In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution is the pointwise product of Fourier transforms. In other words, convolution in one domain (e.g., time domain) equals pointwise multiplication in the other domain (e.g., frequency domain). Versions of the convolution theorem are true for various Fourierrelated transforms. Let and be two functions with convolution . (Note that the asterisk denotes convolution in this context, and not multiplication. The tensor product symbol is sometimes used instead.) Let denote the Fourier transform operator, so and are the Fourier transforms of and , respectively. Then
where denotes pointwise multiplication. It also works the other way around:
By applying the inverse Fourier transform , we can write:
Note that the relationships above are only valid for the form of the Fourier transform shown in the Proof section below. The transform may be normalised in other ways, in which case constant scaling factors (typically or ) will appear in the relationships above.
This theorem also holds for the Laplace transform, the twosided Laplace transform and, when suitably modified, for the Mellin transform and Hartley transform (see Mellin inversion theorem). It can be extended to the Fourier transform of abstract harmonic analysis defined over locally compact abelian groups.
This formulation is especially useful for implementing a numerical convolution on a computer: The standard convolution algorithm has quadratic computational complexity. With the help of the convolution theorem and the fast Fourier transform, the complexity of the convolution can be reduced to O(n log n). This can be exploited to construct fast multiplication algorithms.
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