What is the method for solving the eigenvalue problem with integration by parts?

In summary, the conversation discusses solving the eigenvalue problem O_{6} \Psi(x) = \lambda \Psi(x) and determining the values of eigenvalue \lambda that lead to square integral eigenfunctions. The hint is to differentiate both sides of the equation with respect to x, but the person is having trouble with integration by parts and keeps getting infinity. The other person suggests paying closer attention to the hint and solving a simple ODE for \Psi(x).
  • #1
MagicQuantum
1
0
Solve the eigenvalue problem O[tex]_{6}[/tex] [tex]\Psi[/tex](x) = [tex]\lambda[/tex] [tex]\Psi[/tex](x)

O[tex]_{6}[/tex][tex]\Psi[/tex](x) = [tex]\int[/tex] from negative infinity to x of dxprime *[tex]\Psi[/tex](xprime) * xprime
what values of eigenvalue [tex]\lambda[/tex] lead to square integral eigenfuctions? (Hint: Differentiate both sides of the equation with respect to x)

Im trying to do this with integration by parts but i keep getting infinity in some form or another. I am assuming [tex]\Psi[/tex](xprime) is equal to the derivative of [tex]\Psi[/tex](x) with respect to x. so i end up with [tex]\lambda[/tex][tex]\Psi[/tex](xprime) is equal to some integral that keeps working out to infinity. I don't want anyone to give me a solution but if anyone can give me a bump in the right direction i would be pumped.

Thanks
 
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  • #2
You aren't paying enough attention to the hint. How do you find d/dx of an integral with x as the upper limit? You should get a simple ODE to solve for psi.
 

1. What is an eigenvalue problem?

An eigenvalue problem is a mathematical problem that involves finding the values (eigenvalues) and corresponding vectors (eigenvectors) that satisfy a specific equation. It is commonly represented as Av = λv, where A is a square matrix, v is the eigenvector, and λ is the eigenvalue.

2. Why is solving the eigenvalue problem important?

Solving the eigenvalue problem is important in many fields of science, including physics, engineering, and computer science. It allows us to understand the behavior and properties of systems, such as vibrations, waves, and quantum mechanics. It is also used in various applications, such as image and signal processing, data analysis, and optimization problems.

3. How is the eigenvalue problem solved?

The eigenvalue problem can be solved using various methods, such as the power method, inverse power method, QR algorithm, and Jacobi method. These methods involve iterative processes to approximate the eigenvalues and eigenvectors of a given matrix. Advanced techniques, such as the Lanczos algorithm and Arnoldi iteration, can also be used for larger matrices.

4. What are the applications of eigenvalue problem?

The eigenvalue problem has numerous applications in different fields. In physics, it is used to study the behavior of particles in quantum mechanics and to analyze the properties of vibrating systems. In engineering, it is used to model and analyze dynamic systems, such as bridges and skyscrapers. In computer science, it is used in data compression, signal processing, and machine learning algorithms.

5. Are there any limitations to solving the eigenvalue problem?

While the eigenvalue problem is a powerful tool, it does have some limitations. It can only be applied to square matrices, and the computation can become increasingly complex for larger matrices. Additionally, it may not always provide accurate solutions, and some problems may have complex eigenvalues, making it difficult to find solutions. Therefore, it is important to carefully choose the appropriate method and consider the limitations when solving the eigenvalue problem.

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