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**Classi cation of critical points**

In the previous idea we de ned the critical points and sketched how these points can be divided to stable and unstable points. We have seen that mathematical pendulum has two critical points, one is stable, the other is not. In the next example we have seen a system with two unstable critical points. The classi cation of critical points, however, is more subtle and we discuss all possibilities in this section.

Let us first recapitulate our goal. We study planar dynamical system described by equations

We assume that we have found critical point of this system, i.e. point (x_{C}, y_{C} ) such that

and study the behaviour of the system near this critical point. We linearize the equations in the neighbourhood of critical point so that we obtain equations^{2}

This system can be written also in the matrix form

where

Now we discuss several forms of matrix J and classify the critical points. Finally we will show how the analysis can be done for general matrix J .

**Stable and unstable nodes, saddle points**

Consider linear planar system of the form

(8.17)

which corresponds to matrix

(8.18)

System (8.17) can be easily solved. Equations for x and y are independent; we say that these equations are decoupled which means that equation for x_ does not contain y and vice versa.

Let us solve equation

first. In usual mathematical notation, this equation reads

which is separable dierential equation. We can rewrite it as

This form of equation is called separated because the left hand side of the equations contains only x and the right hand side contains only time t. We can integrate the equation,

to obtain

where C is an integration constant. It is customary that if the logarithm appears in the solution, we write the constant as a logarithm as well3:

Exponentiating the last equation we arrive at

By the same procedure we solve equation for y to get

where L is an integration constant again. Notice that, according to the solution, we have

x(0) = K and y(0) = L:

Hence, K and L are values of x and y at time t = 0, respectively. Therefore, we can write the solution of (8.17) in the form

(8.19)

Clearly, the only critical point of system (8.17) is (0; 0). Having derived solution of this system, we can analyze its behaviour near the critical point. Useful function to visualise properties of the system near critical point is StreamPlot which takes the vector eld and plots tra jectories. In the following example we choose λ_{1 }= λ_{2} = 1.

In this figure we can see tra jectories (8.19) for initial points (x_{0,} y_{0}) chosen by Mathematica. Notice that we have inserted the right hand side of (8.17) as an argument of function StreamPlot. We can see that the trajectories are straight lines emanating from the origin (critical point) and tending to in nity exponentially.

What about other choices of λ_{1,2}? It is clear that function eλt is increasing for λ > 0 and decreasing for λ < 0. We can conclude that qualitative behaviour of the system depends on signs of λ_{1,2 }and four possibilities are shown in gure 8.8 which was created by following commands in Mathematica. We distinguish three cases.

In addition to this classi cation, critical points with distinct values λ_{ 1} = λ _{2} are called singular while critical points with the same values λ_{ 1} = λ_{ 2} are called degenerate.

Clearly, the saddle points cannot be singular.

Recall that planar dynamical system (8.17) can be represented by the matrix (8.18),

From elementary linear algebra we know that with matrix J we can associate a set of eigenvalues λ defined by equation

J . e = λ e

where e is called an eigenvector. It is easy to show that the eigenvalues of matrix (8.18) are λ_{1 }and λ_{2} and corresponding eigenvectors are

In other words, vectors e_{1} and e_{2} satisfy equations

We can see that tra jectories starting on lines determined by vectors e_{i}, i = 1, 2, always remain in these lines. If the tra jectory is being repelled from the critical point along direction e, the line determined by vector e is called unstable manifold. If the tra jectory is attracted to the critical point along the vector e, the line determined by e is called stable manifold. For matrix (8.18), vectors e_{1} and e_{2} are always eigenvectors.

We can see that e_{1 }lies on the x axis and e_{2} lies on the y axis. Hence, the axes are stable or unstable manifolds of system (8.17), depending on the sign of λ_{1,2}.

The classi cation introduced above can be reformulated in the following way. Let

be a matrix of general linear dynamical system

If matrix J has two real eigenvalues λ_{1} and λ_{2}, then critical point is stable/unstable node or a saddle point, depending on the signs of these eigenvalues.

We illustrate this classi cation on the example. Consider dynamical system

(8.20)

with the matrix

This matrix is not of the form (8.18) but we can apply the second criterion. Eigenvalues and eigenvectors can be found in Mathematica using

which shows that eigenvalues are

and corresponding eigenvectors are

Since λ_{1} > 0 and λ_{2} < 0, vector e1 de nes the stable manifold and e_{2 }de nes unstable manifold. Since both eigenvalues have different signs, the critical point is a saddle point and it is regular. Phase tra jectories together with stable and unstable manifolds can be plotted by

The result is plotted in gure 8.9.

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