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MA2213 Lecture 10. ODE. Topics. Importance p. 367-368. Introduction to the theory p. 368-373. Analytic solutions p. 368-372. Existence of solutions p. 372. Direction fields p. 376-379. Numerical methods. Forward Euler p. 383. Richardson’s extrapolation formula p. 391.

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Ma2213 lecture 10


Importance p. 367-368

Introduction to the theory p. 368-373

Analytic solutions p. 368-372

Existence of solutions p. 372

Direction fields p. 376-379

Numerical methods

Forward Euler p. 383

Richardson’s extrapolation formula p. 391

Systems of equations p. 432

Two point boundary value problems p. 442

Ma2213 lecture 10


“Differential equations are among the most

important mathematical tools used in producing

models of physical and biological sciences, and

engineering.” They can be classified into:

Ordinary :

have 1 independent variable

Partial :

have > 1 independent variable

wave equation

heat equation

Ma2213 lecture 10

Analytic Solutions


Integrating Factors

Separation of Variables

Ma2213 lecture 10

Existence of Solutions

Theorem 8.1.3 (page 372) Let


be continuous functions of


at all points

in some neighborhood of

Then there is a unique function


defined on some interval

Example 8.1.4 (p. 372) The initial value problem

admits the solution

Ma2213 lecture 10

Direction Fields

At any point (x,y) on the graph of a solution of the

the slope is


Direction fields illustrate these slopes.

Example 8.1.8 (page 376) Consider

The slope at (x,y) is y (independent of x).

[x,y] = meshgrid(-2:0.5:2,-2:0.5:2);

dx = ones(9); % Generates a mesh of 1’s

dy = y; quiver(x,y,dx,dy);

xlabel('x coordinate axis')

ylabel(y coordinate axis')

title(' direction field v = [1 y]^T ')

Ma2213 lecture 10

Solution Curves

The solutions of


hold on

x = -2:0.01:1;;

y1 = exp(x);



hold off

Forward euler method
Forward Euler Method


be the solution of the initial value problem

Numerical methods will give an approximate solution at

a discrete set of nodes

For simplicity we choose evenly spaced nodes

Taylor’s approximation

gives the forward Euler method for approximations

Examples of forward euler method
Examples of Forward Euler Method


be the solution of the initial value problem

For nodes

forward Euler method gives approximations


Error of forward euler method
Error of Forward Euler Method


be the solution of the initial value problem

For nodes

the exact solution is

and the numerical approximation equals

therefore we have the error

Richardson extrapolation
Richardson Extrapolation

It can be shown, using an analysis similar to the one on

the preceding page, that the numerical solution obtained

using the forward Euler method with step size satisfies

therefore, the approximation using step size satisfies

These two estimates can be combined to give

which has a much smaller error than

This process can be extended as in slides 36,40 Lect 7.

Systems of equations
Systems of Equations

The general form of a system of two first-order

differential equations is (page 432)

This system can be simply represented using vectors

Systems of equations1
Systems of Equations

For the system of two equations in slide 3

and the solution of the initial value problem is

Systems of equations2
Systems of Equations

Y0 = [1;0];

h = 0.001;

N = round(1000*2*pi);

x0 = 0;

Y(:,1) = Y0; x(1) = x0;

for n = 1:N

x(n+1) = x(n)+h;

f = [-Y(2,n);Y(1,n)];

Y(:,n+1) = Y(:,n) + h*f;


figure(1); plot(x,Y(1,:),x,Y(2,:)); grid;

title(‘approximate solution’)

figure(2); plot(x,Y(1,:)-cos(x),x,Y(2,:)-sin(x)); grid;


Two point boundary value problems
Two-Point Boundary Value Problems

A second-order linear boundary value problem (p. 442)

can be discretized. We choose



to obtain linear equations for

Homework due lab 5 week 13 12 16 november
Homework Due Lab 5 (Week 13, 12-16 November)

1. The logistic equation

was proposed as a model for population growth

by Peirre Verhulst in 1838. Draw its direction fields

and solution curves for Y(0) = .5K and Y(0)=1.5K.

2. Implement the forward Euler method to compute the two solutions above. Use plots and tables to show how Richardson extrapolation decreases the errors.

3. Study the Lotka-Volterra predator-prey model on page 433 and then do problem 9 on page 441. Extra Credit: Use the secant method to compute the smallest x > 0 so that Y(x) = Y(0)where Y is the solution in part (b).

4. Write the MATLAB Program on page 445, study pages 446-448, and do problem 7 on page 449.

(Extrapolated means Richardson extrapolated)