This HTML version of the book is provided as a convenience, but some math equations are not translated correctly. The PDF version is more reliable.
Chapter 9 Systems of ODEs
A matrix is a two-dimensional version of a vector. Like a vector, it contains elements that are identified by indices. The difference is that the elements are arranged in rows and columns, so it takes two indices to identify an element.
One of many ways to create a matrix is the magic function, which returns a “magic” matrix with the given size:
>> M = magic(3)
M = 8 1 6 3 5 7 4 9 2
If you don’t know the size of a matrix, you can use whos to display it:
>> whos Name Size Bytes Class M 3x3 72 double array
Or the size function, which returns a vector:
>> V = size(M)
V = 3 3
The first element is the number of rows, the second is the number of columns.
To read an element of a matrix, you specify the row and column numbers:
ans = 1
ans = 3
When you are working with matrices, it takes some effort to remember which index comes first, row or column. I find it useful to repeat “row, column” to myself, like a mantra. You might also find it helpful to remember “down, across,” or the abbreviation RC.
Another way to create a matrix is to enclose the elements in brackets, with semi-colons between rows:
>> D = [1,2,3 ; 4,5,6]
D = 1 2 3 4 5 6
ans = 2 3
9.2 Row and column vectors
Although it is useful to think in terms of scalars, vectors and matrices, from MATLAB’s point of view, everything is a matrix. A scalar is just a matrix that happens to have one row and one column:
>> x = 5; >> size(x)
ans = 1 1
And a vector is a matrix with only one row:
>> R = 1:5; >> size(R)
ans = 1 5
Well, some vectors, anyway. Actually, there are two kind of vectors. The ones we have seen so far are called row vectors, because the elements are arranged in a row; the other kind are column vectors, where the elements are in a single column.
One way to create a column vector is to create a matrix with only one element per row:
>> C = [1;2;3]
1 2 3
ans = 3 1
The difference between row and column vectors is important in linear algebra, but for most basic vector operations, it doesn’t matter. When you index the elements of a vector, you don’t have to know what kind it is:
ans = 2
ans = 2
9.3 The transpose operator
The transpose operator, which looks remarkably like an apostrophe, computes the transpose of a matrix, which is a new matrix that has all of the elements of the original, but with each row transformed into a column (or you can think of it the other way around).
In this example:
>> D = [1,2,3 ; 4,5,6]
D = 1 2 3 4 5 6
D has two rows, so its transpose has two columns:
>> Dt = D’
Dt = 1 4 2 5 3 6
Exercise 1 What effect does the transpose operator have on row vectors, column vectors, and scalars?
The Lotka-Voltera model describes the interactions between two species in an ecosystem, a predator and its prey. A common example is rabbits and foxes.
The model is governed by the following system of differential equations:
At first glance you might think you could solve these equations by calling ode45 once to solve for R as a function of time and once to solve for F. The problem is that each equation involves both variables, which is what makes this a system of equations and not just a list of unrelated equations. To solve a system, you have to solve the equations simultaneously.
Fortunately, ode45 can handle systems of equations. The difference is that the initial condition is a vector that contains initial values R(0) and F(0), and the output is a matrix that contains one column for R and one for F.
And here’s what the rate function looks like with the parameters a = 0.1, b = 0.01, c = 0.1 and e = 0.2:
function res = lotka(t, V) r = V(1); f = V(2);
a = 0.1; b = 0.01; c = 0.1; e = 0.2;
drdt = a*r - b*r*f; dfdt = e*b*r*f - c*f;
res = [drdt; dfdt]; end
As usual, the first input variable is time. The second input variable is a vector with two elements, R(t) and F(t). I gave it a capital letter to remind me that it is a vector. The body of the function includes four paragraphs, each explained by a comment.
The first paragraph unpacks the vector by copying the elements into scalar variables. This isn’t necessary, but giving names to these values helps me remember what’s what. It also makes the third paragraph, where we compute the derivatives, resemble the mathematical equations we were given, which helps prevent errors.
The second paragraph sets the parameters that describe the reproductive rates of rabbits and foxes, and the characteristics of their interactions. If we were studying a real system, these values would come from observations of real animals, but for this example I chose values that yield interesting results.
The last paragraph packs the computed derivatives back into a vector. When ode45 calls this function, it provides a vector as input and expects to get a vector as output.
Sharp-eyed readers will notice something different about this line:
res = [drdt; dfdt];
The semi-colon between the elements of the vector is not an error. It is necessary in this case because ode45 requires the result of this function to be a column vector.
Now we can run ode45 like this:
ode45(@lotka, [0, 365], [100, 10])
As always, the first argument is a function handle, the second is the time interval, and the third is the initial condition. The initial condition is a vector: the first element is the number of rabbits at t=0, the second element is the number of foxes.
The order of these elements (rabbits and foxes) is up to you, but you have to be consistent. That is, the initial conditions you provide when you call ode45 have to be the same as the order, inside lotka, where you unpack the input vector and repack the output vector. MATLAB doesn’t know what these values mean; it is up to you as the programmer to keep track.
But if you get the order right, you should see something like this:
The x-axis is time in days; the y-axis is population. The top curve shows the population of rabbits; the bottom curve shows foxes. This result is one of several patterns this system can fall into, depending on the starting conditions and the parameters. As an exercise, try experimenting with different values.
9.5 What can go wrong?
The output vector from the rate function has to be a column vector; otherwise you get
??? Error using ==> funfun/private/odearguments LOTKA must return a column vector.
Error in ==> ode45 at 173 [neq, tspan, ntspan, next, t0, tfinal, tdir, y0, f0, odeArgs, odeFcn, ...
Which is pretty good as error messages go. It’s not clear why it needs to be a column vector, but that’s not our problem.
Another possible error is reversing the order of the elements in the initial conditions, or the vectors inside lotka. Again, MATLAB doesn’t know what the elements are supposed to mean, so it can’t catch errors like this; it will just produce incorrect results.
9.6 Output matrices
As we saw before, if you call ode45 without assigning the results to variables, it plots the results. If you assign the results to variables, it suppresses the figure. Here’s what that looks like:
>> [T, M] = ode45(@lotka, [0, 365], [100, 10]);
You can think of the left side of this assignment as a vector of variables.
As in previous examples, T is a vector of time values where ode45 made estimates. But unlike previous examples, the second output variable is a matrix containing one column for each variable (in this case, R and F) and one row for each time value.
ans = 185 2
This structure—one column per variable—is a common way to use matrices. plot understands this structure, so if you do this:
>> plot(T, M)
MATLAB understands that it should plot each column from M versus T.
You can copy the columns of M into other variables like this:
>> R = M(:, 1); >> F = M(:, 2);
In this context, the colon represents the range from 1 to end, so M(:, 1) means “all the rows, column 1” and M(:, 2) means “all the rows, column 2.”
ans = 185 1
ans = 185 1
So R and F are column vectors.
If you plot these vectors against each other, like this
>> plot(R, F)
You get a phase plot that looks like this:
Each point on this plot represents a certain number of rabbits (on the x axis) and a certain number of foxes (on the y axis).
Since these are the only two variables in the system, each point in this plane describes the complete state of the system.
Over time, the state moves around the plane; this figure shows the path traced by the state during the time interval. This path is called a trajectory.
Since the behavior of this system is periodic, the resulting trajectory is a loop.
If there are 3 variables in the system, we need 3 dimensions to show the state of the system, so the trajectory is a 3-D curve. You can use plot3 to trace trajectories in 3 dimensions, but for 4 or more variables, you are on your own.
Based on the examples we have seen so far, you would think that all ODEs describe population as a function of time, but that’s not true.
According to the Wikipedia1, “The Lorenz attractor, introduced by Edward Lorenz in 1963, is a non-linear three-dimensional deterministic dynamical system derived from the simplified equations of convection rolls arising in the dynamical equations of the atmosphere. For a certain set of parameters the system exhibits chaotic behavior and displays what is today called a strange attractor...”
The system is described by this system of differential equations:
Common values for the parameters are σ = 10, b = 8/3 and r=28.
Use ode45 to estimate a solution to this system of equations.
Are you using one of our books in a class?We'd like to know about it. Please consider filling out this short survey.