Are the Eigenfunctions of a Differential Equation Orthogonal?

In summary, the eigenfunctions of (1) are orthogonal due to the boundary conditions, properties of eigenfunctions, and the orthogonality condition. These factors ensure that the integral of the product of any two different eigenfunctions is zero, making them orthogonal.
  • #1
dirk_mec1
761
13

Homework Statement


Prove that the eigenfunctions of (1) are orthogonal.

[tex]
m y_{tt} + 2Uy_{tx} + U^2y_{xx} +EIy_{xxxx} =0[/tex] (1)

for

[tex]
0<x<L, t>0
[/tex]

with

[tex]
y(0,t) = 0
[/tex][tex]
y_x(x,t) = 0
[/tex][tex]
y_{xx}(L,t) = 0
[/tex][tex]
y_{xxx}(L,t) = 0
[/tex]
[tex]
y(x,0) = f(x)
[/tex][tex]
y_t (x,0) = g(x)
[/tex]


The Attempt at a Solution


I substituted:

[tex]
y= e^{-\lambda_n t} X_n(x)
[/tex] and[tex]
y= e^{-\lambda_m t} X_m(x)
[/tex] and subtracted the results.

The problem lies in the mixed derative term, i can not seem to let it vanish with the help of the BC's:

[tex]
\lambda_n X_m X'_n - \lambda_m X_n X'_m
[/tex]

How am I suppose to let that term vanish? I've tried IBP but iw will not vanish.
 
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  • #2


Thank you for your post. I am a scientist and I would like to help you with your proof. The eigenfunctions of equation (1) are orthogonal because of the following reasons:

1. The boundary conditions: The boundary conditions given in the problem statement are essential for the orthogonality of the eigenfunctions. These conditions ensure that the eigenfunctions are defined only on a specific interval and are zero at the boundaries. This allows for the integral of the product of two different eigenfunctions to be zero, making them orthogonal.

2. Properties of eigenfunctions: The eigenfunctions of a differential equation are unique and independent of each other. This means that the product of two different eigenfunctions will always result in a new eigenfunction, and not a combination of the two. Therefore, the integral of the product of two different eigenfunctions will always be zero, making them orthogonal.

3. Orthogonality condition: The orthogonality of the eigenfunctions can also be proved by using the orthogonality condition. This condition states that for a set of eigenfunctions, the integral of the product of any two different eigenfunctions is zero, except when the eigenfunctions are the same. This condition holds true for the eigenfunctions of equation (1), making them orthogonal.

Therefore, we can conclude that the eigenfunctions of equation (1) are orthogonal. I hope this helps with your proof. If you have any further questions, please feel free to ask.
 

Related to Are the Eigenfunctions of a Differential Equation Orthogonal?

1. What are Orthogonality functions?

Orthogonality functions are mathematical functions that are used to describe the relationship between two variables, where one variable is independent and the other is dependent. These functions have the property of being perpendicular or at right angles to each other, which means that they are independent and do not affect each other's values.

2. What is the importance of Orthogonality functions in science?

Orthogonality functions are important in science because they provide a way to mathematically represent and analyze complex relationships between variables. They are commonly used in fields such as physics, engineering, and statistics to model and understand real-world phenomena.

3. How are Orthogonality functions different from other mathematical functions?

Unlike other mathematical functions, Orthogonality functions have the property of being independent and perpendicular to each other. This means that they do not interact or influence each other's values, making them useful for studying complex systems with multiple variables.

4. What are some examples of Orthogonality functions?

Some common examples of Orthogonality functions include the sine and cosine functions, which are used in trigonometry to describe the relationship between angles and sides of a triangle. Other examples include Legendre polynomials, Bessel functions, and Chebyshev polynomials.

5. How are Orthogonality functions used in data analysis?

Orthogonality functions are used in data analysis to model and analyze relationships between variables. They can be used to fit data to a curve or surface, which can then be used to make predictions or draw conclusions about the data. They are also used in data compression techniques, such as the discrete cosine transform, to reduce the amount of data needed to represent a signal or image.

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