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MATLAB sessions: Laboratory 3 MAT 275 Laboratory 3
Numerical Solutions by Euler and Improved Euler Methods
(scalar equations)
In this session we look at basic numerical methods to help us understand the fundamentals of numerical
approximations. Our objective is as follows.
1. Implement Euler’s method as well as an improved version to numerically solve an IVP.
2. Compare the accuracy and efficiency of the methods with methods readily available in MATLAB.
3. Apply the methods to specific problems and investigate potential pitfalls of the methods.
Instructions: For your lab write-up follow the instructions of LAB 1. Euler’s Method
To derive Euler’s method start from y(t0 ) = y0 and consider a Taylor expansion at t1 = t0 + h:
y(t1 ) = y(t0 ) + y 0 (t0 )(t1 − t0 ) + . . . = y0 + hf (t0 , y(t0 )) + . . . = y0 + hf (t0 , y0 ) + . . . For small enough h we get an approximation y1 for y(t1 ) by suppressing the . . ., namely
y1 = y0 + hf (t0 , y0 ) (L3.1) The iteration (L3.1) is repeated to obtain y2 ' y(t2 ), . . . such that
yn+1
tn+1 = yn + hf (tn , yn )
= tn + h Geometrically, the approximation made is equivalent to replacing the
solution curve by the tangent line at (t0 , y0 ). From the figure we have
f (t0 , y0 ) = f (t0 , y(t0 )) = y 0 (t0 ) = tan θ = y1 − y0
,
h y(t1 )
y1 s
s y0 ....
...
...
.. θ from which (L3.1) follows.
h
As an example consider the IVP t0 t1 y 0 = 3y = f (t, y) with y(0) = 2.
Note that here f does not explicitly depend on t (the ODE is called autonomous), but does implicitly
through y = y(t). To apply Euler’s method we start with the initial condition and select a step size h.
Since we are constructing arrays t and y without dimensionalizing them first it is best to clear these
names in case they have been used already in the same MATLAB work session.
>> clear t y % no comma between t and y! type help clear for more info
>> y(1)=2; t(1)=0; h=0.1;
c 2016
Stefania Tracogna, SoMSS, ASU 1 MATLAB sessions: Laboratory 3
Since f is simple enough we may use the anonymous function syntax:
>> f=@(t,y)(3*y);
Note that the initialization y(1)=2 should not be interpreted as “the value of y at 1 is 2”, but rather “the
first value in the array y is 2”. In other words the 1 in y(1) is an index, not a time value! Unfortunately,
MATLAB indices in arrays must be positive (a legacy from Fortran...). The successive approximations
at increasing values of t are then obtained as follows:
>> y(2)=y(1)+h*f(t(1),y(1)), t(2)=t(1)+h,
y =
2.0000
2.6000
t =
0
0.1000
>> y(3)=y(2)+h*f(t(2),y(2)), t(3)=t(2)+h,
y =
2.0000
2.6000
3.3800
t =
0
0.1000
0.2000
The arrays y and t are now 1 × 3 row arrays. The dimension increases as new values of y and t are
computed.
It is obviously better to implement these steps in a for loop.
y(1)=2; t(1)=0; h = 0.1;
for n = 1:5
y(n+1)= y(n)+h*f(t(n),y(n));
t(n+1) = t(n)+h;
end
Note that the output in each command has been suppressed (with a ;). The list of computed y values
vs t values can be output at the end only by typing:
>> [t(:),y(:)]
ans =
0
2.0000
0.1000
2.6000
0.2000
3.3800
0.3000
4.3940
0.4000
5.7122
0.5000
7.4259 % same as [t’,y’] here The next step is to write a function file that takes in input the function defining the ODE, the time
span [t0 , tfinal ], the initial condition and the number of steps used. Note that, if we are given the number
tfinal − t0
of steps used, N , then the stepsize h can be easily computed using h =
.
N
The following function implements these ideas.
euler.m
function [t,y] = euler(f,tspan,y0,N)
% Solves the IVP y’ = f(t,y), y(t0) = y0 in the time interval tspan = [t0,tf]
% using Euler’s method with N time steps.
% Input:
% f
= name of inline function or function M-file that evaluates the ODE
%
(if not an inline function, use: euler(@f,tspan,y0,N))
%
For a system, the f must be given as column vector.
c 2016
Stefania Tracogna, SoMSS, ASU 2 MATLAB sessions: Laboratory 3
% tspan = [t0, tf] where t0 = initial time value and tf = final time value
% y0
= initial value of the dependent variable. If solving a system,
%
initial conditions must be given as a vector.
% N
= number of steps used.
% Output:
% t = vector of time values where the solution was computed
% y = vector of computed solution values.
m = length(y0);
t0 = tspan(1);
tf = tspan(2);
h = (tf-t0)/N;
% evaluate the time step size
t = linspace(t0,tf,N+1);
% create the vector of t values
y = zeros(m,N+1);
% allocate memory for the output y
y(:,1) = y0’;
% set initial condition
for n=1:N
y(:,n+1) = y(:,n) + h*f(t(n),y(:,n));
% implement Euler’s method
end
t = t’; y = y’;
% change t and y from row to column vectors
end
Remark: You should notice that the code above is slightly different from the first one we wrote (in
particular, note the use of “:” when creating the output y). Although the two codes are equivalent in
the scalar case, only the second one will work also for systems of Differential Equations.
We can implement the function with, say, N = 50 by typing the following commands:
>> [t,y] = euler(f,[0,.5],2,50); % use @f if defined in separate function
>> [t,y]
ans =
0
2.0000
0.0100
2.0600
0.0200
2.1218
0.0300
2.1855
0.0400
2.2510
0.0500
2.3185
:
:
0.4500
7.5632
0.4600
7.7901
0.4700
8.0238
0.4800
8.2645
0.4900
8.5124
0.5000
8.7678
An even longer output is obtained for larger values of N . To compare the two approximations with
N = 5 and N = 50 we plot both approximations on the same figure, together with the exact solution
y(t) = 2e3t . Note that we can no longer call the output simply [t,y] because every time we call the
function we will lose the output from the previous computations. Thus we will call [t5,y5] the output
from Euler with N = 5 and [t50,y50] the output with N = 50. The exact solution of the IVP is
y = 2e3t . We will store the exact solution in the vector y.
>>
>>
>>
>> [t5,y5] = euler(f,[0,.5],2,5); % use @f if defined in separate function
[t50,y50] = euler(f,[0,.5],2,50);
t = linspace(0,.5,100); y = 2*exp(3*t);
% evaluate the exact solution
plot(t5,y5,’ro-’,t50,y50,’bx-’,t,y,’k-’); axis tight; c 2016
Stefania Tracogna, SoMSS, ASU 3 MATLAB sessions: Laboratory 3
>> legend(’Euler N = 5’,’Euler N = 50’,’Exact’,’location’,’northwest’); Euler N = 5
Euler N = 50
Exact 8 7 6 5 4 3 2
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Figure L3a: Euler’s method applied to y 0 = 3y, y(0) = 2 with step h = 0.1 and h = 0.01, compared to
the exact solution.
IMPORTANT REMARK
When using 5 steps of size h = 0.1 the approximation of the exact value y(0.5) = 2e1.5 ' 8.9634 is
stored in y5(6). On the other hand when using 50 intervals of size h = 0.01, it is stored in y50(51).
To avoid confusion we can reference these values by y5(end) and y50(end), respectively. The exact
value of the function at t = 0.5 can be retrieved using y(end).
We can find the error (exact - approximation) at t = 0.5 by entering
>> e5 = y(end) - y5(end)
e5 =
1.5375
>> e50 = y(end) - y50(end)
e50 =
0.1956 % error with N = 5 % error with N = 50 and we can calculate the ratio of the errors to see how the approximation improves when the number of
steps increases by a factor 10
>> ratio = e5/e50
ratio =
7.8619 EXERCISES
1. (a) If you haven’t already done so, enter the following commands:
>> f=@(t,y)(3*y);
>> t=linspace(0,.5,100); y=2*exp(3*t); % defines the exact solution of the ODE
>> [t50,y50]=euler(f,[0,.5],2, 50); % solves the ODE using Euler with 50 steps
Determine the Euler’s approximation for N = 500 and N = 5000 and fill in the following
table with the values of the approximations, errors and ratios of consecutive errors at t = 0.5.
Some of the values have already been entered based on the computations we did above.
Include the table in your report, as well as the MATLAB commands used to find the entries.
c 2016
Stefania Tracogna, SoMSS, ASU 4 MATLAB sessions: Laboratory 3 N approximation error 5 7.4259 1.5375 50 ratio 0.1956 7.8619 500
5000
(b) Examine the last column. How does the ratio of consecutive errors relate to the number of
steps used? Your answer to this question should confirm the fact that Euler’s method is of “
order h ”, that is, every time the stepsize is decreased by a factor k, the error is also reduced
(approximately) by the same factor.
(c) Recall the geometrical interpretation of Euler’s method based on the tangent line. Using this
geometrical interpretation, can you explain why the Euler approximations underestimate the
solution in this particular example?
2. Consider the IVP
y 0 = −3y, y(0) = 2
for 0 ≤ t ≤ 5.
The exact solution of the ODE is y = 2e−3t .
The goal of this exercise is to visualize how Euler’s method is related to the slope field of the differential equation. In order to do this we will plot the direction field together with the approximations
and the exact solution.
(a) To plot the slope field we will use the MATLAB commands meshgrid and quiver. Enter the
following commands:
t = 0:.3:5; y = -20:2:25 ;
[T,Y]=meshgrid(t,y);
dT = ones(size(T));
dY = -3*Y;
quiver(T,Y,dT,dY)
axis tight
hold on %
%
%
%
%
% define grid of values in t and y direction
creates 2d matrices of points in the ty-plane
dt=1 for all points
dy = -3*y; this is the ODE
draw arrows (t,y)->(t+dt, t+dy)
adjust look After running these commands you should get the graph of the slope field.
(b) Use linspace to generate a vector of 100 t-values between 0 and 5. Evaluate the solution y
at these t-values and plot it in black together with the direction field (use ’linewidth’,2).
(c) Enter the function defining the ODE as anonymous.
Use euler.m with N = 6 to determine the approximation to the solution. Plot the approximated points in red (use ’ro-’,’linewidth’,2 as line-style in your plot), together with the
exact solution and the direction field.
You should get Figure L3b. Based on the slope field and the geometrical meaning of Euler’s
method explain why the approximations are so inaccurate for this particular value of the
stepsize.
(d) Open a new figure by typing figure. Plot again the direction field but in a different window:
t = 0:.3:5; y = -1:.4:2 ; Repeat part (b) and repeat part (c) but this time with
N = 12.
You should get Figure L3c. Comment on the result from a geometrical point of view.
Note: Brief explanations of the commands quiver and meshgrid are included in Appendix A. In
Appendix B we describe the Graphical User Interface dfield8 for plotting slope fields.
c 2016
Stefania Tracogna, SoMSS, ASU 5 MATLAB sessions: Laboratory 3 20 15 10 5 0 -5 -10 -15 -20
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Figure L3b: Euler’s method applied to y 0 = −3y, y(0) = 2 N = 6, compared to the exact solution.
2 1.5 1 0.5 0 -0.5 -1
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Figure L3c: Euler’s method applied to y 0 = −3y, y(0) = 3 N = 12, compared to the exact solution. Improved Euler’s Method
The improved Euler’s method is obtained by using an average of f values, i.e., a slope average:
y(t1 ) ' y(t0 ) + h
(f (t0 , y(t0 )) + f (t1 , y(t1 )))
2 (L3.2) Then substitute y(t0 ) = y0 and define y1 ' y(t1 ):
y1 = y0 + h
(f (t0 , y0 ) + f (t1 , y1 )) .
2 (L3.3) The equation (L3.3) defines the trapezoidal method. Unfortunately, this formula defines y1 only implicitly, i.e., y1 appears on both sides of the equality so that an equation must be solved to obtain y1 . To
avoid this problem, and since we already have made an approximation to get (L3.3), we replace y1 on
the right-hand side by the approximation one would obtain by simply applying Euler’s method from
(t0 , y0 ). The resulting quantity


h
y1 = y0 +
f (t0 , y0 ) + f (t1 , y0 + hf (t0 , y0 ) )
(L3.4)
|
{z
}
2
Euler y1 from (L3.1) c 2016
Stefania Tracogna, SoMSS, ASU 6 MATLAB sessions: Laboratory 3
with t1 = t0 + h is the improved Euler approximation. This approximation can be thought of as a
correction to the Euler approximation. The iteration (L3.4) is then repeated to obtain y2 ' y(t2 ), . . .,
i.e.,
f1 = f (tn , yn )
f2 = f (tn + h, yn + hf1 )
yn+1 = yn + h2 (f1 + f2 )
tn+1 = tn + h
Minor modifications are made to the function euler.m to implement the improved Euler method. EXERCISES
3. Modify the M-file euler.m to implement the algorithm for Improved Euler. Call the new file
impeuler.m (include the file in your report).
Test your code for the ODE y 0 = 3y, y(0) = 2.
First enter the function f (t, y) = 3y as anonymous function and then enter the following:
>> [t5,y5] = impeuler(f,[0,.5],2,5); % use @f if defined in separate function
>> [t5,y5]
ans =
0
2.0000
0.1000
2.6900
0.2000
3.6181
0.3000
4.8663
0.4000
6.5451
0.5000
8.8032
Compare your output with the one above.
Note that the improved Euler approximation with 5 steps is already more accurate than the Euler
approximation with 50 steps! (hence the “improved”)
4. Consider the IVP y 0 = 3y, y(0) = 2 0 ≤ t ≤ 0.5 (a) Determine the Improved Euler’s approximation for N = 50, N = 500 and N = 5000. Fill
in the following table with the values of the approximations, errors and ratios of consecutive
errors at t = 0.5. One of the values has already been entered based on the computations we
did above. Recall that the exact solution to the ODE is y = 2e3t .
Include the table in your report, as well as the MATLAB commands used to find the entries.
N approximation 5 8.8032 error ratio 50
500
5000
(b) Examine the last column. How does the ratio of the errors relate to the number of steps used?
Your answer to this question should confirm the fact that Improved Euler’s method is of
“ order h2 ”, that is, every time the stepsize is decreased by a factor k, the error is reduced
(approximately) by a factor of k 2 .
5. Repeat Problem 2 for Improved Euler. Compare the results with the ones obtained with Euler’s
method.
c 2016
Stefania Tracogna, SoMSS, ASU 7 MATLAB sessions: Laboratory 3 APPENDIX A: The commands meshgrid and quiver
The function meshgrid: This command is especially important because it is used also for plotting 3D
surfaces. Suppose we want to plot a 3D function z = x2 − y 2 over the domain 0 ≤ x ≤ 4 and 0 ≤ y ≤ 4.
To do so, we first take several points on the domain, say 25 points. We can create two matrices X and
Y , each of size 5 × 5, and write the xy- coordinates of each point in these matrices. We can then evaluate
z at these xy values and plot the resulting surface. Creating the two matrices X and Y is much easier
with the meshgrid command:
x = 0:4;
% create a vector x = [0, 1, 2, 3, 4]
y = -4:2:4;
% create a vector y = [-4, -2, 0, 2, 4]
[X, Y] = meshgrid(x,y); % create a grid of 25 points and store
% - their coordinates in X and Y
Try entering plot(X,Y,’o’,’linewidth’,2) to see a picture of the 25 points.
Then, just for fun try entering
Z = X.^2 - Y.^2;
surf(X,Y,Z);
to get a picture of the surface representing z = x2 − y 2 . (The graph will be quite rough because of the
limited number of points, but you should nonetheless recognize a “saddle”).
Thus, in our code for the direction field, the meshgrid command is used to generate the points in the
xy plane at which we want to draw the arrows representing the slope or tangent line to the solution at
the point. Type help meshgrid for more information.
The function quiver: The command quiver(X,Y,U,V) draws vectors (arrows) specified by U and
V at the points specified by X and Y . In our examples the vectors drawn are (dT, dY) where dT =1
and dY = -3*y, thus these vectors are in the direction of the slope at the given point. The arrows are
automatically scaled so as not to overlap. The matrices X and Y are built using the meshgrid command
as explained above. Type help quiver for more explanation. APPENDIX B: Plotting direction fields with dfield8
The Graphical User Interface dfield8 originally from J. Polking can also be used to plot the direction
field and selected solution curves. After downloading the file, enter dfield8 in the command window.
A new window will pop up, one that looks like Figure L3d Figure L3d: The dfield8 setup GUI
c 2016
Stefania Tracogna, SoMSS, ASU 8 MATLAB sessions: Laboratory 3 ODEs with up to two parameters can be entered in the Setup window. For instance, we can enter
the ODE y 0 = −2y, identify the independent variable as t and choose the display window’s dimension
(0 ≤ t ≤ 10 and −4 ≤ y ≤ 4). Click on Proceed to see the slope field. A sample display window is shown in Figure L3e. Figure L3e: The dfield8 direction field plot
Solutions curves can be drawn by clicking on the display window. Specific initial conditions can be
entered by clicking on Options ->Keyboard Input. A new window will arise prompting the user to
input initial values for the independent and dependent variables as an initial condition for the IVP.
More information about various options are available at http://math.rice.edu/∼dfield. c 2016
Stefania Tracogna, SoMSS, ASU 9