The eigenvalues are real and that the eigenfunctions are orthogonal

In summary, the conversation discusses a proof for the eigenvalues being real and the eigenfunctions being orthogonal in the Sturm-Liouville problem. The proof involves taking an integral and using the fact that the operator $L$ is self-adjoint, which results in the left side of the relation being equal to 0. There is also a question about why the integral was taken and why the self-adjoint property is important in this proof.
  • #1
mathmari
Gold Member
MHB
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Hey! :eek:

We have the Sturm-Liouville problem $\displaystyle{Lu=\lambda u}$.

I am looking at the following proof that the eigenvalues are real and that the eigenfunctions are orthogonal and I have some questions...

$\displaystyle{Lu_i=\lambda_iu_i}$

$\displaystyle{Lu_j=\lambda_ju_j \Rightarrow Lu_j^*=\lambda_j^*u_j^*}$

$\displaystyle{\int_a^b(u_j^*Lu_i-u_iLu_j^*)dx=\int_a^b((\lambda_i-\lambda_j^*)u_iu_j^*)dx=(\lambda_i-\lambda_j^*)(u_j,u_i)}$

Since the operator $L$ is self-adjoint, the left side of the relation above is equal to $0$.

$\displaystyle{(\lambda_i-\lambda_j^*)(u_j, u_i)=0} $

  • For $i=j:$ $(u_i,u_i)=\int u_i^*u_i dx=\int |u_i|^2 dx \geq 0$. So that is is equal to $0$, it should be $\lambda_i=\lambda_i^* \Rightarrow \lambda_i \in \mathbb{R}$
  • For $i \neq j \Rightarrow \lambda_i \neq \lambda_j$, so that the relation $(\lambda_i-\lambda_j^*)(u_j, u_i)=0$ stands, it should be $(u_j, u_i)=0$. In this case th eigenfunctions are orthogonal.

Could you explain why at the beginning we have taken the integral:
$\displaystyle{\int_a^b(u_j^*Lu_i-u_iLu_j^*)dx}$?? (Wondering)

And also why does it stand that "since the operator $L$ is self-adjoint, the left side of the relation above is equal to $0$."?? (Wondering)$ \left ( \text{ We have defined the dot product as : } \displaystyle{(v,u)=\int_a^b v^* u dx} \right ) $
 
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  • #2
Hi! (Happy)

mathmari said:
$\displaystyle{\int_a^b(u_j^*Lu_i-u_iLu_j^*)dx=\int_a^b((\lambda_i-\lambda_j^*)u_iu_j^*)dx=(\lambda_i-\lambda_j^*)(u_j,u_i)}$

Since the operator $L$ is self-adjoint, the left side of the relation above is equal to $0$.Could you explain why at the beginning we have taken the integral:
$\displaystyle{\int_a^b(u_j^*Lu_i-u_iLu_j^*)dx}$?? (Wondering)

And also why does it stand that "since the operator $L$ is self-adjoint, the left side of the relation above is equal to $0$."?? (Wondering)$ \left ( \text{ We have defined the dot product as : } \displaystyle{(v,u)=\int_a^b v^* u dx} \right ) $

The left hand side is equal to $(u_j, Lu_i) - (Lu_j, u_i)$.
Apparently this was chosen because the definition of a self-adjoint operator $A$ is that $(Ax, y)=(x,Ay)$. In other words, that means that it is 0.

The fact that $L$ is self-adjoint is not given in your problem statement, so I'll have to assume that this is given in some context of your problem. (Wondering)
 
  • #3
I like Serena said:
The left hand side is equal to $(u_j, Lu_i) - (Lu_j, u_i)$.
Apparently this was chosen because the definition of a self-adjoint operator $A$ is that $(Ax, y)=(x,Ay)$. In other words, that means that it is 0.

The fact that $L$ is self-adjoint is not given in your problem statement, so I'll have to assume that this is given in some context of your problem. (Wondering)

Ahaa! I got it! Thanks a lot for your answer! (Mmm)
 

FAQ: The eigenvalues are real and that the eigenfunctions are orthogonal

What is the significance of eigenvalues being real?

The eigenvalues being real indicates that the matrix or operator being studied has a complete set of eigenvectors that can be used to diagonalize it. This is important in many applications, such as solving differential equations or analyzing physical systems.

How do real eigenvalues and orthogonal eigenfunctions relate to each other?

The orthogonality of eigenfunctions means that they are perpendicular to each other, and the real eigenvalues indicate the stretching or scaling factor applied to the eigenvectors. This relationship is important in understanding the behavior of a system and how it can be represented mathematically.

Can a matrix have complex eigenvalues and still have real eigenfunctions?

No, a matrix can only have real eigenfunctions if its eigenvalues are also real. This is because complex eigenvalues would result in complex eigenvectors, which cannot be orthogonal to each other.

How are eigenvalues and eigenvectors used in practical applications?

Eigenvalues and eigenvectors are used in many fields such as physics, engineering, and computer science. They are used to analyze and understand systems, make predictions, and solve complex problems.

What is the relationship between eigenvalues and the behavior of a system?

The eigenvalues of a system determine its behavior and stability. For example, in physics, the eigenvalues of a quantum mechanical system dictate the possible energy states that the system can have. In a practical application, the eigenvalues can indicate the properties or characteristics of a system, such as its natural frequencies or response to external forces.

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