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)Edit
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.
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.
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 orderEdit
Much like one can speak of a separable first-order ODE, one can speak of a separable second-order, third-order or nth-order ODE. Consider the separable first-order 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 first-order 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 second-derivative operator, by analogy, breaks down as follows:
The third-, fourth- and nth-derivative operators break down in the same way. Thus, much like a first-order separable ODE is reducible to the form
a separable second-order ODE is reducible to the form
and an nth-order separable ODE is reducible to
Consider the simple nonlinear second-order differential equation:
This equation is an equation only of y'' and y', meaning it is reducible to the general form described above and is, therefore, separable. Since it is a second-order separable equation, collect all x variables on one side and all y' variables on the other to get:
Now, integrate the right side with respect to x and the left with respect to y':
which simplifies to:
This is now a simple integral problem that gives the final answer:
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.
The variable u denotes temperature. The boundary condition is homogeneous, that is
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:
Substituting u back into equation (1) and using the product rule,
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:
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 u-v then satisfies homogeneous boundary condition, and can be solved with the above method.
Example: mixed derivativesEdit
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 two-dimensional 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 and likewise, taking derivative with respect to leads to and thus , 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
which can each be solved by considering the separate cases for and noting that .
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.
Partial differential equationsEdit
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:
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, self-adjoint 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 square-integrable 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 self-adjoint 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).
The matrix form of the separation of variables is the Kronecker sum.