εN3/2) for the naïve DFT formula, where ε is the machine floating-point relative precision. In fact, the root mean square (rms) errors are much better than these upper bounds, being only O(ε √log N) for Cooley–Tukey and O(ε √N) for the naïve DFT (Schatzman, 1996). These results, however, are very sensitive to the accuracy of the twiddle factors used in the FFT (i.e. the trigonometric function values), and it is not unusual for incautious FFT implementations to have much worse accuracy, e.g. if they use inaccurate trigonometric recurrence formulas. Some FFTs other than Cooley–Tukey, such as the Rader-Brenner algorithm, are intrinsically less stable.

In fixed-point arithmetic, the finite-precision errors accumulated by FFT algorithms are worse, with rms errors growing as O(√N) for the Cooley–Tukey algorithm (Welch, 1969). Moreover, even achieving this accuracy requires careful attention to scaling to minimize loss of precision, and fixed-point FFT algorithms involve rescaling at each intermediate stage of decompositions like Cooley–Tukey.

To verify the correctness of an FFT implementation, rigorous guarantees can be obtained in O(Nlog(N)) time by a simple procedure checking the linearity, impulse-response, and time-shift properties of the transform on random inputs (Ergün, 1995).

## Multidimensional FFTs

As defined in the multidimensional DFT article, the multidimensional DFT

X_\mathbf{k} = \sum_{\mathbf{n}=0}^{\mathbf{N}-1} e^{-2\pi i \mathbf{k} \cdot (\mathbf{n} / \mathbf{N})} x_\mathbf{n}

transforms an array xn with a d-dimensional vector of indices \mathbf{n}=(n_1, \ldots, n_d) by a set of d nested summations (over n_j = 0 \ldots N_j-1 for each j), where the division n/N, defined as \mathbf{n} / \mathbf{N} = (n_1/N_1, \ldots, n_d/N_d), is performed element-wise. Equivalently, it is the composition of a sequence of d sets of one-dimensional DFTs, performed along one dimension at a time (in any order).

This compositional viewpoint immediately provides the simplest and most common multidimensional DFT algorithm, known as the row-column algorithm (after the two-dimensional case, below). That is, one simply performs a sequence of d one-dimensional FFTs (by any of the above algorithms): first you transform along the n1 dimension, then along the n2 dimension, and so on (or actually, any ordering works). This method is easily shown to have the usual O(Nlog(N)) complexity, where N = N_1 \cdot N_2 \cdot \ldots \cdot N_d is the total number of data points transformed. In particular, there are N/N1 transforms of size N1, etcetera, so the complexity of the sequence of FFTs is:

\begin{align} & {} \qquad \frac{N}{N_1} O(N_1 \log N_1) + \cdots + \frac{N}{N_d} O(N_d \log N_d) \\[6pt] & = O\left(N \left[\log N_1 + \cdots + \log N_d\right]\right) = O(N \log N). \end{align}

In two dimensions, the xk can be viewed as an n_1 \times n_2 matrix, and this algorithm corresponds to first performing the FFT of all the rows (resp. columns), grouping the resulting transformed rows (resp. columns) together as another n_1 \times n_2 matrix, and then performing the FFT on each of the columns (resp. rows) of this second matrix, and similarly grouping the results into the final result matrix.

In more than two dimensions, it is often advantageous for cache locality to group the dimensions recursively. For example, a three-dimensional FFT might first perform two-dimensional FFTs of each planar "slice" for each fixed n1, and then perform the one-dimensional FFTs along the n1 direction. More generally, an asymptotically optimal cache-oblivious algorithm consists of recursively dividing the dimensions into two groups (n_1, \ldots, n_{d/2}) and (n_{d/2+1}, \ldots, n_d) that are transformed recursively (rounding if d is not even) (see Frigo and Johnson, 2005). Still, this remains a straightforward variation of the row-column algorithm that ultimately requires only a one-dimensional FFT algorithm as the base case, and still has O(Nlog(N)) complexity. Yet another variation is to perform matrix transpositions in between transforming subsequent dimensions, so that the transforms operate on contiguous data; this is especially important for out-of-core and distributed memory situations where accessing non-contiguous data is extremely time-consuming.

There are other multidimensional FFT algorithms that are distinct from the row-column algorithm, although all of them have O(Nlog(N)) complexity. Perhaps the simplest non-row-column FFT is the vector-radix FFT algorithm, which is a generalization of the ordinary Cooley–Tukey algorithm where one divides the transform dimensions by a vector \mathbf{r}=(r_1, r_2, \ldots, r_d) of radices at each step. (This may also have cache benefits.) The simplest case of vector-radix is where all of the radices are equal (e.g. vector-radix-2 divides all of the dimensions by two), but this is not necessary. Vector radix with only a single non-unit radix at a time, i.e. \mathbf{r}=(1, \ldots, 1, r, 1, \ldots, 1), is essentially a row-column algorithm. Other, more complicated, methods include polynomial transform algorithms due to Nussbaumer (1977), which view the transform in terms of convolutions and polynomial products. See Duhamel and Vetterli (1990) for more information and references.

## Other generalizations

An O(N5/2log(N)) generalization to spherical harmonics on the sphere S2 with N2 nodes was described by Mohlenkamp, along with an algorithm conjectured (but not proven) to have O(N2 log2(N)) complexity; Mohlenkamp also provides an implementation in the libftsh library. A spherical-harmonic algorithm with O(N2log(N)) complexity is described by Rokhlin and Tygert.

The Fast Folding Algorithm is analogous to the FFT, except that it operates on a series of binned waveforms rather than a series of real or complex scalar values. Rotation (which in the FFT is multiplication by a complex phasor) is a circular shift of the component waveform.

Various groups have also published "FFT" algorithms for non-equispaced data, as reviewed in Potts et al. (2001). Such algorithms do not strictly compute the DFT (which is only defined for equispaced data), but rather some approximation thereof (a non-uniform discrete Fourier transform, or NDFT, which itself is often computed only approximately). More generally there are various other methods of spectral estimation.

## Applications

FFT's importance derives from the fact that in signal processing and image processing it has made working in frequency domain equally computationally feasible as working in temporal or spatial domain. Some of the important applications of FFT includes,

## Time-Frequency Relationships

[Note: the terms “complex data” (= real and imaginary numbers) and “in-phase/quadrature data” (I/Q numbers) are used interchangeably].

### Time Domain

The waveform is sampled at \bold N equi-spaced points in time n = 0, 1,......., N-1
(It is preferable for N to be a power of two).
When the sampled waveform is real, the data consists of N real numbers—but if the sampled waveform is complex, the data consists of N sample-pairs of I/Q data.
If the time interval between samples (the "sample interval") is \bold \Delta \bold t secs, then
the total length of the sample record is \bold T , where

                    \bold T = \bold N. \bold \Delta \bold t   secs


### Frequency Domain

For real waveform data, the number of frequency points, ignoring the zero frequency bin, is N/2 +1. [i.e.there are N/2 +1 pairs of (I/Q) data].
For complex (I/Q) waveform data, the number of frequency points = N, with N/2 pairs of complex data at positive frequencies and N/2 pairs of complex data at negative frequencies.
If the frequency separation of the data points, (the “frequency resolution”) is \bold \Delta \bold f Hz, then
the maximum frequency of the display (the “bandwidth”) is \bold + \bold F_{max} , for real numbers, and \bold \pm \bold F_{max} for complex data, where

                    \bold F_{max} \bold = \bold \frac{N}{2}. \bold \Delta \bold f  Hz


### Interrelationships between time and frequency domains

                    \bold \Delta \bold t = \bold \frac{1}{2.F_{max}}  secs

\bold \Delta \bold f = \bold \frac{1}{T}  Hz


## Research Areas

• Big FFTs: With explosion of big data in fields such as astronomy, the need for 512k FFTs has arisen for certain interferometry calculations. The data collected by projects such as MAP and LIGO require FFTs of tens of billions of points. As this size does not fit in to main memory, so called out-of-core FFTs are an active area of research.
• Approximate FFTs: For applications such as MRI, it is necessary to compute DFTs for nonuniformly spaced grid points and/or frequencies. Multipole based approaches can compute approximate quantities with factor of runtime increase.
• Group FFTs: The FFT may also be explained and interpreted using group representation theory that allows for further generalization. A function on any compact group, including non cyclic, has an expansion in terms of a basis of irreducible matrix elements. It remains active area of research to find efficient algorithm for performing this change of basis. Applications including efficient spherical harmonic expansion, analyzing certain markov processes, robotics etc.
• Quantum FFTs: Shor's fast algorithm for integer factorization on a quantum computer has a subroutine to compute DFT of a binary vector. This is implemented as sequence of 1- or 2-bit quantum gates now known as quantum FFT, which is effectively the Cooley-Tukey FFT realized as a particular factorization of the Fourier matrix. Extension to these ideas is currently being explored.

## Implementation with C++

Here is a C++ source code for a simple FFT implementation:


const double TwoPi = 6.283185307179586;

void FFTAnalysis(double *AVal, double *FTvl, int Nvl, int Nft) {
int i, j, n, m, Mmax, Istp;
double Tmpr, Tmpi, Wtmp, Theta;
double Wpr, Wpi, Wr, Wi;
double *Tmvl;

n = Nvl * 2; Tmvl = new double[n+1];

for (i = 0; i  i) {
Tmpr = Tmvl[i]; Tmvl[i] = Tmvl[j]; Tmvl[j] = Tmpr;
Tmpr = Tmvl[i+1]; Tmvl[i+1] = Tmvl[j+1]; Tmvl[j+1] = Tmpr;
}
i = i + 2; m = Nvl;
while ((m>= 2) && (j> m)) {
j = j - m; m = m>> 2;
}
j = j + m;
}

Mmax = 2;
while (n> Mmax) {
Theta = -TwoPi / Mmax; Wpi = Sin(Theta);
Wtmp = Sin(Theta / 2); Wpr = Wtmp * Wtmp * 2;
Istp = Mmax * 2; Wr = 1; Wi = 0; m = 1;

while (m

Meaning of 'negative frequencies' from fast Fourier transforming real valued data

The majority of FFT algorithms result in 'negative frequencies' from Fourier transforming real numbers. The negative frequencies encompass half of the resulting numbers in the transform and provide no new information, since they are conjugate pairs of the positive frequencies. In contrast, Fourier transforming numbers with real and imaginary components does not result in a transformed set of numbers with such a redundancy.

The meaning of these negative frequencies resulting from the Fourier transform of real valued data can be intuited through the DFT matrix. Briefly, the DFT matrix performs a Fourier transform on a set of numbers as a matrix multiplication. The matrix works based on the orthogonality of sinusoidal functions, the real-world analogue of which is a lock-in amplifier. When a sinusoidal function of frequency f1 is multiplied by another sinusoidal function of frequency f2 not equal to f1 and integrated, the result is zero. Instead, when f1 is equal to f2, the average value is equal to half of the product of the amplitudes. The DFT matrix is a matrix of sinusoidal frequencies with an amplitude of 1, with the first row being 0Hz, up to the final row being a sinusoid with N-1 periods (the fastest encodeable frequency with slightly more than one datapoint per period). The phase of f2 is encoded in the real and imaginary component of the transformed value.

That the DFT matrix transforms frequencies with less than 2 samples per period might be viewed as being in violation of the Nyquist–Shannon sampling theorem, which demands that at least two samples per period are necessary to resolve frequencies from a dataset. While this requirement is true for real-valued data which encodes amplitude at every point, complex valued data encodes both amplitude and phase at every point. This two-part complex value conveys twice as much information as a single real-valued datapoint, and as little as one sample per period is necessary to unambiguously resolve frequencies from a complex valued array of numbers.

Despite that real-valued data cannot resolve frequencies with less than 2 samples per period, common Fourier transform algorithms (and the DFT matrix) will still attempt to resolve these frequencies. The result of trying to recover frequencies with less than 2 real valued datapoints is that nonsense 'negative frequencies' are generated in the transform. For example, a sine wave sampled 8 times per period results in a dataset that is identical to a sine wave sampled 8/7 times per period. The Fourier transform will attempt to resolve both of these frequencies, and attempting to recover a waveform sampled 8/7 times per period results in a 'negative frequency' conjugate.

Cooley–Tukey FFT algorithm

Prime-factor FFT algorithm

Bruun's FFT algorithm

Bluestein's FFT algorithm

Butterfly diagram – a diagram used to describe FFTs.

Odlyzko–Schönhage algorithm applies the FFT to finite Dirichlet series.

Overlap add/Overlap save – efficient convolution methods using FFT for long signals

Spectral music (involves application of FFT analysis to musical composition)

Spectrum analyzers – Devices that perform an FFT

FFTW "Fastest Fourier Transform in the West" – C library for the discrete Fourier transform (DFT) in one or more dimensions.

FFTS – The Fastest Fourier Transform in the South.

FFTPACK – another Fortran FFT library (public domain)

Goertzel algorithm – Computes individual terms of discrete Fourier transform

Time series

Math Kernel Library

Generalized distributive law

Multidimensional transform