Linked from

The 14 pages that link to Fast Fourier transform, each with the reason it gives.

• Fourier transformBroader topic: It accelerates practical calculations but is an algorithm, not a different transform.

• Fourier seriesCompared with: It serves the computational role of frequency decomposition for finite sampled sequences.

• Fourier analysisRelated: It makes large-scale discrete frequency analysis computationally practical.

• Chinese remainder theoremRelated: Number-theoretic transforms use modular arithmetic and sometimes recombine residues by this theorem.

• MultiplicationRelated: Polynomial multiplication can be accelerated by transforming coefficients, multiplying pointwise, and transforming back.

• Root of unityBroader topic: Its factorization exploits identities among roots of unity.

• Discrete Fourier transformRelated: It exploits symmetries in the DFT to reduce computation from quadratic to roughly n log n.

• Harmonic analysisBroader topic: It makes frequency analysis computationally practical for large datasets.

• Chebyshev polynomialsRelated: Chebyshev coefficients can be computed efficiently by transforming sampled function values.

• Orthogonal frequency-division multiplexingRelated: Practical OFDM transmitters and receivers use FFT algorithms to process subcarriers.

• Fourier coefficientRelated: It makes large collections of frequency coefficients practical to calculate.

• Mersenne primeRelated: Large-prime searches use fast multiplication techniques built around Fourier transforms.

• Modular exponentiationCompared with: It accelerates structured polynomial multiplication, while modular exponentiation accelerates repeated powers.

• Fourier analysis on finite groupsCompared with: Fast transform algorithms extend Fourier computation to suitable finite groups.