Separation of variables
Differential equations 

Scope 
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Solution 
People 
In mathematics, separation of variables (also known as the Fourier method) is any of several methods for solving ordinary and partial differential equations, in which algebra allows one to rewrite an equation so that each of two variables occurs on a different side of the equation.
Ordinary differential equations (ODE)
A differential equation for the unknown will be separable if it can be written in the form
where and are given functions. This is perhaps more transparent when written using as:
So now as long as h(y) ≠ 0, we can rearrange terms to obtain:
where the two variables x and y have been separated. Note dx (and dy) can be viewed, at a simple level, as just a convenient notation, which provides a handy mnemonic aid for assisting with manipulations. A formal definition of dx as a differential (infinitesimal) is somewhat advanced.
Alternative notation
Those who dislike Leibniz's notation may prefer to write this as
but that fails to make it quite as obvious why this is called "separation of variables". Integrating both sides of the equation with respect to , we have

(A1)
or equivalently,
because of the substitution rule for integrals.
If one can evaluate the two integrals, one can find a solution to the differential equation. Observe that this process effectively allows us to treat the derivative as a fraction which can be separated. This allows us to solve separable differential equations more conveniently, as demonstrated in the example below.
(Note that we do not need to use two constants of integration, in equation (A1) as in
because a single constant is equivalent.)
Example
Population growth is often modeled by the "logistic" differential equation
where is the population with respect to time , is the rate of growth, and is the carrying capacity of the environment. Separation of variables now leads to
which is readily integrated using partial fractions on the left side yielding
where A is the constant of integration. We can find in terms of at t=0. Noting we get
Generalization of separable ODEs to the nth order
Much like one can speak of a separable firstorder ODE, one can speak of a separable secondorder, thirdorder or nthorder ODE. Consider the separable firstorder ODE:
The derivative can alternatively be written the following way to underscore that it is an operator working on the unknown function, y:
Thus, when one separates variables for firstorder equations, one in fact moves the dx denominator of the operator to the side with the x variable, and the d(y) is left on the side with the y variable. The secondderivative operator, by analogy, breaks down as follows:
The third, fourth and nthderivative operators break down in the same way. Thus, much like a firstorder separable ODE is reducible to the form
a separable secondorder ODE is reducible to the form
and an nthorder separable ODE is reducible to
Example
Consider the simple nonlinear secondorder differential equation:
Partial differential equations
The method of separation of variables is also used to solve a wide range of linear partial differential equations with boundary and initial conditions, such as the heat equation, wave equation, Laplace equation, Helmholtz equation and biharmonic equation.
The analytical method of separation of variables for solving partial differential equations has also been generalized into a computational method of decomposition in invariant structures that can be used to solve systems of partial differential equations.
Example: homogeneous case
Consider the onedimensional heat equation. The equation is

(1)
The variable u denotes temperature. The boundary condition is homogeneous, that is

(2)
Let us attempt to find a solution which is not identically zero satisfying the boundary conditions but with the following property: u is a product in which the dependence of u on x, t is separated, that is:

(3)
Substituting u back into equation (1) and using the product rule,

(4)
Since the right hand side depends only on x and the left hand side only on t, both sides are equal to some constant value −λ. Thus:

(5)
and

(6)
−λ here is the eigenvalue for both differential operators, and T(t) and X(x) are corresponding eigenfunctions.
We will now show that solutions for X(x) for values of λ ≤ 0 cannot occur:
Suppose that λ < 0. Then there exist real numbers B, C such that
From (2) we get

(7)
and therefore B = 0 = C which implies u is identically 0.
Suppose that λ = 0. Then there exist real numbers B, C such that
From (7) we conclude in the same manner as in 1 that u is identically 0.
Therefore, it must be the case that λ > 0. Then there exist real numbers A, B, C such that
and
From (7) we get C = 0 and that for some positive integer n,
This solves the heat equation in the special case that the dependence of u has the special form of (3).
In general, the sum of solutions to (1) which satisfy the boundary conditions (2) also satisfies (1) and (3). Hence a complete solution can be given as
where D_{n} are coefficients determined by initial condition.
Given the initial condition
we can get
This is the sine series expansion of f(x) which is amenable to Fourier analysis. Multiplying both sides with and integrating over [0, L] results in
This method requires that the eigenfunctions X, here , are orthogonal and complete. In general this is guaranteed by Sturm–Liouville theory.
Example: nonhomogeneous case
Suppose the equation is nonhomogeneous,

(8)
with the boundary condition the same as (2).
Expand h(x,t), u(x,t) and f(x) into

(9)

(10)

(11)
where h_{n}(t) and b_{n} can be calculated by integration, while u_{n}(t) is to be determined.
Substitute (9) and (10) back to (8) and considering the orthogonality of sine functions we get
which are a sequence of linear differential equations that can be readily solved with, for instance, Laplace transform, or Integrating factor. Finally, we can get
If the boundary condition is nonhomogeneous, then the expansion of (9) and (10) is no longer valid. One has to find a function v that satisfies the boundary condition only, and subtract it from u. The function uv then satisfies homogeneous boundary condition, and can be solved with the above method.
Example: mixed derivatives
For some equations involving mixed derivatives, the equation does not separate as easily as the heat equation did in the first example above, but nonetheless separation of variables may still be applied. Consider the twodimensional biharmonic equation
Proceeding in the usual manner, we look for solutions of the form
and we obtain the equation
Writing this equation in the form
Taking the derivative of this expression with respect to gives which means or and likewise, taking derivative with respect to leads to and thus or , hence either F(x) or G(y) must be a constant, say −λ. This further implies that either or are constant. Returning to the equation for X and Y, we have two cases
and
which can each be solved by considering the separate cases for and noting that .
Curvilinear coordinates
In orthogonal curvilinear coordinates, separation of variables can still be used, but in some details different from that in Cartesian coordinates. For instance, regularity or periodic condition may determine the eigenvalues in place of boundary conditions. See spherical harmonics for example.
Applicability
Partial differential equations
For many PDEs, such as the wave equation, Helmholtz equation and Schrodinger equation, the applicability of separation of variables is a result of the spectral theorem. In some cases, separation of variables may not be possible. Separation of variables may be possible in some coordinate systems but not others, and which coordinate systems allow for separation depends on the symmetry properties of the equation. Below is an outline of an argument demonstrating the applicability of the method to certain linear equations, although the precise method may differ in individual cases (for instance in the biharmonic equation above).
Consider an initial boundary value problem for a function on in two variables:
where is a differential operator with respect to and is a differential operator with respect to with boundary data:
 for
 for
where is a known function.
We look for solutions of the form . Dividing the PDE through by gives
The right hand side depends only on and the left hand side only on so both must be equal to a constant , which gives two ordinary differential equations
which we can recognize as eigenvalue problems for the operators for and . If is a compact, selfadjoint operator on the space along with the relevant boundary conditions, then by the Spectral theorem there exists a basis for consisting of eigenfunctions for . Let the spectrum of be and let be an eigenfunction with eigenvalue . Then for any function which at each time is squareintegrable with respect to , we can write this function as a linear combination of the . In particular, we know the solution can be written as
For some functions . In the separation of variables, these functions are given by solutions to
Hence, the spectral theorem ensures that the separation of variables will (when it is possible) find all the solutions.
For many differential operators, such as , we can show that they are selfadjoint by integration by parts. While these operators may not be compact, their inverses (when they exist) may be, as in the case of the wave equation, and these inverses have the same eigenfunctions and eigenvalues as the original operator (with the possible exception of zero).
Matrices
The matrix form of the separation of variables is the Kronecker sum.
As an example we consider the 2D discrete Laplacian on a regular grid:
where and are 1D discrete Laplacians in the x and ydirections, correspondingly, and are the identities of appropriate sizes. See the main article Kronecker sum of discrete Laplacians for details.
Software
Some mathematical programs are able to do separation of variables: Xcas among others.
See also
Notes
 ^ "Separation of Variables". www.mathsisfun.com. Retrieved 20210918.
 ^ Miroshnikov, Victor A. (15 December 2017). Harmonic Wave Systems: Partial Differential Equations of the Helmholtz Decomposition. ISBN 9781618964069.
 ^ John Renze, Eric W. Weisstein, Separation of variables
 ^ Willard Miller(1984) Symmetry and Separation of Variables, Cambridge University Press
 ^ David Benson (2007) Music: A Mathematical Offering, Cambridge University Press, Appendix W
 ^ "Symbolic algebra and Mathematics with Xcas" (PDF).