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### 8.2Stability and classiﬁcation of isolated critical points

Note: 1.5–2 lectures, §6.1–§6.2 in [EP], §9.2–§9.3 in [BD]

#### 8.2.1Isolated critical points and almost linear systems

A critical point is isolated if it is the only critical point in some small “neighborhood” of the point. That is, if we zoom in far enough it is the only critical point we see. In the above example, the critical point was isolated. If on the other hand there would be a whole curve of critical points, then it would not be isolated.

A system is called almost linear (at a critical point ) if the critical point is isolated and the Jacobian matrix at the point is invertible, or equivalently if the linearized system has an isolated critical point. In such a case, the nonlinear terms are very small and the system behaves like its linearization, at least if we are close to the critical point.

In particular the system we have just seen in Examples 8.1.1 and 8.1.2 has two isolated critical points and , and is almost linear at both critical points as both of the Jacobian matrices and are invertible.

On the other hand a system such as , has an isolated critical point at , however the Jacobian matrix

is zero when . Therefore the system is not almost linear. Even a worse example is the system , , which does not have isolated critical points, as and are both zero whenever , that is, the entire axis.

Fortunately, most often critical points are isolated, and the system is almost linear at the critical points. So if we learn what happens there, we will have ﬁgured out the majority of situations that arise in applications.

#### 8.2.2Stability and classiﬁcation of isolated critical points

Once we have an isolated critical point, the system is almost linear at that critical point, and we computed the associated linearized system, we can classify what happens to the solutions. We more or less use the classiﬁcation for linear two-variable systems from § 3.5, with one minor caveat. Let us list the behaviors depending on the eigenvalues of the Jacobian matrix at the critical point in Table 8.1. This table is very similar to Table 3.1, with the exception of missing “center” points. We will discuss centers later, as they are more complicated.

 Eigenvalues of the Jacobian matrix Behavior Stability real and both positive source / unstable node unstable real and both negative sink / stable node asymptotically stable real and opposite signs saddle unstable complex with positive real part spiral source unstable complex with negative real part spiral sink asymptotically stable

Table 8.1: Behavior of an almost linear system near an isolated critical point.

In the new third column, we have marked points as asymptotically stable or unstable. Formally, a stable critical point is one where given any small distance to , and any initial condition within a perhaps smaller radius around , the trajectory of the system will never go further away from than . An unstable critical point is one that is not stable. Informally, a point is stable if we start close to a critical point and follow a trajectory we will either go towards, or at least not get away from, this critical point.

A stable critical point is called asymptotically stable if given any initial condition suﬃciently close to and any solution satisfying that condition, then

That is, the critical point is asymptotically stable if any trajectory for a suﬃciently close initial condition goes towards the critical point .

Example 8.2.1: Consider , . See Figure 8.3 for the phase diagram. Let us ﬁnd the critical points. These are the points where and . The ﬁrst equation means , and so . Plugging into the second equation we obtain . Factoring we obtain . Since we are looking only for real solutions we get either or . Solving for the corresponding using , we get two critical points, one being and the other being . Clearly the critical points are isolated. Let us compute the Jacobian matrix:

At the point we get the matrix and so the two eigenvalues are and . As the matrix is invertible, the system is almost linear at . As the eigenvalues are real and of opposite signs, we get a saddle point, which is an unstable equilibrium point.

At the point we get the matrix and computing the eigenvalues we get , . The matrix is invertible, and so the system is almost linear at . As we have real eigenvalues and both negative, the critical point is a sink, and therefore an asymptotically stable equilibrium point. That is, if we start with any point close to as an initial condition and plot a trajectory, it will approach . In other words,

As you can see from the diagram, this behavior is true even for some initial points quite far from , but it is deﬁnitely not true for all initial points.

Example 8.2.2: Let us look at , . First let us ﬁnd the critical points. These are the points where and . Simplifying we get . So the critical points are and , and hence are isolated. Let us compute the Jacobian matrix:

At the point we get the matrix and so the two eigenvalues are and . As the matrix is invertible, the system is almost linear at . And, as the eigenvalues are real and of opposite signs, we get a saddle point, which is an unstable equilibrium point.

At the point we get the matrix whose eigenvalues are . The matrix is invertible, and so the system is almost linear at . As we have complex eigenvalues with positive real part, the critical point is a spiral source, and therefore an unstable equilibrium point.

See Figure 8.4 for the phase diagram. Notice the two critical points, and the behavior of the arrows in the vector ﬁeld around these points.

#### 8.2.3The trouble with centers

Recall, a linear system with a center meant that trajectories traveled in closed elliptical orbits in some direction around the critical point. Such a critical point we would call a center or a stable center. It would not be an asymptotically stable critical point, as the trajectories would never approach the critical point, but at least if you start suﬃciently close to the critical point, you will stay close to the critical point. The simplest example of such behavior is the linear system with a center. Another example is the critical point in Example 8.1.1.

The trouble with a center in a nonlinear system is that whether the trajectory goes towards or away from the critical point is governed by the sign of the real part of the eigenvalues of the Jacobian matrix, and the Jacobian matrix in a nonlinear system changes from point to point. Since this real part is zero at the critical point itself, it can have either sign nearby, meaning the trajectory could be pulled towards or away from the critical point.

Example 8.2.3: An easy example where such a problematic behavior is exhibited is the system . The only critical point is the origin . The Jacobian matrix is

At the Jacobian matrix is , which has eigenvalues . Therefore, the linearization has a center.

Using the quadratic equation, the eigenvalues of the Jacobian matrix at any point are

At any point where (so at most points near the origin), the eigenvalues have a positive real part ( can never be negative). This positive real part will pull the trajectory away from the origin. A sample trajectory for an initial condition near the origin is given in Figure 8.5.

The moral of the example is that further analysis is needed when the linearization has a center. The analysis will in general be more complicated than in the above example, and is more likely to involve case-by-case consideration. Such a complication should not be surprising to you. By now in your mathematical career, you have seen many places where a simple test is inconclusive, perhaps starting with the second derivative test for maxima or minima, and requires more careful, and perhaps ad hoc analysis of the situation.

#### 8.2.4Conservative equations

An equation of the form

for an arbitrary function is called a conservative equation. For example the pendulum equation is a conservative equation. The equations are conservative as there is no friction in the system so the energy in the system is “conserved.” Let us write this equation as a system of nonlinear ODE.

These types of equations have the advantage that we can solve for their trajectories easily.

The trick is to ﬁrst think of as a function of for a moment. Then use the chain rule

where the prime indicates a derivative with respect to . We obtain . We integrate with respect to to get . In other words

We obtained an implicit equation for the trajectories, with diﬀerent giving diﬀerent trajectories. The value of is conserved on any trajectory. This expression is sometimes called the Hamiltonian or the energy of the system. If you look back to § 1.8, you will notice that is an exact equation, and we just found a potential function.

Example 8.2.4: Let us ﬁnd the trajectories for the equation , which is the equation from Example 8.1.1. The corresponding ﬁrst order system is

Trajectories satisfy

We solve for

Plotting these graphs we get exactly the trajectories in Figure 8.1. In particular we notice that near the origin the trajectories are closed curves: they keep going around the origin, never spiraling in or out. Therefore we discovered a way to verify that the critical point at is a stable center. The critical point at is a saddle as we already noticed. This example is typical for conservative equations.

Consider an arbitrary conservative equation . All critical points occur when (the -axis), that is when . The critical points are those points on the -axis where . The trajectories are given by

So all trajectories are mirrored across the -axis. In particular, there can be no spiral sources nor sinks. The Jacobian matrix is

The critical point is almost linear if at the critical point. Let denote the Jacobian matrix. The eigenvalues of are solutions to

Therefore . In other words, either we get real eigenvalues of opposite signs (if ), or we get purely imaginary eigenvalues (if ). There are only two possibilities for critical points, either an unstable saddle point, or a stable center. There are never any sinks or sources.

#### 8.2.5Exercises

Exercise 8.2.1: For the systems below, ﬁnd and classify the critical points, also indicate if the equilibria are stable, asymptotically stable, or unstable.
a) b) , c) ,

Exercise 8.2.2: Find the implicit equations of the trajectories of the following conservative systems. Next ﬁnd their critical points (if any) and classify them.
a) b) c) d)

Exercise 8.2.3: Find and classify the critical point(s) of , .

Exercise 8.2.4: Suppose , . a) Show there are two spiral sinks at and . b) For any initial point of the form , ﬁnd what is the trajectory. c) Can a trajectory starting at where spiral into the critical point at ? Why or why not?

Exercise 8.2.5: In the example , show that for any trajectory, the distance from the origin is an increasing function. Conclude that the origin behaves like is a spiral source. Hint: Consider and show it has positive derivative.

Exercise 8.2.6: Suppose is always positive. Find the trajectories of . Are there any critical points?

Exercise 8.2.7: Suppose that , . Suppose that for all and . Are there any critical points? What can we say about the trajectories at goes to inﬁnity?

Exercise 8.2.101: For the systems below, ﬁnd and classify the critical points.
a) , b) , c) ,

Exercise 8.2.102: Find the implicit equations of the trajectories of the following conservative systems. Next ﬁnd their critical points (if any) and classify them.
a) b) c)

Exercise 8.2.103: The conservative system is not almost linear. Classify its critical point(s) nonetheless.

Exercise 8.2.104: Derive an analogous classiﬁcation of critical points for equations in one dimension, such as based on the derivative. A point is critical when and almost linear if in addition . Figure out if the critical point is stable or unstable depending on the sign of . Explain. Hint: see § 1.6.