Bifurcations and Chaos - Complex eigenvalues

In summary, the equilibrium point of the given dynamical system has three eigenspaces: stable, unstable, and center. The stable eigenspace is formed by eigenvectors corresponding to eigenvalues with negative real part, while the unstable eigenspace is formed by eigenvectors corresponding to eigenvalues with positive real part. The center eigenspace is formed by eigenvectors corresponding to eigenvalues with zero real part. The behavior near the center manifold is not determined by the linearization and plays an important role in bifurcation theory.
  • #1
Rubik
97
0

Homework Statement


Identify the stable, unstable and center eigenspaces for

[itex]\dot{y}[/itex] = the 3x3 matrix
row 1: 0, -3, 0
row 2: 3, 0, 0
row 3: 0, 0, 1


Homework Equations





The Attempt at a Solution


This is an example used from the lecture and I understand how to get the eigenvalues
λ1,2 = ±3i and λ3 = 1.

However, I do not see how to find EC?

I understand how EU = the vector (0, 0, 1)
But I do not see how to find EC = {(1, 0, 0) and (0, 1, 0)}

Can anyone explain this for me?
 
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  • #2
Hi Rubik! :smile:

http://en.wikipedia.org/wiki/Center_manifold is very clear on this :smile:, so i'll just quote it (with highlights) …​

In mathematics, the center manifold of an equilibrium point of a dynamical system consists of orbits whose behavior around the equilibrium point is not controlled by either the attraction of the stable manifold or the repulsion of the unstable manifold. The first step when studying equilibrium points of dynamical systems is to linearize the system.

The eigenvectors corresponding to eigenvalues with negative real part form the stable eigenspace, which gives rise to the stable manifold. The stable manifold attracts orbits close to it.

Similarly, eigenvalues with positive real part yield the unstable manifold, which repels orbits close to it.

This concludes the story if the equilibrium point is hyperbolic (i.e., all eigenvalues of the linearization have nonzero real part). However, if there are eigenvalues whose real part is zero, then these give rise to the center manifold.

The behavior near the center manifold is not determined by the linearization and thus more difficult to study.

Center manifolds play an important role in bifurcation theory because the interesting behavior takes place on the center manifold.
 

FAQ: Bifurcations and Chaos - Complex eigenvalues

1. What is a bifurcation in relation to complex eigenvalues?

A bifurcation is a point at which the qualitative behavior of a system changes due to a small change in a parameter or initial condition. In the context of complex eigenvalues, a bifurcation occurs when the eigenvalues of a system cross the imaginary axis, indicating a shift in the stability of the system.

2. How are complex eigenvalues related to chaos?

In systems with complex eigenvalues, small changes in initial conditions or parameters can lead to large, unpredictable changes in the behavior of the system. This sensitivity to initial conditions is a defining characteristic of chaotic systems, making complex eigenvalues a key component of chaos theory.

3. Can bifurcations and chaos be observed in real-world systems?

Yes, bifurcations and chaos can be observed in a wide range of natural and man-made systems, from weather patterns to stock market fluctuations. These phenomena can also be modeled and studied in controlled laboratory experiments.

4. How can complex eigenvalues be calculated and analyzed?

Complex eigenvalues can be calculated using techniques such as the characteristic equation or the Jacobian matrix. They can also be analyzed graphically using bifurcation diagrams, which show how the eigenvalues change as a parameter is varied.

5. What are some practical applications of understanding bifurcations and chaos?

Understanding bifurcations and chaos is important in many fields, such as physics, biology, and economics. It can help predict and control the behavior of complex systems, as well as inform the design of more stable and efficient systems. It also has practical applications in data analysis and signal processing.

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