Autonomous Equations / Stability of Equilibrium Solutions
First order autonomous equations, Equilibrium solutions, Stability, Long▯
term behavior of solutions, direction fields, Population dynamics and
Autonomous Equation: A differential equation where the independent
variable does not explicitly appear in its expression. It has the general form
y′ = f(y).
Examples: y′ = e − y
y′ = y − 4y
y′ = y − 81 + siny
Every autonomous ODE is a separable equation. Because, assuming that
f(y) ≠ 0,
dy dy dy
dt = f (y) → f (y)= dt → ∫ f (y)= d∫ .
Hence, we already know how to solve them. What we are interested now is
to predict the behavior of an autonomous equation’s solutions without
solving it, by using its direction field. But what happens if the assumption
that f (y) ≠ 0 is false? We shall start by answering this very question.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 1 Equilibrium solutions
Equilibrium solutions (or critical points) occur whenever y′ = f(y) = 0. That
is, they are the roots of f(y). Any root c of f(y) yields a constant solution y =
c. (Exercise: Verify that, if c is a root of fhen y = c is a solution of
y′ = f(y.) Equilibrium solutions are constant functions that satisfy the
equation, i.e., they are the constant solutions of the differential equation.
Example: Logistic Equation of Population
y r 2
y = r 1 − y = ry − y
Both r and K are positive constants. The solution y is the population
size of some ecosystem, r is the intrinsic growth rate, and K is the
environmental carrying capacity. The intrinsic growth rate is the
natural rate of growth of the population provided that the availability
of necessary resource (food, water, oxygen, etc) is limitless. The
environmental carrying capacity (or simply, carrying capacity) is the
maximum sustainable population size given the actual availability of
Without solving this equation, we will examine the behavior of its
solution. Its direction field is shown in the next figure.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 2 Notice that the long▯term behavior of a particular solution is determined
solely from the initial condition y(t ) =0y . Th0 behavior can be categorized
by the initial value y :
If y < 0, then y → −∞ as t→ ∞.
If y = 0, then y = 0, a constant/equilibrium solution.
If 0 < y < K, then y → K as t→ ∞.
If y = K, then y = K, a constant/equilibrium solution.
If y > K, then y → K as t→ ∞.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 3 Comment: In a previous section (applications: air▯resistance) you learned an
easy way to find the limiting velocity without having to solve the differential
equation. Now we can see that the limiting velocity is just the equilibrium
solution of the motion equation (which is an autonomous equation). Hence
it could be found by setting v′ = 0 in the given differential equation and
solve for v.
Stability of an equilibrium solution
The stability of an equilibrium solution is classified according to the
behavior of the integral curves near it – they represent the graphs of
particular solutions satisfying initial conditions whose initial values, y 0,
differ only slightly from the equilibrium value.
If the nearby integral curves all converge towards an equilibrium
solution as t increases, then the equilibrium solution is said to be
stable, or asymptotically stable. Such a solution has long▯term
behavior that is insensitive to slight (or sometimes large) variations in
its initial condition.
If the nearby integral curves all diverge away froman equilibrium
solution as t increases, then the equilibrium solution is said to be
unstable. Such a solution is extremely sensitive to even the slightest
variations in its initial condition − as we can see in the previous
example that the smallest deviation in initial value results in totally
different behaviors (in both long▯ and short▯terms).
Therefore, in the logistic equation example, the solution y = 0 is an unstable
equilibrium solution, while y = K is an (asymptotically) stable equilibrium
© 2008, 2012 Zachary S Tseng A▯2 ▯ 4 An alternative graphical method: Plotting y′ = f(y) versus y. This is a
graph that is easier to draw, but reveals just as much information *s the
direction field. It is rather similar to the First Derivative Test for local
extrema in calculus. On any interval (they are separated by equilibrium
solutions / critical points, which are the horizontal▯intercepts of the graph)
where f (y) > 0, y will be increasing and we denote this fact by drawing a
rightward arrow. (Because, y in this plot happens to be the horizontal axis;
and its coordinates increase from left to right, from −∞ to ∞.) Similarly, on
any interval where f(y) < 0, y is decreasing. We shall denote this fact by
drawing a leftward arrow. To summarize: f (y) > 0, y goes up, therefore,
rightward arrow; f(y) < 0, y goes down, therefore, leftward arrow. The result
can then be interpreted in the following way: Suppose y = c is an
equilibrium solution (i.e. f (y) = 0), then
(i.) If f(y) < 0 on the left of c, and f(y) > 0 on the right of
c, then the equilibrium solution y = c is unstable.
(Visually, the arrows on the two sides are moving away
(ii.) If f(y) > 0 on the left of c, and f(y) < 0 on the right of
c, then the equilibrium solution y = c is asymptotically
stable. (Visually, the arrows on the two sides are moving
Remember, a leftward arrow means y is decreasing as t increases. It
corresponds to downward▯sloping arrows on the direction field. While a
rightward arrow means y is increasing as t increases. It corresponds to
upward▯sloping arrows on the direction field.
All the steps are really the same, only the interpretation of the result differs.
A result that would indicate a local minimum now means that the
equilibrium solution/critical point is unstable; while that of a local maximum
result now means an asymptotically stable equilibrium solution.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 5 As an example, let us apply this alternate method on t2e same logistic
equation seen previously: y′ = ry − (r/K)y , r = 0.75, K = 10.
The y′▯versus▯y plot is shown below.
As can be seen, the equilibrium solutions y = 0 and y = K = 10 are the
two horizontal▯intercepts (confusingly, they are the y▯intercepts, since
the y▯axis is the horizontal axis). The arrows are moving apart from
y = 0. It is, therefore, an unstable equilibrium solution. On the other
hand, the arrows from both sides converge toward y = K. Therefore, it
is an (asymptotically) stable equilibrium solution.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 6 Example: Logistic Equation with (Extinction) Threshold
y′= − r 1− y1− y y
Where r, T, and K are positive constants: 0 < T < K.
The values r and K still have the same interpretations, T is the extinction
threshold level below which the species is endangered and eventually
become extinct. As seen above, the equation has (asymptotically) stable
equilibrium solutions y = 0 and y = K. There is an unstable equilibrium
solution y = T.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 7 The same result can, of course, be obtained by looking at the y′▯versus▯y plot
(in this example, T = 5 and K = 10):
We see that y = 0 and y = K are (asymptotically) stable, and y = T is unstable.
Once again, the long▯term behavior can be determined just by the initial
value y 0
If y < 0, then y → 0 as t→ ∞.
If y 0 0, then y = 0, a constant/equilibrium solution.
If 0 < y 0 T, then y → 0 as t→ ∞.
If y = T, then y = T, a constant/equilibrium solution.
If T < y 0 K, then y → K as t→ ∞.
If y 0 K, then y = K, a constant/equilibrium solution.
If y 0 K, then y → K as t→ ∞.
Semistable equilibrium solution
A third type of equilibrium solutions exist. It exhibits a half▯and▯half
behavior. It is demonstrated in the next example.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 8 3 2
Example: y′ = y − 2y
The equilibrium solutions are y = 0 and 2. As can be seen below,
y = 2 is an unstable equilibrium solution. The interesting thing here,
however, is the equilibrium solution y = 0 (which corresponding a
double▯root of f (y).
Notice the behavior of the integral curves near the equilibrium solution y = 0.
The integral curves just above it are converging to it, like it is a stable
equilibrium solution, but all the integral curves below it are moving away
and diverging to −∞, a behavior associated with an unstable equilibrium
solution. A behavior such like this defines a semistable equilibrium solution.
© 2008, 2012 Zachary S Tseng A▯2 ▯ 9 An equilibrium solution is semistable if y′ has the same sign on both
adjacent intervals. (In our analogy with the First Derivative Test, if the
result would indicate that a critical point is neither a local maximum nor a
minimum, then it now means we have a semistable equilibrium solution.
(iii.) If f(y) > 0 on both sides of c, or f(y) < 0 on both
sides of c, then the equilibrium solution y = c is
semistable. (Visually, the arrows on one side are moving
toward c, while on the other side they are moving away
Comment: As we can see, it is actually not necessary to graph anything in
order to determine stability. The only thing we need to make the
determination is the sign of y′ on the interval immediately to either side of an
equilibrium solution (a.k.a. critical point), then just apply the above▯
mentioned rules. The steps are otherwise identical to the first derivative test:
breaking the number line into intervals using critical points, evaluate y′ at an
arbitrary point within each interval, finally make determination based on the
signs of y′. This is our version of the first derivative test for classifying
stability of equilibrium solutions of an autonomous equation. (The graphing
methods require more work but also will provide more information –
unnecessary for our purpose here – such as the instantaneous rate of change
of a particular solution at any point.)
Computationally, stability classification tells us the sensitivity (or lack
thereof) to slight variations in initial condition of an equilibrium solution.
An unstable equilibrium solution is very sensitive to deviations in the initial
condition. Even the slightest change in the initial value will result in a very