1. Introduction to MATLAB and programming 2. Workspace, variables and arrays 3. Using operators, expressions and statements 4. Repeating and decision-making 5. Different methods for input and output 6. Common functions 7. Logical vectors 8. Matrices and string arrays 9. Introduction to graphics 10. Loops 11. Custom functions and M-files

1. MATLAB
for Technical Computing
Naveed ur Rehman
http://www.naveedurrehman.com/

2. Outline
1. Introduction to MATLAB and programming
2. Workspace, variables and arrays
3. Using operators, expressions and
statements
4. Repeating and decision-making
5. Different methods for input and output
6. Common functions
7. Logical vectors
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3. Outline
8. Matrices and string arrays
9. Introduction to graphics
10. Loops
11. Custom functions and M-files
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4. Introduction
• MATLAB stands for Matrix Laboratory
• The system was designed to make matrix
computations particularly easy
• It is a powerful computing system for
handling scientific and engineering
calculations.
• MATLAB system is Interpreter.
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5. Introduction
An Array
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Here, A is of order (m, n) = (2, 3)
Element: A (1,2)

6. Introduction
Why MATLAB?
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• MATLAB can be used interactively
• Easy format: Many science and
engineering problems can be solved by
entering one or two commands
• Rich with 2D and 3D plotting capabilities
• Countless libraries and still developing

7. Introduction
What you should already know?
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• The mathematics associated with the
problem you want to solve in MATLAB.
• The logical plan or algorithm for solving a
particular problem.

8. Introduction
What to learn in MATLAB?
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• The exact rules for writing MATLAB
statements and using MATLAB utilities
• Converting algorithm into MATLAB
statements and/or program

9. Introduction
What you will learn with experience?
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• To design, develop and implement
computational and graphical tools to do
relatively complex problems
• To develop a toolbox of your own that
helps you solve problems of interest
• To adjust the look of MATLAB to make
interaction more user-friendly

10. Introduction
User-interface: MATLAB Desktop
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12. Introduction
Using command prompt:
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• Command line: The line with >> prompt
• Command-line editing:
• Use Backspace, Left-arrow, Right-arrow
• Up-arrow and Down-arrow for accessing history
• Smart recall: type some character and press Up-
arrow and Down-arrow
• Execution: Enter

13. Introduction
Training methodology
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1. Presentation + Code execution by Trainer
in Classroom or Lab
2. Code execution + practice problems by
Attendees in Lab

14. Arithmetic
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Addition: 3+2
Subtraction: 3-2
Multiplication: 3*2
Division: 3/2 or 23
Exponent: 3^2

15. Arithmetic
1/0
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Infinity:
inf + 3
Not-a-Number (NaN):
0/0

16. Variables
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Save value in a variable:
x = 3
x = 3;
x
Variables are case-sensitive:
T = 2
t = 3
Z = t+T;

17. Variable naming rules
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1. It may consist only of the letters A-Z,a-z,
the digits 0-9, and the underscore ( _ ).
2. It must start with a letter.

18. Using functions
a = sqrt(4)
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a = 4

19. Using functions
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• pi -> it’s a pre-defined constant
• abs(x) sqrt(x)
• sin(x) cos(x) tan(x)
asin(x) acos(x) atan(x)
sinh(x) cosh(x) tanh(x)
asinh(x) acosh(x) atanh(x)
• atan2(y,x) hypot(y,x)
• ceil(x), floor(x)
• log(x) log10(x) exp(x)

20. Using functions
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fix and rem
Converting 40 inches to feet-inch.
feet = fix(40/12)
inches = rem(40, 12)

21. Using commands
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• help function or command
• clc
• whos
• clear
• clear variable
• date
• calendar
help sin

22. Vectors
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X=[3,2]
X=[3;2]
Row vectors:
Column vector:
X= [1:10]
X= [0:2:10]

23. Vectors
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X=1:10
Y = 2 .* X
Operations with scalar values:
Addition: 3.+X
Subtraction: 3.-X
Multiplication: 3.*X
Division: 3./X or X.3
Exponent: X.^3

24. Vectors
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X=0:180
Y = sin(X.*pi/180)
Operations with functions:

25. Vectors
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X=10:15
Y=30:35
Z=X .* Y
Scalar operations with vectors:
Each element of X is multiplied by the
respective element of Y.

26. Matrices
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X = [1 2; 3 4]
A 2D matrix:
X =
1 2
3 4

27. Matrices
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X = [1 2; 3 4]
Y = 2 .* X
Scalar operations with matrix:
X = [1 2; 3 4]
Y = [5 6; 7 8]
Z = X.*Y
Scalar operations between matrices:

28. Matrices
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X = [1 2; 3 4]
Y = [5 6; 7 8]
Z = X*Y
Vector operations between matrices:

29. Matrices
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Y = [1 4 8; 0 -1 4]'
Transpose:

30. Matrices
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M = magic(3)
Magic square matrix:
the rows, columns, and main diagonal add
up to the same value in a magic matrix.

31. Matrices
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R = rand(2,3)
Random matrix:

32. Arithmetic with Matrices
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k = min(A)
Minimum:
k = max(A)
Maximum:
k = mean(A)
Mean:
k = length(A)
Length:

33. Arithmetic with Matrices
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k = sum(A)
Sum:
k = sum(A .* B)
Sum of products:
k = prod(A)
Product:

34. Accessing elements
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R = rand(5,7);
a = R(1,2)
b = R(1:5,2)
c = R(1:4,1:3)

35. Linear equations
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A = [1 2; 2 -1]
B = [4; 3]
R = AB
Solution with matrix method:

36. Linear equations
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[x,y]=solve('x+2*y=4','2*x-y=3')
Solution with built-in solve function:

37. Polynomials
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P = [1 7 0 -5 9]
A polynomial can be represented as a vector.
Example:
To solve a polynomial at some value of x:
polyval(P,4)

38. Polynomials
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P = [1 7 0 -5 9];
R = roots(P)
Roots of a polynomial:
R = [3 4 5 2];
P = poly(R)
Generating polynomial from roots:

39. Polynomials
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X = [1 2 3 4 5 6];
Y = [5.5 43.1 128 290.7 498.4 978.67];
P = polyfit(X,Y,4)
Fitting a polynomial using least-square
method:

40. Plotting
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X=0:360
Y=sin(X.*pi/180)
plot(X,Y)
Plot function:
0 50 100 150 200 250 300 350 400
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1

41. Plotting
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Options

42. Plotting
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X=0:10:360
Y=sin(X.*pi/180)
plot(X,Y,'r+')
X=0:10:360
Y=sin(X.*pi/180)
plot(X,Y,'r+-')

43. Plotting
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title('some graph title')
xlabel('some label on x-axis')
ylabel('some label on y-axis')
grid %use grid or grid off
axis equal

44. Plotting
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x = [0 : 0.01: 10];
y = sin(x);
g = cos(x);
plot(x, y,'r', x, g,'b')
legend(Sin(x)', 'Cos(x)')
Multiple functions on same graphs:

45. Plotting
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x = [0 : 0.01: 10];
y = sin(x);
g = cos(x);
plot(x, y,'r')
figure
plot(x, g,'b')
Multiple functions on multiple graphs:

46. Plotting
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x = [0 : 0.01: 10];
y = sin(x);
g = cos(x);
subplot(1,2,1)
plot(x, y,'r')
subplot(1,2,2)
plot(x, g,'b')
Multiple functions as subgraphs:

47. Graphics
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x = [1:10];
y = [75, 58, 90, 87, 50, 85, 92, 75, 60, 95];
bar(x,y)
xlabel('Student')
ylabel('Score')
title('First Sem:')
Bar graphs:

48. Graphics
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[x,y] = meshgrid(-5:0.1:5,-3:0.1:3);
g = x.^2 + y;
contour(x,y,g,20)
xlabel('x')
ylabel('y')
Contour graphs:

49. Graphics
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[x,y] = meshgrid(-2:.2:2, -2:.2:2);
z = x .* exp(-x.^2 - y.^2);
surf(x,y,z)
xlabel('x')
ylabel('y')
zlabel('z')
Surface graphs:

50. Equation plotting
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ezplot('2*x+y^2=10')
ezplot function:
-6 -4 -2 0 2 4 6
-6
-4
-2
0
2
4
6
x
y
2 x+y2
=10

51. Symbolic operations
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a = sym('a');
b = sym('b');
c = 2*a
d = 2*c+b*c

52. Symbolic diff. and int.
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Differentiation:
syms t
f = 3*t^2 + 2*t^(-2);
diff(f)
Integration and area under the curve:
syms x
f = 2*x^5
int(f)
area = double(int(f, 1.8, 2.3))

53. Symbolic expand and collect
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Expand:
syms x
syms y
expand((x-5)*(x+9))
expand(sin(2*x))
expand(cos(x+y))

54. Symbolic factor. and simplification
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Factorization:
syms x
syms y
factor(x^3 - y^3)
Simplification:
syms x
syms y
simplify((x^4-16)/(x^2-4))

55. Complex numbers
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Z = 2 + 3*i
Defining:
Functions:
R = real(Z)
G = imag(Z)
C = conj(Z)

56. Complex numbers
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S = sqrt(Z) % using De Moivre's formula
More functions:
E = exp(Z)
A = angle(Z) % in radians
In polar coordinates:
M = abs(Z) % magnitude

57. M-Files: Programming mode
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M-Files are also called Matlab program files.
• Go to File > New > M-File or just type edit
to start with a new m-file.
• Always save M-File file before execution.
Ctrl+S can be used as keyboard shortcut.
• To execute codes, use F5. If the Current
Directory is set, you may drag-drop the
file or type its file name (with out
extension).

58. Displaying in M-Files
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Use disp function.
R = rand(5,1)
disp(R)
disp(R’)
disp([R, R])
disp(['Good number is ', num2str(1+7)])

59. Resetting in M-Files
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Use clear and clc in the beginning of any
program. This will:
1. Delete all variables from workspace
2. Wipe the command window and set
cursor at top.

60. Comments in M-Files
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1. Use % symbol before program comments.
2. The Comment and Uncomment submenu
in Text menu in editor’s tab can also be
used.
3. Program comments are not read by
MATLAB interpreter.

61. Sample program: Speed
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s = 20;
t = 300;
v = s/t;
disp( 'Speed required is:' );
disp( v );

62. Sample program: SpeedS
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s = 10:10:1000;
t = 300;
v = s./t;
disp([s' v']);

63. Sample program: Balance
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balance = 1000;
rate = 0.09;
interest = rate * balance;
balance = balance + interest;
disp( 'New balance:' );
disp( balance );

64. Sample program: Vertical Motion
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g = 9.81; % acceleration due to gravity
u = 60; % initial velocity in metres/sec
t = 0 : 0.1 : 12.3; % time in seconds
s = u .* t + g / 2 .* t .^ 2; % vertical displacement in metres
plot(t, s)
title( 'Vertical motion under gravity' )
xlabel( 'time' )
ylabel( 'vertical displacement' )
grid
disp( [t' s'] ) % display a table

65. Taking input from user
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Use input function to take input from user.
A = input('How many apples: ');
Numeric input:
N = input('Enter your name: ','s');
String input:

66. Tic-toc
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Tic-toc can be used for knowing execution
time.
tic
A = input('How many apples: ');
toc

67. Saving and loading via files
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save and load commands are used to save
and load a variable via files:
A = rand(3,3);
save record.txt A -ascii
C = load('record.txt')
Files can be generated by external programs
or data loggers can be read using load.

68. Communication with MS Excel
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csvread and csvwrite commands are used to
read and save variable in MS Excel format file:
A = rand(3,3);
csvwrite('record.csv',A)
B = csvread('record.csv')

69. Predicate expression
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1. A predicate is one in which relational
and/or logical operator(s) are used.
2. They result in either true (1) or false (0).

70. Relational operators
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5 > 3
A = 5;
B = 10;
C = B >= A
== Equals to
< Less than
> Greater than
<= Less than or equals to
>= Greater than or equals to
~= Not equals to

71. Logical operators
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(5 > 3) & (10 < 4)
A = 5;
B = 10;
C = 30;
D = 100;
E = (B >= A) | (D<C)
~ Logical Not
| Logical OR
& Logical AND
AND
1 & 1 = 1
1 & 0 = 0
0 & 1 = 0
0 & 0 = 0
OR
1 | 1 = 1
1 | 0 = 1
0 | 1 = 1
0 | 0 = 0

72. Condition using if constructs
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if predicate
statements
end
if predicate
statements
else
statements
end
if predicate
statements
elseif predicate
statements
elseif predicate
statements
end
if predicate
statements
elseif predicate
statements
else
statements
end
1
2
3 4

73. Example: If-end
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r = rand
if r > 0.5
disp( 'greater indeed' )
end
1

74. Example: If-else-end
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r = rand
if r > 0.5
disp( 'greater indeed' )
else
disp( ‘smaller indeed' )
end
2

75. Example: If-elseif-end
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r = rand
if r == 0.5
disp( 'equals indeed' )
elseif (r > 0.5)
disp( 'greater indeed' )
end
3

76. Example: If-elseif-else-end
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r = rand
if r == 0.5
disp( 'equals indeed' )
elseif (r > 0.5)
disp( 'greater indeed' )
else
disp( ‘smaller indeed' )
end
4

77. Repetition using For loop
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for variable = expr
statements...
end
expr can be like: f:l or f:i:l
1. 1:10
2. 0:2:10
3. 10:-1:0

78. For loop: Factorial program
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n = 10;
fact = 1;
for k = 1:n
fact = k * fact;
disp( [k fact] )
end

79. Repetition using While loop
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while expr
statements
end
expr is a predicate expression.

80. While loop: Numbering program
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k = 1
while k < 10
disp(k);
k = k + 1;
end

81. Controlling in loops
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1. To terminate a loop at any iteration:
break
2. To pass control to the next iteration:
continue

82. Logical vectors
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The elements of a logical vector are either 1
or 0. In the following example, G and H are
logical vectors:
R = rand(1,10);
G = R>0.5;
H = (R>=0.5) | (R<=0.3);

83. Logical vectors
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Counting true and false:
R = rand(1,100);
T = sum(R>0.5);
F = length(R) - sum(R>0.5);

84. Logical vectors
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Functions:
Ra = [0 0 0 0 1];
Rb = [0 0 0 0 0];
any(Ra)
any(Rb)
any: is any true element exist?

85. Logical vectors
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Functions:
Ra = [0 0 1 0 1];
Rb = [1 1 1 1 1];
all(Ra)
all(Rb)
all: are all elements true?

86. Logical vectors
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Functions:
exist('a') %where a is a variable name
exist: is a variable exists in workspace?
R = [0 0 1 0 1];
find(R)
find: returns subscripts on true elements.

87. Logical vectors
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Example: calculate sixes in a dice game.
a = 1;
b = 6;
t = 500;
r = floor(a + (b-a+1).*rand(1,t)); % [a, b]
sixes = sum(r==6)

88. Sorting
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A vector or matrix can be sort in ascending or
descending order.
v = rand(1,10);
a = sort(v);
d = sort(v,'descend');

89. Primes and factors
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primes:
v = primes(100)
isprime:
v = isprime(97)
factors:
v = factor(96)

90. Evaluation of expression
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Lets say:
eqn = '(1/2)*x+x^2-3'
Evaluation:
x = 4;
y = eval(eqn)

91. Vectorization of expression
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Lets say:
eqn = '(1/2)*x+x^2-3'
Vectroization:
v = vectorize(eqn);

92. Evaluation of vectorized expression
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yeqn = '(1/2)*x+x^2-3';
x = 0:20;
y = eval(vectorize(yeqn));
disp([x' y'])
plot(x,y)

93. Ordinary differential equations
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dsolve('Dy = x*y','x')
First order ODE:
Solution:
r = dsolve('Dy = x*y','y(1)=1','x')
Numerical solution for initial condition:

94. Ordinary differential equations
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r = dsolve('Dy = x*y','y(1)=1','x');
x = 0:0.1:5;
y = eval(vectorize(r))
plot(x,y)
Solution set and plot when independent
variable x ranges from 0 to 20.
@

95. Ordinary differential equations
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eqn = 'D2y + 8*Dy + 2*y = cos(x)';
inits = 'y(0)=0, Dy(0)=1';
r = dsolve(eqn,inits,'x');
x = 0:0.1:20;
y = eval(vectorize(r));
disp([x' y']);
plot(x,y);
Second order ODE:

96. Ordinary differential equations
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[x,y,z]=dsolve('Dx=x+2*y-
z','Dy=x+z','Dz=4*x-4*y+5*z')
System of ODEs:

97. Ordinary differential equations
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inits='x(0)=1,y(0)=2,z(0)=3';
[x,y,z]=dsolve('Dx=x+2*y-
z','Dy=x+z','Dz=4*x-4*y+5*z',inits)
System of ODEs with Numerical solution for
initial conditions:

98. Ordinary differential equations
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inits='x(0)=1,y(0)=2,z(0)=3';
[x,y,z]=dsolve('Dx=x+2*y-
z','Dy=x+z','Dz=4*x-4*y+5*z',inits);
t=0:0.02:.5;
xx=eval(vectorize(x));
yy=eval(vectorize(y));
zz=eval(vectorize(z));
plot(t, xx, 'r',t, yy, 'g',t, zz,'b');
System of ODEs with solution set and plot:

99. Example: Palindrome!
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Ask user to input a number. Check if it is
palindrome or not.
Hint:
A palindrome number is a number such that if
we reverse it, it will not change. Use:
1. num2str: converts number to a string
2. fliplr: flips a string, left to right
3. str2num: converts string to a number

100. Example: Projectile motion
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Ask user to enter initial velocity and angle from
horizontal for a projectile motion.
Calculate:
1. Range
2. Flight time
3. Max. height
Also plot:
1. Projectile trajectory (x vs. y)
2. Projectile angle vs. speed

101. Example: Projectile motion
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Range:
Flight time:
Max. height:
Trajectory:
Instant. Velocity & angle:

102. Example: Projectile motion
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103. Example: Projectile motion
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104. Example: pi using Monte Carlo
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R
Area of circle / Area of square = pi*r^2 / (2r)^2
C / S = pi / 4
pi = 4 * C / S
Prove that the
value of pi is 3.142

105. Example: pi using Monte Carlo
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Procedure:
1. Throw T number of darts
2. Count the number of darts falling inside Circle (C)
3. Count the number of darts falling inside Square (S)
4. Calculate pi = 4 * C / S

106. Example: pi using Monte Carlo
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Hint:
1. Assume that radius (R) is 1 unit.
2. Generate T number of X and Y random numbers
between 0 and 1. Let the dart fall on square at
(X,Y).
3. Calculate position of dart by P2 = X2 + Y2
4. Increment in C if P <= 1
5. Darts inside square will be S = T

107. Example: Lookup
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Investment Profit
10 100
20 250
30 400
40 390
50 380
Find out the maximum profit and optimum
investment.

108. Anonymous (in-line) functions
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function_name = @(arglist)expression
Usage:
power = @(x, n) x.^n;
result1 = power(7, 3)
result2 = power(49, 0.5)
result3 = power(10, -10)
result4 = power (4.5, 1.5)

109. Anonymous (in-line) functions
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Write an anonymous function to calculate
average of three numbers.
myavg = @(a, b, c) (a+b+c)/3;
....
...
...
result = myavg (1,2, 3)

110. M-File functions
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function [out1,out2, ..., outN] =
myfun(in1,in2,in3, ..., inN)
Note:
1. Functions should be saved in separate
files.
2. Name of file should be as same as the
name of function.

111. M-File functions
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function [sum,avg] = mysumavg(n1, n2, n3)
%This function calculates the sum and
% average of the three given numbers
sum = n1+n2+n3;
avg = sum/3;
mysumavg.m:
[thesum,theavg] = mysumavg(1,2,3)
program.m:

112. M-File functions
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Write a function to calculate roots of a
quadratic equation.

113. M-File functions with subfunctions
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function [o1,o2…] = function(in1,in2…)
….
end
function [o1,o2…] = subfunction(in1,in2…)
….
end
function.m:

114. M-File functions with subfunctions
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Note:
1. File name should be as same as main
function’s name
2. Variables of main function are unknown
in subfunction
3. Variables of sub function are unknown in
main function

115. M-File functions with subfunctions
naveedurrehman.com
Write a function to calculate roots of a
quadratic equation. The discriminant should
be calculated in a sub function.

116. M-File functions with nested funcs
naveedurrehman.com
function [o1,o2…] = function(in1,in2…)
….
function [o1,o2…] = nestfunction(in1,in2…)
….
End
…
end
function.m:

117. M-File functions with nested funcs
naveedurrehman.com
Note:
1. File name should be as same as main
function’s name
2. Variables of main function and sub
functions are known.

118. M-File functions with nested funcs
naveedurrehman.com
Write a function calculate roots of a
quadratic equation. The discriminant should
be calculated in a nested function.

119. Global variables
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These variables are known in all program as
well as In functions.

120. Global variables
naveedurrehman.com
global TOTAL;
TOTAL = 10;
n = [34, 45, 25, 45, 33, 19, 40, 34, 38, 42];
av = average(n)
function avg = average(nums)
global TOTAL
avg = sum(nums)/TOTAL;
end

121. THANK YOU!
Naveed ur Rehman
http://www.naveedurrehman.com/