Eigenvalue problem and initial-value problem?

In summary: Hi the_wolfman,I have figured out what's going on here. I have originally many negative eigenvalues and one positive eigenvalues, so initially the energy of the state should decay before asymptotically increase. Previously, I failed to look at a sufficiently long time (I observe the energy "always" decays), so I thought I did something in the numerics. Now I fixed it, what I have to do is to run a very long time before I would see the decay of the energy.Thank you anyway.
  • #1
jollage
63
0
Hi all,

I want to ask a question about the eigenvalue problem (EVP) and the initial value problem (IVP).

Let's say we are solving this linear equation [itex]\frac{\partial u}{\partial t}=\mathcal{L}u[/itex], the operator L is dependent on some parameters like Reynolds number.
I first check the eigenvalue of the equation at a specific parameter set.

My question is if the eigenvalue at this specific parameter is positive (meaning the system is unstable), now I solved the IVP counterpart [itex]\frac{u^{n+1}-u^n}{dt}=\mathcal{L}u[/itex] with an ARBITRARY initial condition, will this initial condition amplifies over all the time history (especially at the early phase, because I know asymptotically the system is governed by the positive eigenvalue)?

Thanks in advance

Jo
 
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  • #2
jollage said:
Hi all,

I want to ask a question about the eigenvalue problem (EVP) and the initial value problem (IVP).

Let's say we are solving this linear equation [itex]\frac{\partial u}{\partial t}=\mathcal{L}u[/itex], the operator L is dependent on some parameters like Reynolds number.
I first check the eigenvalue of the equation at a specific parameter set.

My question is if the eigenvalue at this specific parameter is positive (meaning the system is unstable), now I solved the IVP counterpart [itex]\frac{u^{n+1}-u^n}{dt}=\mathcal{L}u[/itex] with an ARBITRARY initial condition, will this initial condition amplifies over all the time history (especially at the early phase, because I know asymptotically the system is governed by the positive eigenvalue)?

Thanks in advance

Jo

Assuming that you're initial perturbation is sufficiently random, it will kick off all the unstable modes of the system. However the most unstable mode will grow fastest and overtime it will dominate the solution.
Thus the procedure you describe will asymptotically yield the eigenvector associated with the largest eigenvalue.
Does that make sense?
 
  • #3
the_wolfman said:
Assuming that you're initial perturbation is sufficiently random, it will kick off all the unstable modes of the system. However the most unstable mode will grow fastest and overtime it will dominate the solution.
Thus the procedure you describe will asymptotically yield the eigenvector associated with the largest eigenvalue.
Does that make sense?

Hi the_wolfman,

I have figured out what's going on here.

I have originally many negative eigenvalues and one positive eigenvalues, so initially the energy of the state should decay before asymptotically increase. Previously, I failed to look at a sufficiently long time (I observe the energy "always" decays), so I thought I did something in the numerics. Now I fixed it, what I have to do is to run a very long time before I would see the decay of the energy.

Thank you anyway.

Jo
 

FAQ: Eigenvalue problem and initial-value problem?

1. What is an eigenvalue problem?

An eigenvalue problem is a mathematical problem in which we seek to find the values (eigenvalues) and corresponding vectors (eigenvectors) that satisfy a specific equation. This equation is often represented as a matrix equation, and the eigenvalues and eigenvectors provide important information about the behavior of the system.

2. How is an eigenvalue problem different from an initial-value problem?

An eigenvalue problem involves finding a set of values and vectors that satisfy a specific equation, while an initial-value problem involves finding a solution to a differential equation that satisfies certain initial conditions. In an eigenvalue problem, the equation is often linear and the values and vectors are constants, while in an initial-value problem, the equation is often nonlinear and the solution varies with time.

3. What are some real-world applications of eigenvalue problems?

Eigenvalue problems have applications in many fields, including physics, engineering, and computer science. They are used to analyze the stability of structures, model vibrations and oscillations, and solve differential equations in quantum mechanics and quantum chemistry. They are also used in image and signal processing, data compression, and machine learning algorithms.

4. How are eigenvalue problems solved?

There are various methods for solving eigenvalue problems, including the power method, inverse iteration, and Jacobi's method. These methods involve iterative processes and can be computationally intensive for large matrices. In some cases, exact solutions can be found analytically, but this is not always possible.

5. What is the relationship between eigenvalues and eigenvectors?

The eigenvalues and eigenvectors of a matrix are intimately connected. Each eigenvalue corresponds to a specific eigenvector, and the eigenvalues determine the direction and magnitude of the corresponding eigenvectors. Additionally, the eigenvectors form a basis for the vector space of the matrix, and any vector can be expressed as a linear combination of the eigenvectors with the eigenvalues as coefficients.

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