I understand “transform methods” as recipes, but beyond this they are a big mystery to me.

There are two aspects of them I find bewildering.

One is the sheer number of them. Is there a unified framework that includes all these transforms as special cases?

The second one is heuristic: what would lead anyone to

discoversuch a transform in the course of solving a problem?(My hope is to find a unified treatment of the subject that simultaneously addresses both of these questions.)

**Answer**

The essential idea of many transforms is to change the basis in the space of functions with the hope that in the new basis the problem will simplify.

Let me give a finite-dimensional example. Suppose we have a 2\times2 matrix A and we want to compute A^{1000}. Direct approach would not be very wise. However, if we first *diagonalize* A as PA_dP^{-1} (i.e. rotate the basis by P), the calculation becomes much easier: the answer is given by PA_d^{1000}P^{-1} and computing powers of diagonal matrix is a very simple task.

A somewhat analogous infinite-dimensional example would be the solution of the heat equation u_t=u_{xx} using Fourier transform u(x,t)\rightarrow \hat{u}(\omega,t). The point is that in the Fourier basis the operator \partial_{xx} becomes diagonal: it simply multiplies \hat{u}(\omega,t) by -\omega^2. Therefore, in the new basis, our *partial* differential equation simplifies and becomes *ordinary* differential equation.

In general, the existence of a transform adapted to a particular problem is related to its symmetry. The new basis functions are chosen to be eigenfunctions of the symmetry generators. For instance, in the above PDE example we had translation symmetry with the generator T=-i\partial_x. In the same way, e.g. Mellin transform is related to scaling symmetry, etc.

**Attribution***Source : Link , Question Author : kjo , Answer Author : Start wearing purple*