Eigenfunctions from eigenvalues unsure

In summary, the homework statement uses X'(x)+lambda*X(x)=0 to find the eigenvalues and eigenfunctions. However, for the case of lambda=-k2, lambda=k2, and lambda=k2, the Attempt at a Solution I have found does not work. It gives values for k that are incorrect and it is unclear how to find the correct eigenvalues for this problem.
  • #1
WtKemper
6
0

Homework Statement


using X''(x)+ lambda*X(x)=0 find the eigenvalues and eigenfunctions accordingly.
Use the case lambda=0, lambda=-k2, lambda=k2
where k>0

Homework Equations


X(0)=0, X'(1)+X(1)=0


The Attempt at a Solution


I know that for lambda=0
X(x)=C1x+C2
which applying the conditions gives no E.V.

also for lambda=-k2
X(x)=C1cosh(kx)+C2sinh(kx)
and applying the conditions gives no E.V.

for the final case lambda=k2
X(x)=C1cos(kx)+C2sin(kx)

using X(0)=C1=0

X'(x)=C2kcos(kx)

applying second condition then
C2(kcos(k)+sin(k))=0 so if we make the assumption that C2 is not 0 then kcos(k)+sin(k)=0

I've tried multiple things and finally came to dividing by cos(k) so that it becomes
k+tan(k)=0 or k=-tan(k)

but, this is where I get confused. My professor offered the hint that k becomes an approximation so I plotted x and -tan(x) and found where they intersect. This gives a few values but I don't understand how to get a value for k. Normally k=n*pi or some sort of thing. So, my question is how do I use this information to find lambda and the Eigenfunctions for this problem. Any help is appreciated.
 
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  • #2
It doesn't "give" general values for k. As your professor told you, the best you can do is approximate them, perhaps by graphing as you did. Obviously k= 0 is one value but that is the only one that can be written in a simple way.
 
  • #3
I figured out what was meant by approximation. As if you plot y=x and y=-tan(x) the points at which they cross close in on the values of pi*n/2 at odd n values. so k~(2n-1)*pi/2.
which makes the Eigenfunction sin(kx)=sin[((2n-1)*pi*x)/2]
 
  • #4
NO, it does not! All you can say is that the correct eigenvalues are close to (2n-1)pi/2. And, by the way, that is only true for small eigenvalues. As the eigenvalues get larger, that approximation becomes very poor.
 
  • #5
Sorry I didn't mean to say that it closes in but that it is close for (as you stated) small values. But, for some reason the professor didn't tell us that this was the case and I'm unsure why they are only concerned with the smaller values Thank You for the input and help!
 

1. What are eigenfunctions and eigenvalues?

Eigenfunctions and eigenvalues are concepts used in linear algebra and functional analysis. An eigenfunction is a function that, when multiplied by a constant, remains unchanged. An eigenvalue is the constant by which the eigenfunction is multiplied. In other words, an eigenfunction is a special type of vector that remains unchanged when multiplied by a specific scalar value, which is the eigenvalue.

2. What is the importance of eigenfunctions and eigenvalues?

Eigenfunctions and eigenvalues are important in many areas of mathematics and physics. They are used to solve differential equations, analyze data, and understand the behavior of systems in quantum mechanics, fluid dynamics, and other fields. They also have practical applications in signal processing and image recognition.

3. How are eigenfunctions and eigenvalues related?

Eigenfunctions and eigenvalues are closely related. An eigenfunction is associated with a specific eigenvalue, and together they form an eigenvector. The eigenvalue determines the magnitude of the eigenvector, while the eigenfunction determines its direction. In essence, the eigenfunction and eigenvalue represent different aspects of the same mathematical object.

4. Can eigenfunctions and eigenvalues be calculated?

Yes, eigenfunctions and eigenvalues can be calculated using various methods, such as diagonalization, power iteration, and the QR algorithm. However, the calculations can become complex and time-consuming for larger systems, so numerical methods are often used.

5. How are eigenfunctions and eigenvalues used in data analysis?

Eigenfunctions and eigenvalues are used in data analysis to reduce the dimensionality of large datasets. By finding the dominant eigenfunctions and eigenvalues, data can be represented in a lower-dimensional space without losing significant information. This is known as principal component analysis and is commonly used in machine learning and data compression.

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