Transcript The Rectangle Method..

The Rectangle Method
Introduction
• Definite integral (High School material):
• A definite integral a∫b f(x) dx is the integral of a function
f(x) with fixed end point a and b:
• The integral of a function f(x) is equal to the area under the
graph of f(x).
• Graphically explained:
Introduction (cont.)
• Rectangle Method:
• The rectangle method (also called the midpoint rule)
is the simplest method in Mathematics used to
compute an approximation of a definite integral.
Rectangle Method: explained
• Rectangle Method:
1. Divide the interval [a .. b] into n pieces; each piece has
the same width:
Rectangle Method: explained (cont.)
The width of each piece of the smaller intervals is equal to:
b-a
width = ------n
Rectangle Method: explained (cont.)
2. The definite integral (= area under the graph is
approximated using a series of rectangles:
Rectangle Method: explained (cont.)
The area of a rectangle is equal to:
area of a rectangle = width ×
height
We (already) know the width of each rectangle:
b-a
width = ------n
Rectangle Method: explained (cont.)
• The height of the rectangles:
• The different rectangles has different heights
• Heights of each rectangle:
• The heights of each rectangle = the function
value at the start of the (small) interval
Rectangle Method: explained (cont.)
• Example: first interval
• First (small) interval: [a .. (a + width)] (remember
that: width = (b − a)/n)
• Height of first (small) interval:
Rectangle Method: explained (cont.)
• Therefore: height of first rectangle = f(a)
• Area of the rectangle = f(a) × width
Rectangle Method: explained (cont.)
• Example: second interval
• the second (small) interval is [(a+width) ..
(a+2width)] (remember that: width = (b − a)/n)
• Height of first (small) interval:
Rectangle Method: explained (cont.)
• Therefore: height of first rectangle = f(a+width)
• Area of the rectangle = f(a+width) × width
Rectangle Method: explained (cont.)
• Example: third interval
• the third (small) interval is [(a+2width) ..
(a+3width)] (remember that: width = (b − a)/n)
• Height of first (small) interval:
Rectangle Method: explained (cont.)
• Therefore: height of first rectangle = f(a+2width)
• Area of the rectangle = f(a+2width) × width
Rectangle Method: explained (cont.)
• We see a pattern emerging:
• Height of rectangle 1 = f(a + 0×width)
• Height of rectangle 2 = f(a + 1×width)
• Height of rectangle 3 = f(a + 2×width)
• ...
• Height of rectangle n−1 = f(a + (n−2)×width)
• Height of rectangle n = f(a + (n−1)×width)
Rectangle Method: explained (cont.)
• Note: there are n (smaller) interval in total.
• Conclusion:
Height of rectangle i = f( a + (i-1)*width )
= the function value at the point
"a + (i-1)*width"
b-a
Recall that: width = ----n
Rectangle Method: explained (cont.)
• The area of the rectangles:
• This figure helps you to visualize the computation:
Rectangle Method: explained (cont.)
• The width of rectangle i is equal to:
b-a
width = ------n
Rectangle Method: explained (cont.)
• The height of rectangle i is equal to:
height = f( a + (i-1)×width )
Rectangle Method: explained (cont.)
• The area of rectangle i is equal to:
area = width × height
= width × f( a + (i-1)×width )
Rectangle Method: explained (cont.)
• The approximation of the definite integral:
Approximation = sum of the area of the rectangles
= area of rectangle 1
+ area of rectangle 2
+ ...
+ area of rectangle n
= width × f( a + (1-1)×width )
+ width × f( a + (2-1)×width )
+ ...
+ width × f( a + (n-1)×width )
The general running sum algorithm
• We have seen a running sum computation algorithm
previously that adds in simple series of numbers:
• Compute the sum: 1 + 2 + 3 + .... + n
• Running sum algorithm:
sum = 0;
// Clear sum
for ( i = 1; i <= n ; i++ )
{
sum = sum + i; // Add i to sum
}
The general running sum algorithm (cont.)
• The running sum algorithm can be generalized to add a
more general series
• Example: compute 12 + 22 + 32 + ... + i2 + ... + n2
• The ith term in the sum = i2
• Therefore, the running sum algorithm to compute this
sum is:
sum = 0;
// Clear sum
for ( i = 1; i <= n ; i++ )
{
sum = sum + i*i; // Add i2 to sum
}
Algorithm to compute the sum of the area
of the rectangles
• We can use the running sum algorithm to compute the sum
of the area of the rectangles
Algorithm to compute the sum of the area
of the rectangles (cont.)
• Recall:
Approximation = sum of the area of the rectangles
= width × f( a + (1-1)×width )
+ width × f( a + (2-1)×width )
+ ...
+ width × f( a + (i-1)×width )
+ ...
+ width × f( a + (n-1)×width )
Where:
width = (b - a) / n
Algorithm to compute the sum of the area
of the rectangles (cont.)
• The ith term of the running sum is equal to:
ith term = width × f( a + (i-1)×width
)
Algorithm to compute the sum of the area
of the rectangles (cont.)
• Algorithm to compute the sum of the area of the
rectangles:
Variables:
double w;
double sum;
// w contains the width
// sum contains the running sum
Algorithm to compute the sum:
w = (b - a)/n; // Compute width
sum = 0.0;
// Clear running sum
for ( i = 1; i <= n; i++ )
{
sum = sum + w*f( a + (i-1)*w );
}
The Rectangle Method in Java
• Rough algorithm (pseudo code):
input a, b, n; // a = left end point of integral
// b = right end point of integral
// n = # rectangles used in the approximation
Rectangle Method:
w = (b - a)/n;
// Compute width
sum = sum of area of the n rectangles; // Compute area
print sum;
// Print result
The Rectangle Method in Java (cont.)
• Algorithm in Java:
public class RectangleMethod01
{
public static void main(String[] args)
{
double a, b, w, sum, x_i;
int i, n;
**** Initialize a, b, n ****
/* --------------------------------------------------The Rectangle Rule Algorithm
--------------------------------------------------- */
The Rectangle Method in Java (cont.)
w = (b-a)/n;
sum = 0.0;
// Compute width
// Clear running sum
for ( i = 1; i <= n; i++ )
{
x_i = a + (i-1)*w;
// Use x_i to simplify formula...
sum = sum + ( w * f(x_i) ); // width * height of rectangle i
}
System.out.println("Approximate integral value = " + sum);
}
}
The Rectangle Method in Java (cont.)
• Example 1: compute
0∫
1
x3 dx (the exact answer = 0.25)
public class RectangleMethod01
{
public static void main(String[] args)
{
double a, b, w, sum, x_i;
int i, n;
a = 0.0; b = 1.0; // 1∫2x3 dx
n = 1000;
// Use larger value for better approximation
/* --------------------------------------------------The Rectangle Rule Algorithm
--------------------------------------------------- */
The Rectangle Method in Java (cont.)
w = (b-a)/n;
sum = 0.0;
// Compute width
// Clear running sum
for ( i = 1; i <= n; i++ )
{
x_i = a + (i-1)*w;
sum = sum + ( w * (x_i * x_i * x_i) ); // f(x_i) = (x_i)3
}
System.out.println("Approximate integral value = " + sum);
}
}
The Rectangle Method in Java (cont.)
• Example Program: (Demo above code)
– Prog file:
http://mathcs.emory.edu/~cheung/Courses/170/Syllabus/07/Progs/
RectangleMethod01.java
• How to run the program:
• Right click on link and save in a scratch directory
• To compile: javac RectangleMethod01.java
• To run:
java RectangleMethod01
• Output: Approximate integral value =
0.2495002499999998
Exact answer: 0.25
The Rectangle Method in Java (cont.)
• Example: compute
1∫
2
(1/x) dx (the answer = ln(2))
public class RectangleMethod02
{
public static void main(String[] args)
{
double a, b, w, sum, x_i;
int i, n;
a = 1.0; b = 2.0; // 1∫2(1/x) dx
n = 1000;
// Use larger value for better approximation
/* --------------------------------------------------The Rectangle Rule Algorithm
--------------------------------------------------- */
The Rectangle Method in Java (cont.)
w = (b-a)/n;
sum = 0.0;
// Compute width
// Clear running sum
for ( i = 1; i <= n; i++ )
{
x_i = a + (i-1)*w;
sum = sum + ( w * (1/x_i) ); // f(x_i) = 1/x_i
}
System.out.println("Approximate integral value = " + sum);
}
}
The Rectangle Method in Java (cont.)
• Example Program: (Demo above code)
– Prog file:
http://mathcs.emory.edu/~cheung/Courses/170/Syllabus/07/Progs/
RectangleMethod02.java
• How to run the program:
• Right click on link and save in a scratch directory
• To compile: javac RectangleMethod02.java
• To run:
java RectangleMethod02
• Output: Approximate integral value =
0.6933972430599376
Exact answer: ln(2) = 0.69314718
The effect of the number of rectangles used
in the approximation
• Difference in the approximations when using different
number of rectangles:
The effect of the number of rectangles used
in the approximation (cont.)
• Clearly:
• Using more rectangles will give us a more accurate
approximation of the area under the graph
The effect of the number of rectangles used
in the approximation (cont.)
• However:
• Using more rectangles will make the algorithm add
more numbers
I.e., the algorithm must do more work --- it must adds
more smaller values (because the rectangles are smaller
and have smaller areas))
The effect of the number of rectangles used
in the approximation (cont.)
• Trade off:
• Often, in computer algorithms, a more accurate result
can be obtained by a longer running execution of the
same algorithm
• We call this phenomenon: trade off
• You cannot gain something without give up
on something else
The effect of the number of rectangles used
in the approximation (cont.)
You can experience the trade off phenomenon by using n =
1000000 in the above algorithm.
It will run slower, but give you very accurate results !
Outputs for n = 1000000:
1. Approximate integral value of
dx = 0.24999950000025453
0∫
1
x3
2. Approximate integral value of
dx = 0.6931474305600139
1∫
2
(1/x)