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.


3 Answers 3


There are several key ingredients I will briefly describe here. I won't go into too much detail as you've mentioned that you don't have a graduate real analysis background yet. But indeed a full description of the theory is a standard part of a graduate course in linear PDE. So I hope that answers your side question as well.

  1. We start with a strongly elliptic linear operator (such as the Laplacian) and, along with some nice boundary condition, we restrict to some appropriate solution (Hilbert) space.

  2. In that solution space, we can prove under fairly general conditions that the eigenvalues of the operator are countable and that eigenvectors (eigenfunctions) form an orthogonal basis for the solution space. This is the infinite-dimensional generalization of the diagonalizability result from regular matrix theory. The proof relies on the spectral theorem for compact operators. The key here is that, up to a shift, the inverse of a strongly elliptic operator is compact.

  3. This demonstrates that if we can construct all the eigenvectors of the operator, the general solution can be written as a decomposition of these eigenvectors.

  4. It remains to find the eigenvectors; in special cases (most famously, 2D Laplacian on a rectangle) this can be done via separation of variables. Therefore it remains to address "Why does separation of variables produce all eigenvectors?" To answer this question, we note that we proved that the eigenvectors form a complete basis. Next, we see that because of the specific symmetry of the Laplacian on the rectangle, using separation of variables reduces the problem to a pair of second-order equations in one-dimension; in this process we produce the eigenvectors of these one-dimensional operators, and then from the existing theory (in particular, Sturm-Liouville theory) we know that we have produced a set of functions that span the space. As we have produced a basis, no other eigenvectors are needed to form a general solution.

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    $\begingroup$ Thankyou for your answer! I'm just wondering do you know any undergraduate/relatively easy graduate text that covers that above topics (only for the brief understanding)? I'm planning to take the PDE sequence 2 years later, and I think getting an intuition earlier might help me with the understanding of physics as well. $\endgroup$
    – Lynnx
    Commented Nov 2, 2020 at 0:07
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    $\begingroup$ A standard graduate textbook is Partial Differential Equations by L.C. Evans. It should be readable by an advanced undergraduate, especially as it is designed to be entirely self-contained (that is, it basically never refers to material outside of the book itself). However the material itself can be challenging and very technical, so I'm not sure if it's the best way to obtain a broad intuition. $\endgroup$ Commented Nov 2, 2020 at 0:16
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    $\begingroup$ +1 At the end of part 4, we have a basis for two 1D operators. How do we know that multiplying these two bases (element wise) will give us a basis for the Laplacian in a 2D rectangle? Why don't we need the "cross-terms"? And how is it enough to be a basis for the 2D region? $\endgroup$ Commented Nov 2, 2020 at 8:44
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    $\begingroup$ In other words, what exactly is the intuition behind, eigenfunctions in one dimension multiplied by eigenfunctions in the other dimension produce eigenfunctions on the entire 2D region? And also why are the cross terms unnecessary? $\endgroup$ Commented Nov 2, 2020 at 8:53
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    $\begingroup$ @FixedPoint It is basically the same as comparing Fourier Series in 1D vs. 2D. In 1D the basis functions are $e^{inx}$ for integers $n$, and in 2D they are $e^{inx + imy}$ for integers $n,m$. More abstractly, one can show that $L^2([0,1] \times [0,1])$ is isomorphic to the Hilbert space tensor product $L^2[0,1] \otimes L^2[0,1]$. $\endgroup$ Commented Nov 2, 2020 at 19:50

The answer by @Christopher is very complete and definitely better than what this answer will be. But I would like to make some comments on Separation of Variables.

Separation of Variables is a process of splitting a multi-dimensional problem, into several single dimensional problems. However, this relies on an inherent symmetry of the domain, which itself determines the coordinates which allow for separation of variables.

If the question is posed in a rectangle, then it is quite natural that the problem given in rectangular coordinates can be broken down into two one dimensional problems in each orthogonal dimension. If the problem is posed on a circle, then polar coordinates are required. However, if the problem is given on a completely arbitrary domain then it is unlikely that you could find a coordinate system that can reflect the symmetry of the domain and allow for separation of variables.

If you get deeper into Lie theory, one can describe a group theoretic method of determining the possible coordinate systems that allow for a given equation to be separable. However, I don't think I have a deep enough understanding on this to comment further.

  • $\begingroup$ @epilam I'm sorry for bringing this back almost a year after, but I find your last remark about Lie theory and separation of variables very interesting. Could you please recommend references about such a treatment ? Thank you. $\endgroup$ Commented Jul 22, 2021 at 7:29
  • $\begingroup$ @DanielKatzner I find good references on the matter are few and far between but the book "Symmetry and Separation of Variables" by W. Miller (albeit quite old) would probably be a good place to start. $\endgroup$
    – user765629
    Commented Jul 25, 2021 at 6:56

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.


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