Eigenfunctions and their Eigenvalues

In summary, the conversation discusses constructing linear combinations of eigenfunctions that are orthogonal on a given interval. The concept of orthogonality is defined as having an inner product of 0, and the inner product used in this scenario is the most common one for real valued functions on an interval. The conversation also mentions the use of an "orthogonal projection" formula to calculate the desired combinations, and the success of this method is confirmed.
  • #1
g782k936
3
0
If I have two eigenfunctions of an operator with the same eigenvalue how do I construct linear combinations of my eigenfunctions so that they are orhtogonal?

My eigenfunctions are: f=e^(x) and g=e^(-x)

and the operator is (d)^2/(dx)^2
 
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  • #2
Orthogonal wrt what?

You need a scalar product.

Daniel.
 
  • #3
I want to have linearly independent combinations of f and g that are orthognal on the interval from (-1,1) I'm guesing that they need to be wrt f and g.
 
  • #4
g782k936 said:
I want to have linearly independent combinations of f and g that are orthognal on the interval from (-1,1) I'm guesing that they need to be wrt f and g.
No, that was not the question. "Orthogonal" means that the inner product is 0 so whether or not two vectors are orthogonal depends on the inner product used.

The most common inner product for real valued functions on an interval (a, b) is [itex]\int_a^b f(x)g(x)dx[/itex].

Since, if two eigenvectors correspond to the same eigenvalue, any linear combination is also an eigenvector corresponding to that eigenvalue, a simple "orthogonal projection" will work.

If u and v are two vectors in an inner product space, then the "projection of v onto u" is given by
[tex]\frac{<u,v>}{<u,u>}\vec{u}[/tex]
The "orthogonal projection" is v minus that:
[tex]\vec{v}- \frac{<u,v>}{<u,u>}\vec{u}[/tex]

Calculate that with u= ex, v= e-x, and inner product [itex]<u,v>= \int_{-1}^1 u(x)v(x)dx[/itex].
 
  • #5
O.k.

I think that worked. I had been trying the integral in a slightly different way using f + g instead of fg.

Thanks.
 

What are eigenfunctions and eigenvalues?

Eigenfunctions and eigenvalues are concepts in linear algebra that are used to describe the relationship between a linear transformation and its corresponding vector space. An eigenfunction is a function that, when multiplied by a scalar value, remains unchanged within the vector space. An eigenvalue is the corresponding scalar value that the eigenfunction is multiplied by.

What is the importance of eigenfunctions and eigenvalues in science?

Eigenfunctions and eigenvalues have numerous applications in science, particularly in the fields of physics, engineering, and mathematics. They are used to describe the behavior of systems, such as quantum mechanical systems, and to solve differential equations. They also play a crucial role in data analysis and signal processing.

How are eigenfunctions and eigenvalues calculated?

To calculate eigenfunctions and eigenvalues, one needs to find the roots of the characteristic equation of a given linear transformation. The characteristic equation is obtained by subtracting the eigenvalue from the diagonal elements of the transformation matrix and setting the resulting determinant to zero. The resulting eigenvalues are then used to find the corresponding eigenfunctions.

What is the relationship between eigenfunctions and orthonormality?

Eigenfunctions are often chosen to be orthonormal, meaning they are independent and have a unit norm. This allows for easier manipulation and calculation, and also ensures that the corresponding eigenvalues are real numbers. In addition, orthonormality allows for the use of the spectral theorem, which states that a Hermitian operator (a type of linear transformation) can be expressed as a sum of eigenvalues and their corresponding eigenfunctions.

Can eigenfunctions and eigenvalues be generalized to non-linear systems?

Yes, eigenfunctions and eigenvalues can also be applied to non-linear systems, but the concept is slightly different. In non-linear systems, the eigenfunctions and eigenvalues are not constant but vary depending on the point in the vector space. These are known as generalized eigenfunctions and generalized eigenvalues. They are still useful in solving non-linear equations and describing the behavior of non-linear systems.

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