TITLE: Why does separation of variable gives the general solution to a PDE
QUESTION [32 upvotes]: I was reading about physics and came across the method of using separation of variables to solve specific PDEs, but I can't figure out why the specific solutions give rise to the general solution (the book didn't give any explanation for all these).
The specific example in the book was the Laplace Equation in $2$ variables:
$$\frac {\partial^2 V}{\partial x^2}+\frac {\partial^2 V}{\partial y^2}=0$$
For the above example, separation of variable is essentially solving for the eigen-vectors of the operator $\frac {\partial^2 }{\partial x^2}$ and $\frac {\partial^2 }{\partial y^2}$, which are Hermitian and commutes with each other. I know that in the finite dimensional case, such operators are simultaneously diagonalizable, then solving for the eigen-vectors will give all the solution, but I'm not sure does this work for infinite dimension. I'm also not sure does this approach works in the general case, for other PDEs that can be solved by separation of variable.
All the other post I find on here are all explaining how or when separation of variable work, instead of why such techniques will give the general solutions.
Another side question is: What kind of classes will cover these topics? The only undergraduate class that seems relevant at my university is Linear Analysis, which doesn't cover this. The graduate PDE sequence have graduate Real Analysis sequence as pre-requisite, which I don't think I'll be able to take soon.

REPLY [9 votes]: Separation of variables relies on being able to choose an orthogonal coordinate system in which the Laplace operator separates. That is a rather strong restriction. For example, the 3d Laplacian splits in only a couple dozen different orthogonal coordinate systems. And the solid in which you are solving the Laplace equation must be a cube in the curvilinear coordinate system, so that each surface of the solid is described as a rectangle in two variables of the curvilinear coordinate system. Then, under these conditions, the transformed Laplacian permits the use of separation of variables for solving the Laplace equation.
The ODEs that result from separation of variables are Sturm-Liouville eigenvalue problems, which is where Sturm-Liouville theory originated. The Sturm-Liouville problems are easier to analyze than the PDE. One can prove that eigenfunction expansions exist for Sturm-Liouville problems. And that gives you enough to solve the Laplace equation by using the eigenfunction expansions coming from the Sturm-Liouville ODEs. You do not necessarily end up with discrete sum expansions of eigenfunctions. If the domain is infinite in one or more coordinates, or if the Jacobian of the orthogonal transformation to curvilinear coordinates vanishes somewhere on the outer surface or at an interior point, then eigenfunction expansions may involve discrete sums and/or integrals of eigenfunctions in the eigenvalue parameter. The theory is not necessarily simple, but it was worked out well before the general theory of Elliptic PDEs, and it remains important because of being able to find explicit solutions for some rather important cases. The method is validated by proving the completeness of eigenfunction expansions associated with Sturm-Liouville problems.
The general theory of Elliptic PDEs is far more general than that required to deal with the problems where separation of variables applies for the Laplace equation. On the other hand, the general theory is not needed when separation of variables applies. Separation of variables is one of the few ways to obtain general, explicit solutions for specific geometries. Even though there are not many cases where explicit solutions are possible, these cases are useful special cases that help reveal the general nature of elliptic PDEs.