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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. An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors.〔Charles Van Loan, ''Computational Frameworks for the Fast Fourier Transform'' (SIAM, 1992).〕 As a result, it manages to reduce the complexity of computing the DFT from , which arises if one simply applies the definition of DFT, to , where 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. ==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(''N''2) 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 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「fast fourier transform」の詳細全文を読む スポンサード リンク
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