Did you know that we can interpret the solution of a linear homogeneous systems as parametric equations of curves in the phase plane (xy-plane)?
In fact, each curve is a called a trajectory, and the resulting graph depicting the solution of a system of differential equations is known as a Phase Plane Portrait of the system.
Let’s investigate.
From Eigenvalues to Trajectories
So, in our previous lesson we learned how to use eigenvalues and eigenvectors to write general and particular solutions for a system of differential equations.
Well, those eigenvectors actually describe straight line solutions to our system, and by plotting points we are able to graph a direction of motion known as Trajectories.
Inspired by Paul’s Online Notes, our ultimate goal is to sketch these trajectories in the phase plane, examining if the solution approaches the critical point or equilibrium solution as t increases to infinity.
Unlocking the Secrets of Eigenvalues
Alright, so this begs us to ask some fundamental questions.
- How do we find the eigenvalues of a linear system?
- And how do we use them to identify the nature of the solutions in the neighborhood of the critical point?
In other words, we want to ensure there is only one critical point \(X_{0}=(0,0)\), thus by solving the following equation we can determine the stability and shape of the phase plane portrait.
If \(A=\left[\begin{array}{ll}a & b \\ c & d\end{array}\right]\) then the eigenvalues of \(\mathrm{A}\) can be found by:
- \(\lambda=\frac{\tau \pm \sqrt{\tau^{2}-4 \Delta}}{2}\)
- where \(\tau=\operatorname{trace}(A)=a+d\) and \(\Delta=\operatorname{det}(A)=a d-b c\)
Classifying Critical Points
And we classify the critical point of a linear system by determining the trace \((\tau)\) and determinant \((\Delta)\) using the following properties:
Real Distinct Eigenvalues: \(\tau^{2}-4 \Delta>0\)
-
If both eigenvalues are negative
- \(\tau^{2}-4 \Delta>0, \tau<0\) and \(\Delta>0\)
- then the critical point is a stable node
-
If both eigenvalues are positive
- \(\tau^{2}-4 \Delta>0, \tau>0\) and \(\Delta>0\)
- then the critical point is an unstable node
-
If the eigenvalues have opposite signs
- \(\tau^{2}-4 \Delta>0\) and \(\Delta<0\)
- then the critical point is a saddle point
Repeated Real Eigenvalues: \(\tau^{2}-4 \Delta=0\)
-
If there are two linearly independent eigenvectors and the repeated eigenvalue is negative
- \(\lambda < 0\)
- then the critical point is called a degenerate stable node
-
If there are two linearly independent eigenvectors and the repeated eigenvalue is positive
- \(\lambda > 0\)
- then the critical point is called a degenerate unstable node
-
If there is a single linearly independent eigenvector and the repeated eigenvalue is negative
- \(\lambda < 0\)
- then the critical point is called a degenerate stable node
-
If there is a single linearly independent eigenvector and the repeated eigenvalue is positive
- \(\lambda > 0\)
- then the critical point is called a degenerate unstable node
Complex Eigenvalues: \(\tau^{2}-4 \Delta<0\)
-
If \(\alpha<0\)
- the solutions are elliptical spirals with motion toward the origin
- and the critical point is called a stable spiral point
-
If \(\alpha>0\)
- the solutions are elliptical spirals with motion away from the origin
- and the critical point is called an unstable spiral point
-
If \(\alpha=0\)
- the solutions are ellipses with the center at the origin
- then the critical point is called the center
Putting It All Together: A Qualitative Approach
Rest assured, once you see it in action, the approach, classification, and resulting phase plane portraits will all make perfect sense.
Together, let’s:
- Use a qualitative approach to classify critical points
- Sketch graphs based on eigenvalues and eigenvectors
- Determine if critical points are:
- Stable or unstable saddle points
- Nodal sources or sinks
- Degenerate or improper nodal sources or sinks
- Centers or spiral sources or sinks
There’s a lot to unpack, so let’s jump right in!
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