Placeholder.

The greatest of the integral transforms.

## Misc

I especially need to learn about Fourier transforms of radial functions.

The Fast Fourier Transform (FFT): Most Ingenious Algorithm Ever? is a virtuosic, illustrated, surprisingly deep explanation of some ideas here (specifically the fast fourier transform) by reducible. Also, great animations.

Terry Tao is kind of an expert on this and has some very crisp lecture notes on this idea

## Useful properties

- \(f(x)\,\)
- \(\hat{f}(\nu) = \int f(x) e^{-i \nu x}\, dx\)
- Linearity: \(a\cdot f(x) + b\cdot g(x)\,\)
- \(a\cdot \hat{f}(\nu) + b\cdot \hat{g}(\nu)\,\)
- Time shift \(f(x - a)\,\)
- \(e^{- i a \nu} \hat{f}(\nu)\,\)
- Frequency shift \(f(x)e^{iax}\,\)
- \(\hat{f}(\nu - a)\,\)
- Dilation \(f(a x)\,\)
- \(\frac{1}{|a|} \hat{f}\left( \frac{\nu}{a} \right)\,\)
- Duality \(\hat{f}(x)\,\)
- \(2\pi f(-\nu)\,\)
- \(\frac{d^n f(x)}{dx^n}\,\)
- \((i\nu)^n \hat{f}(\nu)\,\)
- \(x^n f(x)\,\)
- \(i^n \frac{d^n \hat{f}(\nu)}{d\nu^n}\)
- Convolution \((f * g)(x)\,\)
- \(\hat{f}(\nu) \hat{g}(\nu)\,\)
- \(f(x) g(x)\,\)
- \(\frac{1}{2\pi}\left(\hat{f} * \hat{g}\right)(\nu)\,\)
- Hermitian symmetry. For\(f(x)\) purely real
- \(\hat{f}(-\nu) = \overline{\hat{f}(\nu)}\,\)
- For \(f(x)\) purely real and even
- \(f(\nu)\) is purely real even functions.
- For \(f(x)\) purely real and odd
- \(f(\nu)\) are purely imaginary and odd.
- For\(f(x)\) purely imaginary
- \(\hat{f}(-\nu) = -\overline{\hat{f}(\nu)}\,\)
- Complex conjugation \(\overline{f(x)}\)
- \(\overline{\hat{f}(-\nu)}\)
- \(f(x) \cos (a x)\)
- \(\frac{\hat{f}(\nu-a)+\hat{f}(\nu+a)}{2}\)
- \(f(x)\sin( ax)\)
- \(\frac{\hat{f}(\nu-a)-\hat{f}(\nu+a)}{2i}\)
- \(e^{-\alpha x^{2}}\)
- \({\sqrt{\frac {\pi }{\alpha }}}\cdot e^{-{\frac {\nu ^{2}}{4\alpha}}}\)
- \(e^{-i\alpha x^{2}}\)
- \({\sqrt{\frac {\pi }{\alpha }}} \cdot e^{i(\frac {\nu ^{2}}{4\alpha}-\frac {\pi }{4})}\)
- \(f(x)e^{-\alpha x^{2}}\)
- …anything useful?

## Interpolation

Fourier transforms are useful for cheap interpolations, specifically a magical kind of polynomial basis function.

## Enveloped Fourier transforms

If you multiply your function by an *envelope* or *tapering function* or *window* or whatever before taking a Fourier transform, then this is no longer strictly an FT; but I wonder which features we can relate this back to FTs?

This is heavily studied in the context of time frequency analyses, but I have not found a reference for all the results I need, so I will derive a couple of my own here.

## References

*IEEE Transactions on Signal Processing*58 (3): 1157–70. https://doi.org/10.1109/TSP.2009.2033329.

*Quarterly of Applied Mathematics*70 (1): 77–97. https://doi.org/10.1090/S0033-569X-2011-01239-2.

*2017 International Conference on Sampling Theory and Applications (SampTA)*, 82–86. Tallin, Estonia: IEEE. https://doi.org/10.1109/SAMPTA.2017.8024365.

*Journal of Computational and Applied Mathematics*73 (1): 257–70. https://doi.org/10.1016/0377-0427(96)00047-7.

*The Quarterly Journal of Mathematics*12 (1): 165–68. https://doi.org/10.1093/qmath/12.1.165.

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