Eigenvalue Problem Simplified: A Simple Solution to the Eigenvalue Problem

In summary, the conversation is about solving the eigenvalue problem of the given differential equation and finding a simpler solution. One person had already solved it but was looking for a simpler approach. The proposed solution involved using linear algebra and determinants to determine the constants in the general solution. However, there were concerns about the completeness of the solution as the constants had not been determined using the boundary conditions. The conversation ends with a request for help in determining the constants.
  • #1
mathwizarddud
25
0
Solve the eigenvalue problem

[tex]\frac{d^2 \phi}{dx^2} = -\lambda \phi[/tex]

subject to

[tex]\phi(0) = \phi(2\pi)[/tex]

and

[tex] \frac{d \phi}{dx} (0) = \frac{d \phi}{dx} (2 \pi).[/tex]

I had the solution already, but am looking for a much simpler way, if any.

EDIT:

Sorry that I accidentally posted this twice; I meant to edit the post, but not sure why the edited post becomes a new one...
 
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  • #2
How do we know that our suggested solution is simpler than yours if you don't demonstrate your attempt?
 
  • #3
Here's what I had:

after solving the ODE, we have the general solution

[tex]\phi = C_1 \sin(\sqrt{\lambda}x) + C_2 \cos(\sqrt{\lambda}x)[/tex]

applying the conditions we have the system

[tex]C_2 = C_1 \sin(\sqrt{\lambda}2\pi) + C_2 \cos(\sqrt{\lambda}2\pi)[/tex]

[tex] C_1 \sqrt{\lambda} = C_1 \sqrt{\lambda} \cos(\sqrt{\lambda}2\pi) - C_2 \sqrt{\lambda} \sin(\sqrt{\lambda}2\pi)[/tex]

then using a little knowledge of linear algebra and determinant, we get, in order not to have trivial solutions,

[tex]C_1 C_2 u^2 + C_1 C_2 (v-1)^2 = 0[/tex]

where [tex]u = \sin(\sqrt{\lambda}2\pi)[/tex] and
[tex]v = \cos(\sqrt{\lambda}2\pi)[/tex]

or simply

[tex] u^2 + (v-1)^2 = 0[/tex]

So [tex]u^2 = (v-1)^2 = 0[/tex] or
[tex] u = 0; v = 1[/tex]

[tex] \sqrt{\lambda_n}2\pi = 2n\pi[/tex]
[tex]\lambda_n = n^2[/tex]

So the eigenfunction is

[tex]\phi_n = C_1 \sin(nx) + C_2 \cos(nx)[/tex]

I don't think this is complete because we haven't determined [tex]C_1 [/tex] and [tex] C_2[/tex] yet.
 
  • #4
Use the BC's to get your constants.
 
  • #5
dirk_mec1 said:
Use the BC's to get your constants.

I thought that I've already used them in first determining the eigenvalue.
 
  • #6
anyone?
 

Related to Eigenvalue Problem Simplified: A Simple Solution to the Eigenvalue Problem

What is an eigenvalue problem?

An eigenvalue problem is a mathematical problem that involves finding the eigenvalues and corresponding eigenvectors of a given square matrix. Eigenvalues are special numbers that represent the scaling factor of the eigenvectors when they are multiplied by the matrix. Solving the eigenvalue problem is important in many fields of science, including physics, engineering, and data analysis.

Why is solving the eigenvalue problem important?

Solving the eigenvalue problem allows us to understand the behavior of a system or process described by a matrix. Eigenvalues and eigenvectors can provide insights into the stability, dynamics, and patterns of a system. In addition, many important calculations in science, such as diagonalizing a matrix and solving differential equations, rely on eigenvalues and eigenvectors.

How do you solve an eigenvalue problem?

The most common method for solving an eigenvalue problem is through diagonalization, where the given matrix is transformed into a diagonal matrix using eigenvectors. This involves finding the eigenvalues through solving the characteristic equation of the matrix, and then finding the corresponding eigenvectors. Other methods include power iteration, QR algorithm, and Jacobi method.

What are the applications of solving the eigenvalue problem?

The applications of solving the eigenvalue problem are vast and diverse. In physics, eigenvalues and eigenvectors are used to describe the behavior of quantum systems and determine the energy levels of atoms and molecules. In engineering, they are used to analyze structures and systems, such as bridges and electrical circuits. In data analysis, eigenvalues and eigenvectors are used in techniques like principal component analysis to reduce the dimensionality of data and identify patterns.

Are there any challenges in solving the eigenvalue problem?

Yes, there can be challenges in solving the eigenvalue problem, especially for larger matrices. The computational complexity of finding eigenvalues and eigenvectors increases with the size of the matrix, making it difficult to solve for very large matrices. In addition, some matrices may have complex eigenvalues, which can make the calculations more complex. However, with advancements in technology and algorithms, these challenges can be overcome.

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