PDE's Find the values of lambda (eigenvalues)

In summary: So, in summary, the problem involves finding values of λ for which there are nontrivial solutions and determining the corresponding nontrivial eigenfunctions. The solution method involves completing the square and evaluating the cases where λ>1,λ<1, and λ=1. The resulting eigenfunctions are given by y_n=c_ne^\pi sin(nx).
  • #1
whynot314
76
0
the problem stays to find the values of Lambda for which the given problem has nontrivial solutions.
Also to determine the corresponding nontrivial eigenfunctions.
y''-2y'+[itex]\lambda[/itex]y=0

0<x<[itex]\pi[/itex], y(0)=0, y([itex]\pi[/itex])=0

[itex]r^{2}[/itex]-2r=-[itex]\lambda[/itex]
r=1±i[itex]\sqrt{\lambda+1}[/itex]
y=[itex]e^{x}[/itex]([itex]c_{1}[/itex]cos([itex]\sqrt{\lambda+1}[/itex]x)+[itex]c_{2}[/itex]sin([itex]\sqrt{\lambda+1}[/itex]x)

for lambda i got [itex]\lambda_{n}[/itex]=[itex]n^{2}[/itex]-1

and for the function i got
[itex]y_{n}[/itex]=[itex]c_{n}[/itex][itex]e^{\pi}[/itex]sin(nx)
is this anywhere close to being right?
 
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  • #2
whynot314 said:
the problem stays to find the values of Lambda for which the given problem has nontrivial solutions.
Also to determine the corresponding nontrivial eigenfunctions.
y''-2y'+[itex]\lambda[/itex]y=0

0<x<[itex]\pi[/itex], y(0)=0, y([itex]\pi[/itex])=0

[itex]r^{2}[/itex]-2r=[itex]\color {red}{-\lambda}[/itex]
r=1±i[itex]\sqrt{\lambda+1}[/itex]

Put the ##\lambda## back on the other side and check that quadratic formula again.

y=[itex]e^{x}[/itex]([itex]c_{1}[/itex]cos([itex]\sqrt{\lambda+1}[/itex]x)+[itex]c_{2}[/itex]sin([itex]\sqrt{\lambda+1}[/itex]x)

for lambda i got [itex]\lambda_{n}[/itex]=[itex]n^{2}[/itex]-1

and for the function i got
[itex]y_{n}[/itex]=[itex]c_{n}[/itex][itex]e^{\pi}[/itex]sin(nx)
is this anywhere close to being right?

Yes, it's close. Once you fix that ##\lambda## thing I think you will find a ##\lambda -1## under the radical. You have assumed the quantity under the radical is less than zero. To be complete you need to eliminate the cases where it is greater than or equal to zero.
 
  • #3
well what i did was put the lambda on the right side and then completed the square. and I am evaluating the case when λ>0, this is why I have i[itex]\sqrt{\lambda+1}[/itex]
 
  • #4
Ahh ok I think you assumed i was doing it for λ<0
 
  • #5
whynot314 said:
well what i did was put the lambda on the right side and then completed the square. and I am evaluating the case when λ>0, this is why I have i[itex]\sqrt{\lambda+1}[/itex]

Like I said, check it. It should be ##\sqrt{1-\lambda}##

whynot314 said:
Ahh ok I think you assumed i was doing it for λ<0

That isn't what I assumed or what you should do. The relevant cases are ##\lambda>1,\ \lambda < 1,\ \lambda = 1##.
 
  • #6
ahh ok got a bit confused there
 
  • #7
Thank you
 

FAQ: PDE's Find the values of lambda (eigenvalues)

What are eigenvalues and why are they important in PDEs?

Eigenvalues are special numbers that represent the scaling factor of a vector in a linear transformation. In PDEs, they are important because they help us solve for the corresponding eigenvectors, which are used to find a general solution to the PDE.

How do you determine the eigenvalues of a PDE?

To find the eigenvalues of a PDE, you must first rewrite the PDE in its matrix form. Then, you can solve for the roots of the characteristic polynomial, which will give you the eigenvalues.

Can a PDE have multiple eigenvalues?

Yes, a PDE can have multiple eigenvalues. In fact, most PDEs have an infinite number of eigenvalues, as each eigenvalue corresponds to a different eigenvector.

How do eigenvalues affect the solution to a PDE?

The eigenvalues determine the behavior of the solution to a PDE. The eigenvectors corresponding to the eigenvalues act as the building blocks for the general solution, and the eigenvalues determine the rate of change of each eigenvector in the solution.

Are there any special techniques for finding the eigenvalues of PDEs?

Yes, there are several techniques for finding eigenvalues of PDEs, such as the separation of variables method and the method of characteristics. These techniques can be used to simplify the PDE and make it easier to find the eigenvalues.

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