Introduction | Exercise 1 |

Euler's method review | Exercise 2 |

The Euler Halfstep (RK2) Method | Exercise 3 |

Runge-Kutta Methods | Exercise 4 |

The Midpoint Method | Exercise 5 |

Adams-Bashforth Methods | Exercise 6 |

Introduction

In this lab we consider solution methods for ordinary differential equations (ODEs). We will be looking at two classes of methods that excel when the equations are smooth and derivatives are not too large. This lab will take two class sessions. If you print this lab, you may prefer to use the pdf version.

The lab begins with an introduction to Euler's method for ODEs. Euler's method is the simplest approach to computing a numerical solution of an initial value problem. However, it has about the lowest possible accuracy. If we wish to compute very accurate solutions, or solutions that are accurate over a long interval, then Euler's method requires a large number of small steps. Since most of our problems seem to be computed ``instantly,'' you may not realize what a problem this can become when solving a ``real'' differential equation.

Applications of ODEs are divided between ones with space as the independent variable and ones with time as the independent variable. We will use as independent variable consistently. Sometimes it will be interpreted as a space variable ( -axis) and sometimes as time.

A number of methods have been developed in the effort to get solutions that are more accurate, less expensive, or more resistant to instabilities in the problem data. Typically, these methods belong to ``families'' of increasing order of accuracy, with Euler's method (or some relative) often being the member of the lowest order.

In this lab, we will look at ``explicit'' methods, that is, methods defined by an explicit formula for , the approximate solution at the next time step, in terms of quantities derivable from previous time step data. In a later lab, we will address ``implicit'' methods that require the solution of an equation in order to find . We will consider the Runge-Kutta and the Adams-Bashforth families of methods. We will talk about some of the problems of implementing the higher order versions of these methods. We will try to compare the accuracy of different methods applied to the same problem, and using the same number of steps.

Runge-Kutta methods are ``single-step'' methods while Adams-Bashforth methods are ``multistep'' methods. Multistep methods require information from several preceeding steps in order to find and are a little more difficult to use. Nonetheless, both single and multistep methods have been very successful and there are very reliable Matlab routines (and libraries for other languages) available to solve ODEs using both types of methods.

Euler's method

A very simple ordinary differential equation (ODE) is the *explicit
scalar first-order initial value problem*:

Here is shorthand for the derivative . The equation is

An *analytic solution* of an ODE is a formula
,
that we can evaluate, differentiate, or analyze in any way we want.
Analytic solutions can only be determined for a small
class of ODE's.

A ``numerical solution'' of an ODE is simply a table of
abscissæ and approximate values
that approximate the value of an analytic solution. This
table is usually accompanied by some rule for interpolating
solution values between the abscissæ.
With rare exceptions, a numerical solution is *always wrong*; the
important question is, how wrong is it? One way to pose this question
is to determine how close the computed values
are to the analytic solution, which we might write as
.

The simplest method for producing a numerical solution of an ODE
is known as *Euler's method*. Euler's method is discussed in
Atkinson, Section 6.2, p. 341ff. Given a solution value
,
we estimate the solution at the next abscissa by:

(The stepsize is denoted in Atkinson and here. Sometimes it is denoted .) We can take as many steps as we want with this method, using the approximate answer from one step as the starting point for the next step.

Typically, Euler's method will be applied to systems of ODEs rather than a single ODE. This is because higher order ODEs can be written as systems of first order ODEs. The following Matlab function m-file implements Euler's method for a system of ODEs.

function [ x, y ] = euler ( f, xRange, yInitial, numSteps ) % [ x, y ] = euler ( f, xRange, yInitial, numSteps ) uses % Euler's explicit method to solve a system of first-order ODEs % y'=f(x,y). % f = name of an m-file with signature % yprime = f(x,y) % to compute the right side of the ODE as a row vector % xRange = [x1,x2] where the solution is sought on x1<=x<=x2 % yInitial = column vector of initial values for y at x1 % numSteps = number of equally-sized steps to take from x1 to x2 % x = row vector of values of x % y = matrix whose n-th row is the approximate solution at x(n). x=zeros(numSteps+1,1); x(1) = xRange(1); h = ( xRange(2) - xRange(1) ) / numSteps; y(1,:) = transpose(yInitial); for k = 1 : numSteps x(k+1) = x(k) + h; y(k+1,:) = y(k,:) + h * transpose(feval ( f, x(k), y(k,:) )); end

Warning: In the above code, the initial value is a
column vector, and the function that gets called returns a
column vector; however, the values are returned in the
rows of the matrix `m`!
The function `tranpose` is used instead of a prime
because a prime without a dot means ``Hermitian'' or ``adjoint'' or
``conjugate-transpose,'' but only a true transpose is needed here.
The `.'` operator could have been used, but is harder to read.

In the following exercise, you will use `euler.m` to
find the solution of the initial value problem

The exact solution of this IVP is .

**Exercise 1**:- If you have not done so already, copy (use cut-and-paste)
the above code into a file named
`euler.m`or download`euler.m`. - Copy the
following code into a Matlab m-file called
`expm_ode.m`.function yprime = expm_ode ( x, y ) % yprime = expm_ode ( x, y ) is the right side function for % the ODE y'=-y+x % x is the independent variable % y is the dependent variable % yprime is y' yprime = -y+x;

- Now you can use Euler's method to march
from
`y=yInit`at`x=0`:yInit = 1.0; [ x, y ] = euler ( 'expm_ode', [ 0.0, 2.0 ], yInit, numSteps );

for each of the values of`numSteps`in the table below. Use at least four significant figures when you record your numbers, and you can use the first line as a check on the correctness of the code. In addition, compute the error as the difference between your approximate solution and the exact solution at`x=2`,`y=2*exp(-2)+1`, and compute the ratios of the error for each value of`nstep`divided by the error for the succeeding value of`nstep`. As the number of steps increases, your errors should become smaller and the ratios should tend to a limit.Euler's explicit method numSteps Stepsize Euler Error Ratio 10 0.2 1.21474836 5.59222e-2 _________ 20 0.1 __________ __________ _________ 40 0.05 __________ __________ _________ 80 0.025 __________ __________ _________ 160 0.0125 __________ __________ _________ 320 0.00625 __________ __________

- Based on the ratios in the table, estimate the order of
accuracy of the method,
*i.e.,*estimate the exponent in the error estimate , where h is the step size. is an integer in this case. (Atkinson, Theorem 6.3).

- If you have not done so already, copy (use cut-and-paste)
the above code into a file named

The Euler Halfstep (RK2) Method

The ``Euler halfstep'' or ``RK2'' method is a variation of Euler's
method. It is one of a family of methods called ``Runge-Kutta''
methods, discussed by Atkinson in Section 6.10, and RK2 is the method
given in Equation (6.10.9). As part of each step of the method, an
auxiliary solution, one that we don't really care about, is computed
halfway, using Euler's method:

The derivative function is evaluated at this point, and used to take a full step from the original point:

Although this method uses Euler's method, it ends up having a higher order of convergence. Loosely speaking, the initial half-step provides additional information: an estimate of the derivative in the middle of the next step. This estimate is presumably a better estimate of the overall derivative than the value at the left end point. Atkinson shows that the per-step error is and, since there are steps to reach the end of the range, overall. Keep in mind that we do not regard the auxilliary points as being part of the solution. We throw them away, and make no claim about their accuracy. It is only the whole-step points that we want.

In the following exercise we compare the results of RK2 with Euler.

**Exercise 2**:- Write a Matlab function m-file named
`rk2.m`that implements the Euler halfstep (RK2) method sketched above in Equations (1) and (2). Keep the same calling parameters and results as for`euler.m`above. Keeping these the same will make it easy to compare different methods. The following model for the file is based on the`euler.m`file with the addition of the variables`xhalf`and`yhalf`representing the auxiliary variables and in Equation (1). Add comments to this outline, including explanations of all the variables in the signature line, and fill in expressions where`???`have been left.function [ x, y ] = rk2 ( f, xRange, yInitial, numSteps ) % comments including the signature, meanings of variables, % math methods, your name and the date x=zeros(numSteps+1,1); x(1) = xRange(1); h = ( xRange(2) - xRange(1) ) / numSteps; y(1,:) = transpose(yInitial); for k = 1 : numSteps x1 = ??? ; y1 = ??? ; x(k+1) = x(k) + h; y(k+1,:) = y(k,:) + h * transpose(feval( ??? )); end

- Use this file to compute the numerical solution of the model ODE
for the exponential,
`expm_ode.m`, from Exercise 1, from`x = 0.0`to`x = 2.0`, and with the same initial value as in Exercise 1, but using Euler's halfstep method, RK2, with stepsizes below. For each case, record the value of the numerical solution at`x = 2.0`; the error, that is, the difference between the numerical solution and the true solution at the end point`x=2`(`y=2*exp(-2)+1`); and, the ratios of the error for each value of`numSteps`divided by the error for the succeeding value of`numSteps`. Use at least 4 significant digits when you record values.RK2 numSteps Stepsize RK2 Error Ratio 10 0.2 1.27489606 4.2255e-03 __________ 20 0.1 __________ __________ __________ 40 0.05 __________ __________ __________ 80 0.025 __________ __________ __________ 160 0.0125 __________ __________ __________ 320 0.00625 __________ __________

- Based on the ratios in the table, estimate the order of
accuracy of the method,
*i.e.,*estimate the exponent in the error estimate . is an integer in this case. - Compare errors from Euler's method (Exercise 1) and Euler's
halfstep method for this problem. You should clearly see that
Euler's halfstep method (RK2) converges much faster than Euler's method.
Euler numSteps Stepsize Error RK2 Error 10 0.2 __________ __________ 20 0.1 __________ __________ 40 0.05 __________ __________ 80 0.025 __________ __________ 160 0.0125 __________ __________ 320 0.00625 __________ __________

- Based on the above table, roughly how many steps does Euler require to
achieve the accuracy that RK2 has for
`numSteps=10`? - You have already found the the error for Euler's method is
proportional to
and the error for RK2 is
proportional to
.
Based on one or both of these estimates,
roughly how many steps would Euler require to achieve the accuracy
That RK2 has for
`numSteps=320`?

`rk2.m`file when you send me your summary.- Write a Matlab function m-file named

Runge-Kutta Methods

The idea in Euler's halfstep method is to ``sample the water'' between where we are and where we are going. This gives us a much better idea of what is doing, and where our new value of ought to be. Euler's method (``RK1'') and Euler's halfstep method (``RK2'') are the junior members of a family of ODE solving methods known as ``Runge-Kutta'' methods.

To develop a higher order Runge-Kutta method, we sample the
derivative function
at even more ``auxiliary points''
between our last computed solution and the next one. These points are
*not* considered part of the solution curve; they are just a
computational aid. The formulas tend to get complicated, but let me
describe the next one, at least.

The third order Runge Kutta method ``RK3,'' given
,
and a stepsize
, computes two intermediate points:

and then estimates the solution as:

The global accuracy of this method is , and so we say it has ``order'' 3 method. Higher order Runge Kutta methods have been computed, with those of order 4 and 5 the most popular.

**Exercise 3**:- Write a Matlab function m-file called
`rk3.m`with the signaturefunction [ x, y ] = rk3 ( f, xRange, yInitial, numSteps ) % comments including the signature, meanings of variables, % math methods, your name and the date

that implements the above algorithm. You can use`rk2.m`as a model. - Repeat the numerical experiment in Exercise 2 (using
`expm_ode`) and fill in the following table. Use the first line of the table to confirm that you have written the code correctly.RK3 numSteps Stepsize RK3 Error Ratio 10 0.2 1.27045877 2.1179e-04 __________ 20 0.1 __________ __________ __________ 40 0.05 __________ __________ __________ 80 0.025 __________ __________ __________ 160 0.0125 __________ __________ __________ 320 0.00625 __________ __________

- Based on the ratios in the table, estimate the order of
accuracy of the method,
*i.e.,*estimate the exponent in the error estimate . is an integer in this case.

- Write a Matlab function m-file called

**Exercise 4**:The equation describing the motion of a pendulum can be described by the single dependent variable representing the angle the pendulum makes with the vertical. The coefficients of the equation depend on the length of the pendulum, the mass of the bob, and the gravitational constant. Assuming a coefficient value of 1.5, the equation is

0

This second order equation can be written as a system

(Recall that this transformation is accomplished by the change of variables and . See the discussion in Atkinson on page 340.)- Write a function m-file named
`pendulum_ode.m`with signaturefunction yprime = pendulum_ode(x,y) % comments including the signature, meanings of variables, % math methods, your name and the date

Be sure to put comments after the signature line and wherever else they are needed. Be sure that you return a*column*vector. - This exercise is the first time you will be applying
`euler.m`and`rk3.m`to a system of equations. You may have to do some extra debugging! Compare the solutions using the first order Euler method, and the third order RK3 method, over the interval 0 to 25, with initial condition`y=[1;0]`, and using 100 steps for each method. Fill in the following table.x Euler RK3 0.00 1.00 0.00 1.00 0.00 10.00 __________ __________ __________ __________ 20.00 __________ __________ __________ __________ 25.00 __________ __________ __________ __________

- Note that conservation of energy guarantees that should stay between -1 and 1. Compare the plots of the two solutions. Can you see anything in the plot of the Euler's method solution that might indicate that it is wrong, aside from conservation of energy? You do not have to send me a copy of the plot.
- Solve the ODE using Euler's method again, but use 10,000 steps instead of 100. Plot both the refined Euler solution and the original RK3 solution on the same plot and include it with your summary. Your plots should illustrate the fact that refining the mesh helps Euler, but it is still inaccurate compared with RK3. Please include this plot with your summary.

- Write a function m-file named

Stability

You have seen a considerable amount of theory about stability
of methods for ODEs, and you will see more in the future.
Explicit methods generally are conditionally stable, and require
that the step size be smaller than some critical value in
order to converge. It is interesting to see what happens when
this stability limit is approached and exceeded. In the following
exercise you will drive Euler's method and Runge-Kutta third-order
methods unstable using the `expm_ode` function in
a previous exercise. You should observe very similar behavior
of the two methods. This behavior, in fact, is similar to that
of most conditionally stable methods.

**Exercise 5**: Each part of this exercise results in a plot. Please include each of the plots with your summary.- Use
`euler`to solve the ODE using`expm_ode`, using`numSteps=20`and starting from`y=1`over the interval [0,10]. This solution is well within the stable regime. Plot this case. - Same, but over the intervals [0,20], [0,25], [0,30], [0,35],
and [0,40]. Continue using
`numSteps=20`. The consequence of increasing the interval length is to increase the step size. Plot these five solutions on the same plot. You should observe increasing ``plus-minus'' oscillations in the solutions. - Same, but over the interval [0,45]. This solution ``explodes'' in a ``plus-minus'' fashion. Plot this case.
- Use
`rk3`to solve the ODE using`expm_ode`, using`numSteps=20`, and starting from`y=1`over the intervals [0,35], [0,40], [0,45], and [0,50]. Plot these on the same plot. The first of these is stable. - Same, but over the interval [0,55]. This solution ``explodes'' in a ``plus-minus'' fashion. Plot it.

- Use

The message you should get from the previous exercise is that you can observe poor solution behavior when you are near the stability boundary. The ``poor behavior'' appears as a ``plus-minus'' oscillation that can shrink, grow, or remain of constant amplitude (unlikely, but possible). It can be tempting to accept solutions with small ``plus-minus'' oscillations that die out, but it is dangerous, especially in nonlinear problems, where the oscillations can cause the solution to move to a nearby curve with different initial conditions that has very different qualitative behavior from the desired solution.

Adams-Bashforth Methods

Like Runge-Kutta methods, Adams-Bashforth methods want to estimate the behavior of the solution curve, but instead of evaluating the derivative function at new points close to the next solution value, they look at the derivative at old solution values and use interpolation ideas, along with the current solution and derivative, to estimate the new solution. This way they don't compute solutions at auxiliary points and then throw the auxiliary values away. The savings can result in increased efficiency.

Looked at in this way, the Euler method is the first order
Adams-Bashforth method, using *no* old points at all, just the
current solution and derivative. The second order method, which we'll
call ``AB2,'' adds the derivative at the previous
point into the interpolation mix. We might write the formula this
way:

The AB2 method requires derivative values at two previous points, but we only have one. If we simply used an Euler step, we would pick up a relatively large error on the first step, which would pollute all our results. In order to get a reasonable starting value, we should use the RK2 method, whose per-step error is order , the same as the AB2 method.

The following is a complete version of Matlab code for the Adams-Bashforth second-order method.

function [ x, y ] = ab2 ( f, xRange, yInitial, numSteps ) % [ x, y ] = ab2 ( f, xRange, yInitial, numSteps ) uses % Adams-Bashforth second-order method to solve a system % of first-order ODEs y'=f(x,y). % f = name of an m-file with signature % yprime = f(x,y) % to compute the right side of the ODE as a row vector % % xRange = [x1,x2] where the solution is sought on x1<=x<=x2 % yInitial = column vector of initial values for y at x1 % numSteps = number of equally-sized steps to take from x1 to x2 % x = column vector of values of x % y = matrix whose k-th row is the approximate solution at x(k). x=zeros(numSteps+1,1); x(1) = xRange(1); h = ( xRange(2) - xRange(1) ) / numSteps; y(1,:) = transpose(yInitial); yprime(1,:) = transpose(feval ( f, x(1), y(1,:) )); k = 1; xhalf = x(k) + 0.5 * h; yhalf = y(k,:) + 0.5 * h * yprime(k,:); yprime1 = transpose(feval ( f, xhalf, yhalf )); x(k+1) = x(k) + h; y(k+1,:) = y(k,:) + h * yprime1; yprime(k+1,:) = transpose(feval ( f, x(k+1), y(k+1,:) )); for k = 2 : numSteps x(k+1) = x(k) + h; y(k+1,:) = y(k,:) + ... h * ( 3.0 * yprime(k,:) - yprime(k-1,:) ) / 2.0; if k<numSteps yprime(k+1,:) = transpose(feval ( f, x(k+1), y(k+1,:) )); end end

**Exercise 6**:- Copy the code to a file called
`ab2.m`or download`ab2.m`. - Take a minute to look over
this code and see if you can understand what is happening. Insert the
following two comment lines into the code in the correct locations:
% The Adams-Bashforth algorithm starts here % The Runge-Kutta algorithm starts here

Be sure to include a copy of the commented code in your summary. - The temporary variable
`yprime`has been introduced here but was not needed in the Euler, RK2 or RK3 methods. What is its use here? (No more than two sentences.) - If
`numSteps`is 100, then*exactly*how many times will we call the derivative function`f`? - Use
`ab2`to compute the numerical solution of the ODE for the exponential (`expm_ode`) from`x = 0.0`to`x = 2.0`, starting at`y=1`with stepsizes of 0.2, 0.1, 0.05, 0.025, 0.0125 and 0.00625. For each case, record the value of the numerical solution at`x = 2.0`, and the error, and the ratios of the errors. The first line of the table can be used to verify that your code is operating correctly.AB2 numSteps Stepsize AB2(x=2) Error(x=2) Ratio 10 0.2 1.28013993 9.4694e-03 __________ 20 0.1 __________ __________ __________ 40 0.05 __________ __________ __________ 80 0.025 __________ __________ __________ 160 0.0125 __________ __________ __________ 320 0.00625 __________ __________

- Based on the ratios in the table, estimate the order of
accuracy of the method,
*i.e.,*estimate the exponent in the error estimate . is an integer in this case.

- Copy the code to a file called

Adams-Bashforth methods try to squeeze information out of old
solution points. For problems where the solution is smooth, these
methods can be highly accurate and efficient. Think of efficiency in
terms of how many times we evaluate the derivative function. To
compute `numSteps` new solution points, we only have to compute roughly
`numSteps` derivative values, no matter what the order of the method
(remember that the method saves derivative values at old points). By
contrast, a third order Runge-Kutta method would take roughly `3*numSteps`
derivative values. In comparison with the Runge Kutta method,
however, the old solution points are significantly further away from
the new solution point, so the data is less reliable and a little
``out of date.'' So Adams-Bashforth methods are often unable
to handle a solution curve which changes its behavior over a short
interval or has a discontinuity in its derivative. It's important to
be aware of this tradeoff between efficiency and reliability!

Back to MATH2071 page.

Mike Sussman 2007-12-25