CHAPTER 4: MATHEMATICAL MODELING WITH MATLAB
Lecture 4.2: Summation rules for numerical integration
Trapezoidal, Simpson and midpoint rules for integrals:
Problem: Given a set of data points:
(x0,y0); (x1,y1); (x2,y2); …(xn,yn)
Suppose the data points represent a function y = f(x). Suppose also that the data points are equally spaced with
constant step size h = x1 - x0. Find a numerical approximation for the integral, which is the signed area under
the curve y = f(x) between the end points (x0,y0) and (xn,yn).
Example: Linear electrical circuits can be easily miniaturized if they do not include large and bulky
inductors. When a current I = I(t) is applied to the input port of a simple resistor-capacitor one-port network,
the voltage V = V(t) develops across the port terminals. At the time instance t = T, the voltage output is
determined as a sum of the voltage drop across the resistor (which is R I(T)) and of the voltage drop across
T
the capacitor (which is V0 +
 dtI (t ) / C), where V
0
is an initial voltage. If the input current I(t) can be
0
measured at different times t = tk for k = 0,1,2,…,n, such that t0 = 0 and tn = T, them the integral is to be
evaluated numerically from the given data set.
Solution: The function y = f(x) is either analytically defined or given in a tabular form. The numerical
integration is based on the use of numerical interpolation y = Pn(x) fitted to the given data points and
analytical integration of the polynomial Pn(x). This is a so-called Newton-Cotes integration algorithm. The
most important Newton-Cotes integration formulas are trapezoidal, Simpson and midpoint rules.
Trapezoidal rule:
A linear interpolation between the points (x0,y0) and
(x1,y1) approximates the area under the curve y = f(x)
by the area of the trapezoid:
x1
 f ( x)dx
 Itrapezoidal(f;x0,x1) =
x0
h
( y1 + y0 )
2
Trapezoidal rule is popular in numerical integration
as it is a simple method. Although its accuracy is
low, the accuracy can be controled by doubling the
number of elementary subintervals (trapezoids).
Simpson rule:
A quadratic interpolation between the points (x0,y0)
(x1,y1), and (x2,y2) approximates the area under the
curve y = f(x) by the area under the interpolant:
x2
 f ( x)dx
x0
 ISimpson(f;x0,x2) =
h
( y0 + 4y1 + y2 )
3
Simpson rule is popular because of high accuracy of
numerical integration compared to the trapezoidal
rule.
Mid-point rule:
A constant interpolation between the point (x1,y1),
centered in the interval between (x0,y0) and (x2,y2),
approximates the area under the curve y = f(x) by the
area of a rectangle centered at the midpoint:
x2
 f ( x)dx
 Imid-point(f;x0,x2) = 2h y1
x0
Mid-point rule is popular in numerical integration of
functions with singularities at the end of the interval.
It has the same accuracy as the trapezoidal rule and is
often used in combination with the trapezoidal rule
for computations of integrals near singularities.
h = 0.1; x0 = 0; x1 = x0+h; x2 = x0+2*h;
% the three data points are taken on [0,1] with equal step size
y0 = sqrt(1-x0^2); y1 = sqrt(1-x1^2); y2 = sqrt(1-x2^2);
yIexact = quad('sqrt(1-x.^2)',x0,x2); % 'exact answer' is computed by MATLAB
yItrap = h*(y0+y1+y1+y2)/2; % trapezoidal rule for two subintervals with h
yIsimp = h*(y0+4*y1+y2)/3; % Simpson rule for two subintervals with h
yImid = 2*h*y1; % mid-point rule for two subintervals with h
fprintf('Exact = %6.6f\nTrapezoidal = %6.6f\nSimpson = %6.6f\nMid-point =
%6.6f',yIexact,yItrap,yIsimp,yImid);
Exact = 0.198659
Trapezoidal = 0.198489
Simpson = 0.198658
Mid-point = 0.198997
Composite summation rules:
The summation rules are extended to multiple intervals, when the function y = f(x) is represented by (n+1)
data points with constant step size h. The composite rule is obtained by summating areas of all n individual
areas.

Composite trapezoidal rule:
xn
 f ( x)dx
 Itrapezoidal(f;x0,x1,…,xn) =
x0

Composite Simpson rule:
xn
 f ( x)dx
 Isimpson(f;x0,x1,…,xn) =
x0

h
( y0 + 2 y1 + 2 y2 + … + 2 yn-1 + yn )
2
h
( y0 + 4 y1 + 2 y2 + 4 y3 + 2 y4 + … + 2 yn-2 + 4 yn-1 + yn )
3
Composite mid-point rule:
xn
 f ( x)dx
x0
 Imid-point(f;x0,x1,…,xn) = 2h ( y1 + y3 + … + yn-3 + yn-1 )
For the composite Simpson and mid-point rules, the total interval between x  [x0,xn] has to be divided into
even number of subintervals.
Errors of numerical integration:
Numerical integration is much more reliable process compared to numerical differentiation. Rounding errors
in computing sums in numerical integration are always constant, which are independent neither of the
integration rule nor from the number of subintervals for summation. Truncation errors can be reduced with
the use of more accurate summation rules for numerical integration.
Consider the equally spaced data points with constant step size: h = x2 – x1 = x1 – x0. The theory based on the
Taylor expansion method shows the following local truncation errors:

Trapezoidal rule:
x1

f(x)dx – Itrapezoidal(f;x0,x1 ) = -
x0
h3
f''(x), x  [x0,x1]
12
The truncation error of the trapezoidal rule is proportional to h3, i.e. it has the order of O(h3). The error is also
proportional to the second derivative of the function f(x) at an interior point x of the integration interval.
Thus, the trapezoidal rule is exact for a linear function f(x).

Simpson rule:
x2 1

x0
h5
f(x)dx – ISimpson(f;x0,x2 ) = f''''(x), x  [x0,x2]
90
The truncation error of the Simpson rule is proportional to h5 rather than h3, i.e. it has the order of O(h5). The
error is also proportional to the fourth derivative of the function f(x) at an interior point x of the integration
interval. The Simpson rule is exact for polynomial functions f(x) of order m = 0,1,2,3.

Mid-point rule:
x2 1

f(x)dx – Imid-point(f;x0,x2 ) =
x0
h3
f''(x), x  [x0,x2]
3
The truncation error of the mid-point rule is as bad as that of the trapezoidal rule.
The global truncation error is computed for composite integration rules when an interval between x [x0,xn] is
divided into n partial subintervals. The global truncation error is obtained by summation of local truncation
errors and all local rounding errors:

Composite trapezoidal rule:
xn
etrapezoidal = |

x0
h2
(xn – x0) + eps (xn – x0),
f ( x)dx - Itrapezoidal(f;x0,x1,…,xn) | < M2
12
where M2 = max | f''(x)|. The global truncation error is proportional to the length of the integration interval
and is order of O(h2).

Composite Simpson rule:
xn
eSimpson = |

x0
f ( x)dx - ISimpson(f;x0,x1,…,xn) | < M4
h4
(xn – x0) + eps (xn – x0),
180
where M4 = max | f''''(x)|. The global truncation error is proportional to the length of the integration interval
and is order of O(h4).
h = 0.1; k = 1;
while (h > 0.0000001)
x = 0 : h : 1; y = sqrt(1.-x.^2); n = length(x)-1;
yIexact = pi/4; % exact integral, 1/4 of area of a unit disk
yItrap = h*(y(1)+2*sum(y(2:n))+y(n+1))/2; % composite trapezoidal rule
yIsimp = h*(y(1)+4*sum(y(2:2:n))+2*sum(y(3:2:n-1))+y(n+1))/3;
yImid = 2*h*sum(y(2:2:n)); % composite mid-point rule
eItrap(k) = abs(yItrap-yIexact); eIsimp(k) = abs(yIsimp-yIexact);
eImid(k) = abs(yImid-yIexact); hst(k) = h; h = h/2; k = k+1;
end,
plot(hst,eItrap,'g:',hst,eIsimp,'b--',hst,eImid,'r');
0.025
0.02
0.015
0.01
0.005
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
.
If the step size h between two points becomes smaller, the truncation error of the summation rule decreases. It
decreases faster for Simpson rule and slower for trapezoidal and mid-point rule. For example, if h is halved,
the global truncation error of the Simpson rule is reduced by a factor of 16, while the global truncation errors
of the trapezoidal and mid-point rules are reduced only by a factor of 4.
Since the rounding error is bounded by the integration interval multiplied by the mashine precision eps, the
error of numerical integration can be reduced to that constant global number.
T
Example: The numerical approximations of the integral
 dtI (t ) , where I(t) is the current in a resistor0
capacitor network, are obtained with step size h = 10 (green pluses) and with step size h = 5 (blue dots),
versus the exact integral (red solid curve). The composite trapezoidal rule's error reduces with smaller step
size h (blue dots are closer to the exact red curve compared to the green pluses). The figure also shows that
the global truncation error for the integral grows with the length of the interval T.
MATLAB numerical integration:



quad: evaluates numerically the integral of a function by using adaptive Simpson quadrature
quadl: evaluates numerically the integral of a function by using adaptive Lobatto quadrature
dblquad: evaluates the double integral of a function of two variables in a rectangular domain
% The function for numerical integration should be written as a MATLAB M-file
% The functon should accept a vector argument X and return a vector result Y
function [Y] = integrand(X)
% this M-file sets up a function y = f(x) = sqrt(1 + exp(x))
% which is the integrand for the integral to be evaluated by function "quad"
Y = sqrt(1 + exp(X));
% Compute the integral: I = int_0^2 sqrt(1 + exp(x)) dx
format long; I1 = quad(@integrand,0,2)
% the default tolerance is 10^(-6) for absolute error of numerical integration
tolerance = 10^(-8); I2 = quad(@integrand,0,2,tolerance)
tolerance = 10^(-12); I3 = quad(@integrand,0,2,tolerance)
I4 = quadl(@integrand,0,2)
h = 0.0001; x = 0 : h : 2; y = feval(@integrand,x); n = length(y)-1;
I5 = h*(y(1)+2*sum(y(2:n))+y(n+1))/2 % composite trapezoidal rule
I6 = h*(y(1)+4*sum(y(2:2:n))+2*sum(y(3:2:n-1))+y(n+1))/3 % composite Simpson rule
I7 = 2*h*sum(y(2:2:n)) % composite mid-point rule
I1 =
4.00699422404935
I2 =
4.00699422326706
I3 =
4.00699422325470
I4 =
4.00699422322700
I5 =
4.00699422402304
I6 =
4.00699422325470
I7 =
4.00699422171802
W1 = quad('sin(pi*x.^2)',0,1), W2 = quad(inline('sin(pi*x.^2)'),0,1)
% standard MATLAB functions can be used for numerical integration as strings
W1 =
0.5049
W2 =
0.5049
Romberg integration for higher-order Newton-Cotes integration formulas:
More accurate integration formulas with smaller truncation error can be obtained by interpolating several data
points with higher-order interpolating polynomials. For example, the third-order interpolating polynomial
P3(x) between four data points leads to the Simpson 3/8 rule:
x3
 f ( x)dx
 ISimpson 3/8(f;x0,x3) =
x0
3h
( y0 + 3y1 + 3y2 + y3 )
8
while the fourth-order interpolating polynomial P4(x) between five data points leads to the Booles rule:
x4
 f ( x)dx
 IBooles(f;x0,x4) =
x0
2h
( 7 y0 + 32 y1 + 12 y2 + 32 y3 + 7 y4 )
45
The higher-order integration formulas can be recovered with the use of the Romberg integration algorithm
based on the Richardson extrapolation algorithm.

Recursive integration formulas:
The composite trapezoidal rule has the global truncation error of order O(h2). Denote the composite
trapezoidal rule for the integral of f(x) between [x0,xn] as R1(h). Compute two numerical approximations of
the integral with two step sizes h and 2h:
xn

f(x) dx = R1(h) +  h ;
2
x0
xn

f(x) dx = R1(2h) + 4  h4;
x0
where  is unknown coefficient for the global truncation error. The number of trapezoids must be even in
order the numerical approximation with double step-size (2h) could be computed. By cancelling the
truncation error of order O(h2), we define a new integration rule for the same integral:
xn

x0
f(x) dx =
4R1 (h)  R1 (2h)
= R2(h)
3
The new integration rule R2(h) for the same integral is more accurate since the truncation error is of order
O(h4). It is in fact the composite Simpson's rule as it can be checked directly. If the step size is sufficiently
small, the composite Simpson's rule gives a much better numerical approximation for the integral, compared
to the composite trapezoidal rule.
The process can be continued to find a higher-order integration formulas Rm(h) with the truncation error
O(h2m). The recursive formula for Romberg integration formulas:
Rm+1(h) =Rm(h) +
Rm (h)  Rm (2h)
4k  1

Numerical algorithm
There are two modifications of the Romberg integration algorithm: with doubling the step size h and with
halving the step size. The first modification is used when a limited number of data values is available. The
second modification is more preferable if the function y = f(x) is available analytically and the data values
(xk,yk) can be computed for any step size h. In order to compute the higher-order integration rules of the
integral of f(x) between x0  x  xn up to the order m, the number n must be matched with m as: n = 2m-1. In
this case, there are sufficient number of points to compute integration rules of lower order with larger step
sizes: h, 2h, 4h, 8h, …, (m-1)h. The numerical approximations for the integrals can be arranged in a table of
recursive integration formulas starting with the simplest approximation R1(h) (composite trapezoidal rule):
step size
h
2h
4h
8h
16h
R1
R1(h)
R1(2h)
R1(4h)
R1(8h)
R1(16h)
R2
R3
R4
R5
R2(h)
R2(2h)
R2(4h)
R2(8h)
R3(h)
R3(2h)
R3(4h)
R4(h)
R4(2h)
R5(h)
The diagonal entries are values of higher-order integration rules for the integral of f(x) between x0  x  xn .
The higher-order approximation Rk(h) has the truncation error O(h2k). If h is small, the truncation error
rapidly decreases with larger k. However, the rounding error is constant with larger value of k. As a result, no
further increase in accuracy of numerical integration can be obtained after some number m  M. The
algorithm can be terminated when the relative error falls below an error tolerance.
Example: The figure below presents the numerical approximations R1(h) and R2(h) for the integral of the
current I(t) with the step size h = 10. Blue circles are found by the composite Simpson rule R2(h), green
pluses are obtained by composite trapezoidal rule R1(h), and the exact integral of I(t) is shown by a red solid
curve. The composite Simpson rule R2(h) is clearly more accurate than the composite trapezoidal rule R1(h).