Integral transforms
In mathematics, an integral transform is any transform T of the following form:
The input of this transform is a function f, and the output is another function Tf. An integral transform is a particular kind of mathematical operator.
There are numerous useful integral transforms. Each is specified by a choice of the function K of two variables, the kernel function, integral kernel or nucleus of the transform.
Some kernels have an associated inverse kernel K−1(u, t) which (roughly speaking) yields an inverse transform:
A symmetric kernel is one that is unchanged when the two variables are permuted.
Mathematical notation aside, the motivation behind integral transforms is easy to understand. There are many classes of problems that are difficult to solve—or at least quite unwieldy algebraically—in their original representations. An integral transform "maps" an equation from its original "domain" into another domain. Manipulating and solving the equation in the target domain can be much easier than manipulation and solution in the original domain. The solution is then mapped back to the original domain with the inverse of the integral transform.
Also there are many applications of probability that rely on integral transforms, such as "pricing kernel" or , or the smoothing of data recovered from robust statistics, see kernel (statistics).
The precursor of the transforms were the Fourier series to express functions in finite intervals. Later the Fourier transform was developed to remove the requirement of finite intervals.
Using the Fourier series, just about any practical function of time (the voltage across the terminals of an electronic device for example) can be represented as a sum of sines and cosines, each suitably scaled (multiplied by a constant factor), shifted (advanced or retarded in time) and "squeezed" or "stretched" (increasing or decreasing the frequency). The sines and cosines in the Fourier series are an example of an orthonormal basis.
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