# Arithmetic complexity of the discrete Fourier transform

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Arithmetic complexity of the discrete Fourier transform

See Fast Fourier transform#Bounds on complexity and operation counts for a general summary of this issue.

Bounds on the multiplicative complexity of FFT

In his PhD thesis in 1987 [1] , Michael Heidman focus on the arithmetic theory of complexity for a Discrete Fourier transform (DFT) and hit upon remarkable results. Among them, a lower bound for the multiplicative (floating-point) complexity required to compute discrete transforms, which is presented below. Let us denote by "M"DFT("N") the minimal multiplicative complexity for the exact computing a DFT of blocklength "N" [2] .

Theorem (Heidman). For a given $N=prod_\left\{i=1\right\}^\left\{m\right\}$ "p"i"e""i" where "p"i, i=1,...,"m" are distinct primes and "e""i", "i" = 1, ..., "m" are positive integers, it follows then$M_\left\{DFT\right\}\left(N\right)=2N-sum_\left\{i_1=0\right\}^\left\{e_1\right\}sum_\left\{i_2=0\right\}^\left\{e_2\right\}ldotssum_\left\{i_m=0\right\}^\left\{e_m\right\}phi\left(operatorname\left\{gcd\right\}\left(prod_\left\{i=1\right\}^\left\{m\right\}p_j^\left\{i_j\right\},4\right)\right).$

$\left(1+sum_\left\{d_1|frac\left\{phi\left(p_1^\left\{i_1\right\}\right)\right\}\left\{phi\left(operatorname\left\{gcd\right\}\left(p_1^\left\{i_1\right\},4\right)sum_\left\{d_2|frac\left\{phi\left(p_2^\left\{i_2\right\}\right)\right\}\left\{phi\left(operatorname\left\{gcd\right\}\left(p_2^\left\{i_2\right\},4\right)ldots sum_\left\{d_m|frac\left\{phi\left(p_m^\left\{i_m\right\}\right)\right\}\left\{phi\left(operatorname\left\{gcd\right\}\left(p_m^\left\{i_m\right\},4\right)frac\left\{prod_\left\{k=1\right\}^\left\{m\right\}phi\left(d_k\right)\right\}\left\{phi \left(lcm\left(d_1,d_2,ldots,d_m\right)\right\}\right)$

where $phi$(.) is the Euler's totient function function, gcd(.,.) denotes the greatest common divisor and lcm(.,.) is the least common multiple. Proof. See [1, page 98] .

The application of this theorem for several values of "N" yields the complexities shown on the table. The difference between pointed complexities is striking. A further point to be observed is the fact that some people believe that Fast Fourier transform (FFT, Cooley-Tukey) is a close-to-optimum algorithm for computing a DFT. This minimal complexity is the same as that one required for the Discrete Hartley transform (DHT) of the same blocklength.

:: "Table I - Minimal multiplicative complexity (expressed as the number of floating-point multiplications) required for computing a DFT for a few selected blocklengths."

Recently, a new fast Fourier transform algorithm was introduced [3,4] , which is based on a multilayer Hadamard decomposition so as to evaluate a DFT via a discrete Hartley transform (DHT), which achieve the minimal floating-point multiplicative complexity for blocklengths until "N" = 24.

References

* [1] M.T. Heidman, "Multiplicative Complexity, Convolution and the DFT", Springer-Verlag, 1988.

* [2] M.T. Heideman and C. Sidney Burrus, 1986, On the number of multiplications necessary to compute a length-2n DFT, "IEEE Trans. Acoust. Speech. Sig. Proc.", vol.34: pp.91-95.

* [3] H.M. de Oliveira, R.J.S. Cintra, R.M.C. Souza, Multilevel Hadamard Decomposition of Discrete Hartley Transforms, In: "Annals of the Brazilian Symposium on Telecommunications", XVIII Simpósio Brasileiro de Telecomunicações, Gramado, RS. Brazil, 2000.

* [4] Ibdem, A Factorization Scheme for Discrete Hartley Transform Matrices, In: International Conference on System Engineering, Comm. and Inform. Technologies, "Proc. of the ICSECIT (Int. Conf. on System Engineering, Comm. and Info. Technol.). "Punta Arenas, 2001. http://www2.ee.ufpe.br/codec/1_01.pdf

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