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3.5 Two dimensional systems and their vector fields

Note: 1 lecture, part of §6.2 in [EP], parts of §7.5 and §7.6 in [BD]

Let us take a moment to talk about constant coefficient linear homogeneous systems in the plane. Much intuition can be obtained by studying this simple case. Suppose we have a 2 × 2 matrix P and the system

[ ]′ [ ] x = P x . y y
(3.2)

The system is autonomous (compare this section to § 1.6) and so we can draw a vector field (see end of § 3.1). We will be able to visually tell what the vector field looks like and how the solutions behave, once we find the eigenvalues and eigenvectors of the matrix P . For this section, we assume that P has two eigenvalues and two corresponding eigenvectors.


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Figure 3.3: Eigenvectors of P .


Case 1. Suppose that the eigenvalues of P are real and positive. We find two corresponding eigenvectors and plot them in the plane. For example, take the matrix [ ] 10 1 2 . The eigenvalues are 1 and 2 and corresponding eigenvectors are [1] 0 and [1] 1 . See Figure 3.3.

Now suppose that x and y are on the line determined by an eigenvector ⃗v for an eigenvalue λ . That is, [x] y = a⃗v for some scalar a . Then

[ ]′ [ ] x = P x = P(a⃗v) = a(P ⃗v) = a λ⃗v. y y

The derivative is a multiple of ⃗v and hence points along the line determined by ⃗v . As λ > 0 , the derivative points in the direction of ⃗v when a is positive and in the opposite direction when a is negative. Let us draw the lines determined by the eigenvectors, and let us draw arrows on the lines to indicate the directions. See Figure 3.4.

We fill in the rest of the arrows for the vector field and we also draw a few solutions. See Figure 3.5. The picture looks like a source with arrows coming out from the origin. Hence we call this type of picture a source or sometimes an unstable node.


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Figure 3.4: Eigenvectors of P with directions.
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Figure 3.5: Example source vector field with eigenvectors and solutions.


Case 2. Suppose both eigenvalues are negative. For example, take the negation of the matrix in case 1, [-1 -1] 0 -2 . The eigenvalues are - 1 and - 2 and corresponding eigenvectors are the same, [1] 0 and [1] 1 . The calculation and the picture are almost the same. The only difference is that the eigenvalues are negative and hence all arrows are reversed. We get the picture in Figure 3.6. We call this kind of picture a sink or sometimes a stable node.


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Figure 3.6: Example sink vector field with eigenvectors and solutions.
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Figure 3.7: Example saddle vector field with eigenvectors and solutions.


Case 3. Suppose one eigenvalue is positive and one is negative. For example the matrix [1 1] 0 -2 . The eigenvalues are 1 and - 2 and corresponding eigenvectors are [1] 0 and [1 ] -3 . We reverse the arrows on one line (corresponding to the negative eigenvalue) and we obtain the picture in Figure 3.7. We call this picture a saddle point.

For the next three cases we will assume the eigenvalues are complex. In this case the eigenvectors are also complex and we cannot just plot them in the plane.

Case 4. Suppose the eigenvalues are purely imaginary. That is, suppose the eigenvalues are ± ib . For example, let  [0 1] P = -4 0 . The eigenvalues turn out to be ± 2i and eigenvectors are [1] 2i and [ 1] -2i . Consider the eigenvalue 2i and its eigenvector [ ] 21i . The real and imaginary parts of ⃗vei2t are

 [ ] [ ] R e 1 ei2t = co s(2t) , 2i - 2sin(2t) [ ] [ ] Im 1 ei2t = sin(2t) . 2i 2 cos(2t)
We can take any linear combination of them to get other solutions, which one we take depends on the initial conditions. Now note that the real part is a parametric equation for an ellipse. Same with the imaginary part and in fact any linear combination of the two. This is what happens in general when the eigenvalues are purely imaginary. So when the eigenvalues are purely imaginary, we get ellipses for the solutions. This type of picture is sometimes called a center. See Figure 3.8.


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Figure 3.8: Example center vector field.
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Figure 3.9: Example spiral source vector field.


Case 5. Now suppose the complex eigenvalues have a positive real part. That is, suppose the eigenvalues are a ± ib for some a > 0 . For example, let  [ 1 1] P = -4 1 . The eigenvalues turn out to be 1 ± 2i and eigenvectors are [1] 2i and [ 1] -2i . We take 1 + 2i and its eigenvector [1] 2i and find the real and imaginary parts of ⃗ve(1+2i)t are

 [ ] [ ] R e 1 e(1+2i)t = et cos(2t) , 2i - 2sin(2t) [ 1] [ sin(2t) ] Im e(1+2i)t = et . 2i 2 cos(2t)
Note the et in front of the solutions. This means that the solutions grow in magnitude while spinning around the origin. Hence we get a spiral source. See Figure 3.9.

Case 6. Finally suppose the complex eigenvalues have a negative real part. That is, suppose the eigenvalues are - a ± ib for some a > 0 . For example, let P = [-1 -1] 4 -1 . The eigenvalues turn out to be - 1 ± 2i and eigenvectors are [ 1] -2i and [1] 2i . We take - 1 - 2i and its eigenvector [1] 2i and find the real and imaginary parts of  (-1-2i)t ⃗ve are

 [ ] [ ] 1 (-1-2i)t -t cos(2t) Re 2i e = e 2sin(2t), [ ] [ ] 1 (-1-2i)t -t - sin(2t) Im 2i e = e 2cos(2t) .
Note the  -t e in front of the solutions. This means that the solutions shrink in magnitude while spinning around the origin. Hence we get a spiral sink. See Figure 3.10.


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Figure 3.10: Example spiral sink vector field.


We summarize the behavior of linear homogeneous two dimensional systems in Table 3.1.


Eigenvalues Behavior
real and both positive source / unstable node
real and both negative sink / stable node
real and opposite signs saddle
purely imaginary center point / ellipses
complex with positive real part spiral source
complex with negative real part spiral sink

Table 3.1: Summary of behavior of linear homogeneous two dimensional systems.

3.5.1 Exercises

Exercise 3.5.1: Take the equation  ′′ ′ mx + cx + kx = 0 , with m > 0 , c ≥ 0 , k > 0 for the mass-spring system. a) Convert this to a system of first order equations. b) Classify for what m, c,k do you get which behavior. c) Can you explain from physical intuition why you do not get all the different kinds of behavior here?

Exercise 3.5.2: Can you find what happens in the case when  [1 1] P = 0 1 ? In this case the eigenvalue is repeated and there is only one eigenvector. What picture does this look like?

Exercise 3.5.3: Can you find what happens in the case when P = [11 1 1] ? Does this look like any of the pictures we have drawn?

Exercise 3.5.101: Describe the behavior of the following systems without solving:
a) x′ = x + y ,  y′ = x - y .
b) x′= x 1 + x2 1 ,  x′ = 2x2 2 .
c) x′= - 2x 1 2 ,  x′ = 2x 2 1 .
d)  ′ x = x + 3y ,   ′ y = -2x - 4y .
e) x′ = x - 4y ,  y′ = -4x + y .

Exercise 3.5.102: Suppose that ⃗x′ = Ax⃗ where A is a 2 by 2 matrix with eigenvalues 2 ± i . Describe the behavior.

Exercise 3.5.103: Take [x]′ [0 1] [x] y = 0 0 y . Draw the vector field and describe the behavior. Is it one of the behaviors that we have seen before?