Estimating Eigenvalues from linear ODE

In summary, the conversation discusses finding the lowest eigenvalue for the given equation using the Sturm-Louiville operator. The solution involves guessing a function and solving the eigenvalue problem, which yields the result that ##4\lambda_0 = \pi^2##.
  • #1
member 428835

Homework Statement


Given $$u''(x)+\lambda u = 0\\
u(-1)=u(1)=0.$$
If ##\lambda_0## is the lowest eigenvalue, show that ##4 \lambda_0 = \pi^2##.

Homework Equations


$$\lambda_0 = glb\frac{(L(u),u)}{(u,u)}$$ where ##glb## denotes greatest lower bound and ##L## is the Sturm-Louiville operator. I found this equation in the book though I am not sure it is needed.

The Attempt at a Solution


We have ##L \equiv -d^2_x##, so $$\lambda_0 = glb\frac{(L(u),u)}{(u,u)}\\ = glb\frac{\int_{-1}^1 u''u\,dx}{\int_{-1}^1 u^2\,dx}\\=glb\frac{\int_{-1}^1 u'^2\,dx}{\int_{-1}^1 u^2\,dx}$$
but from here I'm stuck. I know that last integral should be ##\pi/4## but I'm unsure how to proceed. Perhaps I'm not on the correct track to start? Any ideas?
 
Physics news on Phys.org
  • #2
The solution is to guess ##u=\cos n\pi x/2## and from there it all works out...inspection.
 
  • #3
Or you could just solve the eigenvalue problem. Look at the cases $$
\lambda = \mu^2 > 0,~\lambda = -\mu^2 < 0, \lambda = 0$$Show only the first case yields non-zero solutions and find the eigenvalues.
 

Related to Estimating Eigenvalues from linear ODE

1. What is the significance of estimating eigenvalues from linear ODE?

Estimating eigenvalues from linear ODE is essential for understanding the behavior and stability of a system described by an ordinary differential equation. Eigenvalues represent the parameters of the system that determine its stability and response to different inputs.

2. How do you estimate eigenvalues from linear ODE?

There are several methods for estimating eigenvalues from linear ODE, such as the power method, the inverse power method, and the QR algorithm. These methods involve iteratively solving the eigenvalue equation until the desired accuracy is achieved.

3. What is the difference between real and complex eigenvalues?

Real eigenvalues represent the stability of a system in a single dimension, whereas complex eigenvalues represent the stability of a system in two dimensions. Real eigenvalues are easier to interpret and analyze, while complex eigenvalues may require more advanced mathematical techniques.

4. Can eigenvalues be negative?

Yes, eigenvalues can be negative. Negative eigenvalues indicate that the system is unstable and can lead to exponential growth or decay. This information is crucial for understanding the behavior of a system and making predictions about its future state.

5. How do eigenvalues relate to eigenvectors?

Eigenvalues and eigenvectors are closely related in the context of linear ODE. Eigenvalues represent the parameters of the system, while eigenvectors represent the direction of the system's response to different inputs. The eigenvectors corresponding to the largest eigenvalues are the most influential in determining the behavior of the system.

Back
Top