Eigen Value and Eigen Function

In summary: I wrote above. In summary, an eigenfunction is a function that, when operated on by a linear operator, returns the same function multiplied by a scalar value (eigenvalue). In quantum mechanics, the wavefunction is an eigenfunction of the Hamiltonian operator, and the corresponding eigenvalue is the energy of the system. The term "eigen" comes from German and means "distinct" or "particular".
  • #1
roshan2004
140
0
I know that the eigen equation is given by [tex]A\psi =\lambda\psi[/tex] where A is an operator and [tex]\psi[/tex] is an eigen function and [tex]\lambda[/tex] is an eigen value.
For the particle in an infinite square well of width L the wavefunction is given by [tex]\psi (x)=\sqrt{\frac{2}{L}}sin(\frac{n\pi x}{L})[/tex] and the energy is given by [tex]E=\frac{n^2h^2}{8ml^2}[/tex]
I want to know why [tex]\psi[/tex] is called eigen function and E is called the eigen value.
 
Physics news on Phys.org
  • #2
From German eigen, meaning "distinct", "particular", "[one's] own".

It comes by analogy to linear algebra. If a vector [tex]\vec{v}[/tex] is such that when you multiply a matrix A, by it, you get back the same matrix multiplied by some scalar (lambda):
[tex]\mathbf{A}\vec{v} = \lambda\mathbf{A}[/tex]
Then [tex]\vec{v}[/tex] is an eigenvector of A, and [tex]\lambda[/tex] the corresponding eigenvalue. Any linear algebra textbook will tell you all about it.

This concept can be generalized to linear operators (matrix multiplication being a linear operation). For a linear operator [tex]\hat{O}[/tex], and a function f, if:
[tex]\hat{O}f = \lambda f[/tex]

Then f is an eigenfunction of [tex]\hat{O}[/tex], and [tex]\lambda[/tex] is again the corresponding eigenvalue. If you have a given operator and some boundary conditions, and wish to find the eigenfunctions and eigenvalues for it, then that's called an "eigenvalue problem".

For instance, the differential operator [tex]\hat{D}^n = \frac{d^n}{d x^n}[/tex] is a linear operator.
So differential equations, such as [tex]\frac{df}{d x} - \lambda f(x) = 0[/tex] are eigenvalue problems.
 
  • #3
hi roshan2004! :smile:

(have a psi: ψ and a lambda: λ and a pi: π and a square-root: √ and a curly d: ∂ :wink:)

your wavefunction should also have an eiωt factor

the (energy) operator A is ih∂/∂t (where I'm writing h for h-bar = h/2π),

so the (energy) eigenvalue is hω

(which just happens to be h(nπ/L)2/2m = h2n2/8mL2

see http://en.wikipedia.org/wiki/Square_well" :wink:)​
 
Last edited by a moderator:
  • #4
Thanks, but in this case I couldnot find operator here, which is the operator in this case?
 
  • #5
roshan2004 said:
Thanks, but in this case I couldnot find operator here, which is the operator in this case?

The Hamiltonian. The time-independent Schrödinger equation is simply the eigenvalue problem for the Hamiltonian.
Also, since V = 0 and you only have one dimension in the infinite square well, the Hamiltonian in that case is just the second-order differential operator (multiplied by [tex]-\hbar/2m[/tex])
 

What is an eigenvalue?

An eigenvalue is a scalar value that represents how a linear transformation changes a vector. It is a special value that remains unchanged when multiplied by a vector.

What is an eigenvector?

An eigenvector is a vector that remains in the same direction after a linear transformation has been applied to it. It is associated with an eigenvalue and is often used to find the eigenvalues of a linear transformation.

What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important in linear algebra because they provide a way to simplify complex matrices and make calculations easier. They also have many applications in fields such as physics, engineering, and computer science.

How do you find the eigenvalues and eigenvectors of a matrix?

To find the eigenvalues of a matrix, you can use the characteristic polynomial of the matrix. To find the eigenvectors, you can solve the corresponding system of linear equations using the eigenvalues as coefficients.

What are some real-world applications of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many real-world applications, such as image and signal processing, data compression, and principal component analysis. They are also used in quantum mechanics, population dynamics, and machine learning algorithms.

Similar threads

  • Quantum Physics
Replies
31
Views
3K
  • Quantum Physics
Replies
19
Views
1K
  • Quantum Physics
Replies
6
Views
1K
Replies
2
Views
542
  • Quantum Physics
Replies
31
Views
2K
Replies
3
Views
401
Replies
1
Views
613
  • Advanced Physics Homework Help
Replies
6
Views
1K
Replies
2
Views
699
Replies
4
Views
1K
Back
Top