## Another improper integral solved using trapezoidal rule

In a previous post, I showed how Trapezoidal rule can be used to solve improper integrals.  The example used in the post was an improper integral with an infinite interval of integration.

In an example in this post, we use Trapezoidal rule to solve an improper integral where the integrand becomes infinite.  The integral is $\int_{0}^{b} 1/sqrt{x} dx$.  The integrand becomes infinite at x=0.  Since x=0 would be one of the points where the integrand will be sought by the multiple-segment Trapezoidal rule, we choose the value of the integrand at x=0 to be zero (any other value would do too – a better assumption would be f(h), where h is the segment width in the multiple-segment Trapezoidal rule).

Here is a MATLAB program that shows you the exact value of the integral and then compares it with the multiple-segment Trapezoidal rule.  The convergence is slow but you can integrate improper integrals using Trapezoidal rule.

The MATLAB program that can be downloaded at http://numericalmethods.eng.usf.edu/blog/trapezoidal_improper_sqrtx.m (better to download it as single quotes from the web-post do not translate correctly with the MATLAB editor).  The html file showing the mfile and the command window output is here: http://numericalmethods.eng.usf.edu/blog/html/trapezoidal_improper_sqrtx.html

% Simulation : Can I use Trapezoidal rule for an improper integral?

% Language : Matlab 2007a

% Authors : Autar Kaw, http://numericalmethods.eng.usf.edu

% Mfile available at
% http://numericalmethods.eng.usf.edu/blog/trapezoidal_improper_sqrt.m

% Last Revised : October 8, 2008

% Abstract: This program shows use of multiple segment Trapezoidal
% rule to integrate 1/sqrt(x) from x=0 to b, b>0.

clc
clear all

disp(‘This program shows the convergence of getting the value of ‘)
disp(‘an improper integral using multiple segment Trapezoidal rule’)
disp(‘Author: Autar K Kaw.  autarkaw.wordpress.com’)

%INPUTS.  If you want to experiment, these are the only variables
% you should and can change.
% b  = Upper limit of integration
% m = Maximum number of segments is 2^m
b=9;
m=14;

% SIMULATION
fprintf(‘\nFinding the integral of 1/sqrt(x) with limits of integration as x=0 to x=%g’,b)

% EXACT VALUE OF INTEGRAL
% integrand 1/sqrt(x)
syms x
f=1/sqrt(x);
a=0;
valexact=double(int(f,x,a,b));
fprintf(‘\n\nExact value of integral = %f’,valexact)
disp( ‘  ‘)

f=inline(‘1/sqrt(x)’);
%finding value of the integral using 16,…2^m segments
for k=4:1:m
n=2^k;
h=(b-a)/n;
sum=0;
for i=1:1:n-1
sum=sum+f(a+i*h);
end
% See below how f(a) is not added as f(a)=infinity.  Instead we
% use a value of f(a)=0.  How can we do that? Because as per integral calculus,
% using a different value of the function at one point or
% at finite number of points does not change the value of the
% integral.
sum=2*sum+0+f(b);
sum=(b-a)/(2*n)*sum;
fprintf(‘\nApproximate value of integral =%f with %g segments’,sum,n)
end
disp(‘  ‘)

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu.

An abridged (for low cost) book on Numerical Methods with Applications will be in print (includes problem sets, TOC, index) on December 10, 2008 and available at lulu storefront.

## The BMI (Body Mass Index) Program

In 1998, the federal government developed the body mass index (BMI) to determine ideal weights.  Body mass index is calculated as 703 times the weight in pounds divided by the square of the height in inches, the obtained number is then rounded to the nearest whole number (Hint: 23.5 will be rounded to 24; 23.1 will be rounded to 23; 23.52 will be rounded to 24)

BMI Categories:

BMI <19 (underweight)
19≤BMI≤25 (healthy)
25<BMI≤30 (overweight)
BMI is >30 (obese)

Here is a MATLAB program that calculates the BMI of a person, classifies him/her in the BMI category, and then suggests a healthy weight.

It is better to download the program as single quotes in the pasted version do not translate correctly when pasted into a mfile editor of MATLAB.  See the html version for clarity and sample output.

% So you want my phone number and my BMI?  How shallow can you get?

% Worksheet : Finding the BMI of  a person, classifying the BMI and
% suggesting a healthy weight
% Language : Matlab 2007a
% Authors : Autar Kaw
% Last Revised : September 27, 2008

%  Abstract: Finding the body mass index of a person, classifying their health
% and recommending a target weight.

%
%  In 1998, the federal government developed the body mass index (BMI)
%  to determine ideal weights.  Body mass index is calculated as
%  703 times the weight in pounds divided by the square of the height in inches,
%  the obtained number is then rounded to the nearest whole number
%  (23.5 will be rounded to 24; 23.1 will be rounded to 23; 23.52 will be rounded to 24).
%
% Write a MATLAB program to do the following:
%
% Assign a value to weight in lbs, and height in inches and
% then calculate BMI as a rounded integer.
% Output a variable called health_id as
%     0 if the person’s BMI <19 (underweight)
%     1 if the person’s BMI is 19≤BMI≤25 (healthy)
%     2 if the person’s BMI is 25<BMI≤30 (overweight)
%     3 if the person’s BMI is >30 (obese)
% Output also a variable hw for healthy weight in rounded integer lbs for all the conditions.
% Use fprintf command with explanation for the inputs and outputs
clc
clear all
% INPUTS
% Weight in lbs
weight=180;
% Height in inches
height=69;

%REST OF THE PROGRAM
bmi=weight/height^2*703.0;
bmi=round(bmi);

% Assigning the proper health_id and finding the suggested healthy weight
% health_id= BMI category
% hw = healthy weight
% You can use 4 separate if-end statements or switch case statement also.
if (bmi<19)
health_id=0;
hw=19*height^2/703;
elseif (bmi>=19 & bmi<=25);
health_id=1;
elseif (bmi>25 & bmi<=30);
health_id=2;
hw=25*height^2/703;
else
health_id=3;
hw=25*height^2/703;
end
hw=round(hw);

% Printing the outputs
disp(‘The BMI MATLAB program’)
disp(‘Author: Autar K Kaw’)
disp(‘http://numericalmethods.eng.usf.edu&#8217;)
disp(‘September 28, 2008’)
disp(‘ ‘)
fprintf(‘\nWeight of person =%6.0f lbs’,weight)
fprintf(‘\nHeight of person =%6.0f inches’,height)
if health_id==0
fprintf(‘\nYou are underweight.  \nYour target weight is =%6.0f lbs’,hw)
elseif health_id==1
fprintf(‘\nYou are a healthy weight.  \nYour target weight is =%6.0f lbs’,hw)
elseif health_id==2
fprintf(‘\nYou are overweight.  \nYour target weight is =%6.0f lbs’,hw)
elseif health_id==3
fprintf(‘\nYou are obese.  \nYour target weight is =%6.0f lbs’,hw)
end

________________________________________________________________________________________________

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu.

An abridged (for low cost) book on Numerical Methods with Applications will be in print (includes problem sets, TOC, index) on December 10, 2008 and available at lulu storefront.

## A legend used in the movie “The Happening”

Well M. Night Shyamalan may have made another disappointing movie – The Happening, but I somewhat liked it. I would give it a grade of B.

In the movie, John Leguzomo’s character, a math teacher, is distracting his fellow panicking passenger in the Jeep with a mathematical question. The question he asks her is if he gave her a penny on Day 1 of the month, two pennies on Day 2 of the month, four pennies on Day 3 of the month, and so on, how much would money would she have after a month. She shouts \$300 or some odd number like that. But, do you know that the amount is actually more than a 10 million dollars (Thanks to a student who mentioned that it was a penny that John offered on the first day, not a dollar – sometimes I do feel generous).

This question is based on a story from India and it goes as follows.

King Shriham of India wanted to reward his grand minister Ben for inventing the game of chess. When asked what reward he wanted, Ben asked for 1 grain of rice on the first square of the board, 2 on the second square of the board, 4 on the third square of the board, 8 on the fourth square of the board, and so on till all the 64 squares were covered. That is, he was doubling the number of grains on each successive square of the board. Although Ben’s request looked less than modest, King Shriham quickly found that the amount of rice that Ben was asking for was humongous.

QUESTIONS:

Write a MATLAB (you can use any other programming language) program for the following using the for or while loop.

1. Find out how many grains of rice Ben was asking for.
2. If the mass of a grain of rice is 2 mg, and the world production of rice in recent years has been approximately 600,000,000 tons (1 ton=1000 kg), how many times the modern world production was Ben’s request?
3. Do the inverse problem – find out how many squares are covered if the the number of grains on the chess board are given to you. For example, how many squares will be covered if the number of grains on the chess board are 16?

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu

## Finding the length of curve using MATLAB

As per integral calculus, the length of a continuous and differentiable curve f(x) from x=a to x=b is given by

S=$\int_a^b \sqrt{(1+(dy/dx)^2} dx$

Now how do we find the length of a curve in MATLAB.

Let us do this via an example. Assume one asked you to find the length of $x^2*sin(x)$ from Π to 2Π. In the book, How People Learn, the authors mention that learning a concept in multiple contexts prolongs retention. Although it may not be the context that the authors of the book are talking about, let us find the length of the curve multiple ways within MATLAB. Try the program for functions and limits of your own choice to evaluate the difference.

METHOD 1: Use the formula S=$\int_a^b \sqrt{(1+(dy/dx)^2} dx$ by using the diff and int function of MATLAB

METHOD 2: Generate several points between a and b, and join straight lines between consecutive data points. Add the length of these straight lines to find the length of the curve.

METHOD 3. Find the derivative dy/dx numerically using forward divided difference scheme, and then use trapezoidal rule (trapz command in MATLAB) for discrete data with unequal segments to find the length of the curve.

1. Why does METHOD 3 giving inaccurate results? Can you make them better by using better approximations of derivative like central divided difference scheme?
2. Redo the problem with f(x)=$x^{\frac{3}{2}}$ with a=1 and b=4 as the exact length can be found for such a function.

% Simulation : Find length of a given Curve
% Language : Matlab 2007a
% Authors : Autar Kaw
% Last Revised : June 14 2008
% Abstract: We are finding the length of the curve by three different ways
% 1. Using the formula from calculus
% 2. Breaking the curve into bunch of small straight lines
% 3. Finding dy/dx of the formula numerically to use discrete function
% integration
clc
clear all

disp(‘We are finding the length of the curve by three different ways’)
disp(‘1. Using the formula from calculus’)
disp(‘2. Breaking the curve into bunch of small straight lines’)
disp(‘3. Finding dy/dx of the formula numerically to use discrete function integration’)

%INPUTS – this is where you will change input data if you are doing
% a different problem
syms x;
% Define the function
curve=x^2*sin(x)
% lower limit
a=pi
% b=upper limit
b=2*pi
% n = number of straight lines used to approximate f(x) for METHOD 2
n=100
%p = number of discrete data points where dy/dx is calculated for METHOD 3
p=100

% OUTPUTS
% METHOD 1. Using the calculus formula
% S=int(sqrt(1+dy/dx^2),a,b)
% finding dy/dx
poly_dif=diff(curve,x,1);
% applying the formula
integrand=sqrt(1+poly_dif^2);
leng_exact=double(int(integrand,x,a,b));
fprintf (‘\nExact length =%g’,leng_exact)
%***********************************************************************

% METHOD 2. Breaking the curve as if it is made of small length
% straight lines
% Generating n x-points from a to b

xi= a:(b-a)/n:b;
% generating the y-values of the function
yi=subs(curve,x,xi);
% assuming that between consecutive data points, the
% curve can be approximated by linear splines.
leng_straight=0;
m=length(xi);
% there are m-1 splines for m points
for i=1:1:m-1
dx=xi(i+1)-xi(i);
dy= yi(i+1)-yi(i);
leneach=sqrt(dx^2+dy^2);
leng_straight=leng_straight+leneach;
end
fprintf (‘\n\nBreaking the line into short lengths =%g’,leng_straight)

% METHOD 3. Same as METHOD1, but calculating dy/dx
% numerically and integrating using trapz
xi=a:(b-a)/p:b;
% generating the dy/dx-values
m=length(xi);
for i=1:1:m-1
numer=yi(i+1)-yi(i);
den=xi(i+1)-xi(i);
dydxv(i)=numer/den;
end
% derivative at last point using Backward divided difference formula
% is same as Forward divided difference formula
dydxv(m)=dydxv(m-1);
integrandi=sqrt(1+dydxv.*dydxv);
length_fdd=trapz(xi,integrandi);
disp(‘ ‘)
disp(‘ ‘)
disp (‘Using numerical value of dy/dx coupled’)
disp (‘with discrete integration’)
fprintf (‘ =%g’,length_fdd)

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu

## A simple MATLAB program to show that High order interpolation is a bad idea

In a previous post, we talked about that higher order interpolation is a bad idea.

In this post I am showing you a MATLAB program that will allow you to experiment by changing the number of data points you choose, that is, the value of n (see the input highlighted in red in the code – this is the only line you want to change) and see for yourself why high order interpolation is a bad idea. Just, cut and paste the code below (or download it from http://www.eng.usf.edu/~kaw/download/runge.m) in the MATLAB editor and run it.

### % Simulation : Higher Order Interpolation is a Bad Idea

% Language : Matlab r12
% Authors : Autar Kaw
% Last Revised : June 10 2008
% Abstract: In 1901, Carl Runge published his work on dangers of high order
% interpolation. He took a simple looking function f(x)=1/(1+25x^2) on
% the interval [-1,1]. He took points equidistantly spaced in [-1,1]
% and interpolated the points with polynomials. He found that as he
% took more points, the polynomials and the original curve differed
% even more considerably. Try n=5 and n=25
clc
clear all
clf

disp(‘In 1901, Carl Runge published his work on dangers of high order’)
disp(‘interpolation. He took a simple looking function f(x)=1/(1+25x^2) on’)
disp(‘the interval [-1,1]. He took points equidistantly spaced in [-1,1]’)
disp(‘and interpolated the points with a polynomial. He found that as he’)
disp(‘took more points, the polynomials and the original curve differed’)
disp(‘even more considerably. Try n=5 and n=15’)

%
% INPUT:
% Enter the following
% n= number of equidisant x points from -1 to +1
n=15;

% SOLUTION
disp(‘ ‘)
disp(‘SOLUTION’)
disp(‘Check out the plots to appreciate: High order interpolation is a bad idea’)
fprintf(‘\nNumber of data points used =%g’,n)
% h = equidisant spacing between points
h=2.0/(n-1);
syms xx
% generating n data points equally spaced along the x-axis
% First data point
x(1)=-1;
y(1)=subs(1/(1+25*xx^2),xx,-1);
% Other data points
for i=2:1:n
x(i)=x(i-1)+h;
y(i)=subs(1/(1+25*xx^2),xx,x(i));
end

% Generating the (n-1)th order polynomial from the n data points
p=polyfit(x,y,n-1);

% Generating the points on the polynomial for plotting
xpoly=-1:0.01:1;
ypoly=polyval(p,xpoly);

% Generating the points on the function itself for plotting
xfun=-1:0.01:1;
yfun=subs(1/(1+25*xx^2),xx,xfun);

% The classic plot
% Plotting the points
plot(x,y,’o’,’MarkerSize’,10)
hold on
% Plotting the polynomial curve
plot(xpoly,ypoly,’LineWidth’,3,’Color’,’Blue’)
hold on
% Plotting the origianl function
plot(xfun,yfun,’LineWidth’,3,’Color’,’Red’)
hold off
xlabel(‘x’)
ylabel(‘y’)
title(‘Runges Phenomena Revisited’)
legend(‘Data points’,’Polynomial Interpolant’,’Original Function’)
%***********************************************************************
disp(‘ ‘)
disp(‘ ‘)
disp(‘What you will find is that the polynomials diverge for’)
disp(‘0.726<|x|<1. If you started to choose same number of points ‘)
disp(‘but more of them close to -1 and +1, you would avoid such divergence. ‘)
disp(‘ ‘)
disp(‘However, there is no general rule to pick points for a general ‘)
disp(‘function so that this divergence is avoided; but some rules do exist for ‘)
disp(‘certian types of functions.’)

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu