Differential Equations for EngineersXYZ Homework Edition

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5.1 Sturm-Liouville problems

Boundary Value Problems

In Chapter 4 we have encountered several different eigenvalue problems such as:

X ( x ) + λ X ( x ) = 0 X''(x)+ \lambda X(x)=0 \nonumber

with different boundary conditions

X ( 0 ) = 0 X ( L ) = 0 (Dirichlet), or X ( 0 ) = 0 X ( L ) = 0 (Neumann), or X ( 0 ) = 0 X ( L ) = 0 (Mixed), or X ( 0 ) = 0 X ( L ) = 0 (Mixed) , \begin{array}{rrl} X(0) = 0 & ~~X(L) = 0 & ~~\text{(Dirichlet), or} \\ X'(0) = 0 & ~~X'(L) = 0 & ~~\text{(Neumann), or} \\ X'(0) = 0 & ~~X(L) = 0 & ~~\text{(Mixed), or} \\ X(0) = 0 & ~~X'(L) = 0 & ~~\text{(Mixed)}, \ldots \end{array} \nonumber

For example for the insulated wire, Dirichlet conditions correspond to applying a zero temperature at the ends, Neumann means insulating the ends, etc…. Other types of endpoint conditions also arise naturally, such as the Robin boundary conditions

h X ( 0 ) X ( 0 ) = 0 h X ( L ) + X ( L ) = 0 , hX(0)-X'(0)=0\quad hX(L)+X'(L)=0, \nonumber

for some constant hh. These conditions come up when the ends are immersed in some medium.

Boundary problems came up in the study of the heat equation ut=kuxxu_t=ku_{xx} when we were trying to solve the equation by the method of separation of variables in Section 4.6. In the computation we encountered a certain eigenvalue problem and found the eigenfunctions Xn(x)X_n(x). We then found the eigenfunction decomposition of the initial temperature f(x)=u(x,0)f(x)=u(x,0) in terms of the eigenfunctions

f ( x ) = n = 1 c n X n ( x ) . f(x)= \sum_{n=1}^{\infty}c_nX_n(x). \nonumber

Once we had this decomposition and found suitable Tn(t)T_n(t) such that Tn(0)=1T_n(0)=1 and Tn(t)X(x)T_n(t)X(x) were solutions, the solution to the original problem including the initial condition could be written as

u ( x , t ) = n = 1 c n T n ( t ) X n ( x ) . u(x,t)= \sum_{n=1}^{\infty}c_nT_n(t)X_n(x). \nonumber

We will try to solve more general problems using this method. First, we will study second order linear equations of the form

d d x ( p ( x ) d y d x ) q ( x ) y + λ r ( x ) y = 0 . \label{eq:6} \frac{d}{dx}\left( p(x)\frac{dy}{dx} \right)-q(x)y+\lambda r(x)y=0.

(5.1.1)

Essentially any second order linear equation of the form a(x)y+b(x)y+c(x)y+λd(x)y=0a(x)y''+b(x)y'+c(x)y+\lambda d(x)y=0 can be written as (5.1.1) after multiplying by a proper factor.

Problems satisfying the hypothesis of the theorem (including the "Moreover") are called regular Sturm-Liouville problems, and we will only consider such problems here. That is, a regular problem is one where p(x),p(x),q(x)p(x),\: p'(x),\: q(x) and r(x)r(x) are continuous, p(x)>0p(x)>0, r(x)>0r(x)>0, q(x)0q(x) \geq 0, and α1,α2,β1,β20\alpha_1, \alpha_2,\beta_1, \beta_2 \geq 0. Note: Be careful about the signs. Also be careful about the inequalities for rr and pp, they must be strict for all xx in the interval [a,b][a,b], including the endpoints!

When zero is an eigenvalue, we usually start labeling the eigenvalues at 00 rather than at 11 for convenience. That is we label the eigenvalues λ0<λ1<λ2<\lambda_{0} <\lambda_{1} <\lambda_{2} <\cdots.

First note that λ0\lambda \geq 0 by Theorem 1\PageIndex{1}. Therefore, the general solution (without boundary conditions) is

y ( x ) = A cos ( λ x ) + B sin ( λ x ) if  λ > 0 , y ( x ) = A x + B if  λ = 0 . \begin{align}\begin{aligned} & y(x) = A \cos ( \sqrt{\lambda}\, x) + B \sin ( \sqrt{\lambda}\, x) & & \qquad \text{if } \; \lambda > 0, \\ & y(x) = A x + B & & \qquad \text{if } \; \lambda = 0. \end{aligned}\end{align} \nonumber

Let us see if λ=0\lambda=0 is an eigenvalue: We must satisfy 0=hBA0=hB-A and A=0A=0, hence B=0B=0 (as h>0h>0), therefore, 00 is not an eigenvalue (no nonzero solution, so no eigenfunction).

Now let us try h>0h>0. We plug in the boundary conditions.

0 = h A λ B , 0 = A λ sin ( λ ) + B λ cos ( λ ) . \begin{align}\begin{aligned} 0 &=hA- \sqrt{\lambda}B, \\ 0 &=-A\sqrt{\lambda}\sin(\sqrt{\lambda})+B\sqrt{\lambda}\cos(\sqrt{\lambda}).\end{aligned}\end{align} \nonumber

If A=0A=0, then B=0B=0 and vice-versa, hence both are nonzero. So B=hAλB=\frac{hA}{\sqrt{\lambda}}, and 0=Aλsin(λ)+hAλλcos(λ)0=-A \sqrt{\lambda}\sin(\sqrt{\lambda})+\frac{hA}{\sqrt{\lambda}}\sqrt{\lambda}\cos(\sqrt{\lambda}). As A0A \neq 0 we get

0 = λ sin ( λ ) + h cos ( λ ) , 0=- \sqrt{\lambda}\sin(\sqrt{\lambda})+h\cos(\sqrt{\lambda}), \nonumber

or

h λ = tan λ . \frac{h}{\sqrt{\lambda}}= \tan \sqrt{\lambda}. \nonumber

Now use a computer to find λn\lambda_n. There are tables available, though using a computer or a graphing calculator is far more convenient nowadays. Easiest method is to plot the functions hx\frac{h}{x} and tan(x)\tan(x) and see for which they intersect. There is an infinite number of intersections. Denote the first intersection by λ1\sqrt{\lambda_1} the first intersection, by λ2\sqrt{\lambda_2} the second intersection, etc…. For example, when h=1h=1, we get that λ10.86,λ23.43,...\sqrt{\lambda_1}\approx 0.86,\: \sqrt{\lambda_2}\approx 3.43,.... That is λ10.74,λ211.73,...\lambda_1 \approx 0.74,\: \lambda_2 \approx 11.73,..., …. A plot for h=1h=1 is given in Figure 1\PageIndex{1}. The appropriate eigenfunction (let A=1A=1 for convenience, then B=hλB= \frac{h}{\sqrt{\lambda}}) is

Interactive figureRobin eigenvalues: where tan(x) meets h/xDrag the Robin constant h slider from 0.1 to 5.
Repeated steep branches sweep up from the horizontal axis and off the top of the frame, one after another across the width, while a single curve falls steeply from the upper left and flattens along the axis to the right. Each place that falling curve cuts a rising branch is an eigenvalue, and there is exactly one per branch, so they run on forever and each is larger than the last. Raising the Robin constant lifts the falling curve and slides every crossing to the right. Adjustable parameter: Robin constant h (h) = 1. Viewing window: x from -1.79 to 11.79, y from -4.2 to 4.2.
XYZ Graph · viewer build 5edf91b
The book's Figure 1 unfrozen. The positive crossings of tan(x) and h/x are the values of √λ, and at h = 1 the first two land on the book's own 0.86 and 3.43. Drag h down toward 0 and every crossing slides toward 0, π, 2π, 3π — the Neumann spectrum, which is exactly the y′(0) = 0, y′(1) = 0 problem this section sets as a Your Turn. Drag it up to 5 and they migrate the other way, toward tan's asymptotes at π/2, 3π/2, 5π/2. At every h the crossings run on forever and each is larger than the last, exactly as the theorem on this page promises.

y n ( x ) = cos ( λ n x ) + h λ n sin ( λ n x ) . y_n(x)=\cos(\sqrt{\lambda_n}x)+\frac{h}{\sqrt{\lambda_n}}\sin(\sqrt{\lambda_n}x). \nonumber

When h=1h=1 we get (approximately)

y 1 ( x ) cos ( 0.86 x ) + 1 0.86 sin ( 0.86 x ) , y 2 ( x ) cos ( 3.43 x ) + 1 3.43 sin ( 3.43 x ) , . . . . y_1(x) \approx \cos(0.86x)+ \frac{1}{0.86} \sin(0.86x),\quad y_2(x) \approx \cos(3.43x)+ \frac{1}{3.43} \sin(3.43x),\quad.... \nonumber

graph of y = 1/x and y = tan(x)
Figure 1\PageIndex{1}: Plot of1x\frac{1}{x} and tanx\tan x.

Orthogonality

We have seen the notion of orthogonality before. For example, we have shown that sin(nx)\sin(nx) are orthogonal for distinct nn on [0,π][0, \pi]. For general Sturm-Liouville problems we will need a more general setup. Let r(x)r(x) be a weight function (any function, though generally we will assume it is positive) on [a,b][a, b]. Two functions f(x)f(x), g(x)g(x) are said to be orthogonal with respect to the weight function r(x)r(x) when

a b f ( x ) g ( x ) r ( x ) d x = 0 . \int_a^bf(x)g(x)r(x)dx=0. \nonumber

In this setting, we define the inner product as

f , g = d e f a b f ( x ) g ( x ) r ( x ) d x , \langle f,g \rangle \overset{\rm{def}}= \int_a^bf(x)g(x)r(x)dx, \nonumber

and then say ff and gg are orthogonal whenever f,g=0\langle f,g \rangle=0. The results and concepts are again analogous to finite dimensional linear algebra.

The idea of the given inner product is that those xx where r(x)r(x) is greater have more weight. Nontrivial (nonconstant) r(x)r(x) arise naturally, for example from a change of variables. Hence, you could think of a change of variables such that dξ=r(x)dxd \xi =r(x)dx.

Eigenfunctions of a regular Sturm–Liouville problem satisfy an orthogonality property, just like the eigenfunctions in Section 4.1. Its proof is very similar to the analogous Theorem 4.1.1.

Fredholm Alternative

We also have the Fredholm alternative theorem we talked about before (Theorem 4.1.2) for all regular Sturm-Liouville problems. We state it here for completeness.

This theorem is used in much the same way as we did before in Section 4.4. It is used when solving more general nonhomogeneous boundary value problems. The theorem does not help us solve the problem, but it tells us when a unique solution exists, so that we know when to spend time looking for it. To solve the problem we decompose f(x)f(x) and y(x)y(x) in terms of the eigenfunctions of the homogeneous problem, and then solve for the coefficients of the series for y(x)y(x).

Eigenfunction Series

What we want to do with the eigenfunctions once we have them is to compute the eigenfunction decomposition of an arbitrary function f(x)f(x). That is, we wish to write

f ( x ) = n = 1 c n y n ( x ) , \label{eq:26} f(x)= \sum_{n=1}^{\infty}c_ny_n(x),

(5.1.2)

where yn(x)y_n(x) the eigenfunctions. We wish to find out if we can represent any function f(x)f(x) in this way, and if so, we wish to calculate (and of course we would want to know if the sum converges). OK, so imagine we could write f(x)f(x) as (5.1.2). We will assume convergence and the ability to integrate the series term by term. Because of orthogonality we have

f , y m = a b f ( x ) y m ( x ) r ( x ) d x = n = 1 c n a b y n ( x ) y m ( x ) r ( x ) d x = c m a b y m ( x ) y m ( x ) r ( x ) d x = c m y m , y m . \begin{align}\begin{aligned} \langle f,y_m \rangle &= \int_a^bf(x)y_m(x)r(x)dx \\ &= \sum_{n=1}^{\infty}c_n \int_a^by_n(x)y_m(x)r(x)dx \\ &=c_m \int_a^by_m(x)y_m(x)r(x)dx= c_m \langle y_m,y_m \rangle.\end{aligned}\end{align} \nonumber

Hence,

c m = f , y m y m , y m = a b f ( x ) y m ( x ) r ( x ) d x a b ( y m ( x ) ) 2 r ( x ) d x . \label{eq:28} c_m= \frac{\langle f,y_m \rangle}{\langle y_m,y_m \rangle}= \frac{\int_a^bf(x)y_m(x)r(x)dx}{\int_a^b(y_m(x))^2r(x)dx}.

(5.1.3)

Note that ymy_m are known up to a constant multiple, so we could have picked a scalar multiple of an eigenfunction such that ym,ym=1\langle y_m,y_m \rangle=1 (if we had an arbitrary eigenfunction y~m\tilde{y}_m, divide it by y~m,y~m\sqrt{\langle \tilde{y}_m,\tilde{y}_m \rangle}). When ym,ym=1\langle y_m,y_m \rangle=1 we have the simpler form cm=f,ymc_m=\langle f,y_m \rangle as we did for the Fourier series. The following theorem holds more generally, but the statement given is enough for our purposes.

Note that the series converges to an odd 2π2\pi-periodic (not π\pi-periodic!) extension of f(x)f(x).

Let us compute an example. Consider f(x)=xf(x) = x for 0<x<π20 < x < \frac{\pi}{2}. Some calculus later we find

cn=4π0π2f(x)sin((2n1)x)dx=4(1)n+1π(2n1)2,c_n = \frac{4}{\pi} \int_0^{\frac{\pi}{2}} f(x) \,\sin \bigl( (2n-1)x \bigr) \, dx = \frac{4{(-1)}^{n+1}}{\pi {(2n-1)}^2}, \nonumber

and so for xx in [0,π2][0,\frac{\pi}{2}],

f(x)=n=14(1)n+1π(2n1)2sin((2n1)x).f(x) = \sum_{n=1}^\infty \frac{4{(-1)}^{n+1}}{\pi {(2n-1)}^2} \sin \bigl( (2n-1)x \bigr). \nonumber

This is different from the π\pi-periodic regular sine series which can be computed to be

f ( x ) = n = 1 ( 1 ) n + 1 n sin ( 2 n x ) . f(x)=\sum\limits_{n=1}^\infty \frac{(-1)^{n+1}}{n}\sin (2nx). \nonumber

Both sums converge are equal to f(x)f(x) for 0<x<π20 < x < \frac{\pi}{2}, but the eigenfunctions involved come from different eigenvalue problems.

Footnotes

[1] Named after the French mathematicians Jacques Charles François Sturm (1803–1855) and Joseph Liouville (1809–1882).

Adapted from Differential Equations for Engineers by Jiří Lebl (https://www.jirka.org/diffyqs/), © Jiří Lebl, licensed under CC BY-SA 4.0. Changes were made. License: CC-BY-SA-4.0.

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