Discrete Mathematics  Quick Guide
Discrete Mathematics  Introduction
Mathematics can be broadly classified into two categories −
Continuous Mathematics − It is based upon continuous number line or the real numbers. It is characterized by the fact that between any two numbers, there are almost always an infinite set of numbers. For example, a function in continuous mathematics can be plotted in a smooth curve without breaks.
Discrete Mathematics − It involves distinct values; i.e. between any two points, there are a countable number of points. For example, if we have a finite set of objects, the function can be defined as a list of ordered pairs having these objects, and can be presented as a complete list of those pairs.
Topics in Discrete Mathematics
Though there cannot be a definite number of branches of Discrete Mathematics, the following topics are almost always covered in any study regarding this matter −
 Sets, Relations and Functions
 Mathematical Logic
 Group theory
 Counting Theory
 Probability
 Mathematical Induction and Recurrence Relations
 Graph Theory
 Trees
 Boolean Algebra
We will discuss each of these concepts in the subsequent chapters of this tutorial.
Discrete Mathematics  Sets
German mathematician G. Cantor introduced the concept of sets. He had defined a set as a collection of definite and distinguishable objects selected by the means of certain rules or description.
Set theory forms the basis of several other fields of study like counting theory, relations, graph theory and finite state machines. In this chapter, we will cover the different aspects of Set Theory.
Set  Definition
A set is an unordered collection of different elements. A set can be written explicitly by listing its elements using set bracket. If the order of the elements is changed or any element of a set is repeated, it does not make any changes in the set.
Some Example of Sets
 A set of all positive integers
 A set of all the planets in the solar system
 A set of all the states in India
 A set of all the lowercase letters of the alphabet
Representation of a Set
Sets can be represented in two ways −
 Roster or Tabular Form
 Set Builder Notation
Roster or Tabular Form
The set is represented by listing all the elements comprising it. The elements are enclosed within braces and separated by commas.
Example 1 − Set of vowels in English alphabet, $A = \lbrace a,e,i,o,u \rbrace$
Example 2 − Set of odd numbers less than 10, $B = \lbrace 1,3,5,7,9 \rbrace$
Set Builder Notation
The set is defined by specifying a property that elements of the set have in common. The set is described as $A = \lbrace x : p(x) \rbrace$
Example 1 − The set $\lbrace a,e,i,o,u \rbrace$ is written as −
$A = \lbrace x : \text{x is a vowel in English alphabet} \rbrace$
Example 2 − The set $\lbrace 1,3,5,7,9 \rbrace$ is written as −
$B = \lbrace x : 1 \le x \lt 10 \ and\ (x \% 2) \ne 0 \rbrace$
If an element x is a member of any set S, it is denoted by $x \in S$ and if an element y is not a member of set S, it is denoted by $y \notin S$.
Example − If $S = \lbrace1, 1.2, 1.7, 2\rbrace , 1 \in S$ but $1.5 \notin S$
Some Important Sets
N − the set of all natural numbers = $\lbrace1, 2, 3, 4, .....\rbrace$
Z − the set of all integers = $\lbrace....., 3, 2, 1, 0, 1, 2, 3, .....\rbrace$
Z^{+} − the set of all positive integers
Q − the set of all rational numbers
R − the set of all real numbers
W − the set of all whole numbers
Cardinality of a Set
Cardinality of a set S, denoted by $S$, is the number of elements of the set. The number is also referred as the cardinal number. If a set has an infinite number of elements, its cardinality is $\infty$.
Example − $\lbrace 1, 4, 3, 5 \rbrace  = 4,  \lbrace 1, 2, 3, 4, 5, \dots \rbrace  = \infty$
If there are two sets X and Y,
$X = Y$ denotes two sets X and Y having same cardinality. It occurs when the number of elements in X is exactly equal to the number of elements in Y. In this case, there exists a bijective function ‘f’ from X to Y.
$X \le Y$ denotes that set X’s cardinality is less than or equal to set Y’s cardinality. It occurs when number of elements in X is less than or equal to that of Y. Here, there exists an injective function ‘f’ from X to Y.
$X \lt Y$ denotes that set X’s cardinality is less than set Y’s cardinality. It occurs when number of elements in X is less than that of Y. Here, the function ‘f’ from X to Y is injective function but not bijective.
$If\ X \le Y$ and $X \ge Y$ then $X = Y$. The sets X and Y are commonly referred as equivalent sets.
Types of Sets
Sets can be classified into many types. Some of which are finite, infinite, subset, universal, proper, singleton set, etc.
Finite Set
A set which contains a definite number of elements is called a finite set.
Example − $S = \lbrace x \: \:x \in N$ and $70 \gt x \gt 50 \rbrace$
Infinite Set
A set which contains infinite number of elements is called an infinite set.
Example − $S = \lbrace x \:  \: x \in N $ and $ x \gt 10 \rbrace$
Subset
A set X is a subset of set Y (Written as $X \subseteq Y$) if every element of X is an element of set Y.
Example 1 − Let, $X = \lbrace 1, 2, 3, 4, 5, 6 \rbrace$ and $Y = \lbrace 1, 2 \rbrace$. Here set Y is a subset of set X as all the elements of set Y is in set X. Hence, we can write $Y \subseteq X$.
Example 2 − Let, $X = \lbrace 1, 2, 3 \rbrace$ and $Y = \lbrace 1, 2, 3 \rbrace$. Here set Y is a subset (Not a proper subset) of set X as all the elements of set Y is in set X. Hence, we can write $Y \subseteq X$.
Proper Subset
The term “proper subset” can be defined as “subset of but not equal to”. A Set X is a proper subset of set Y (Written as $ X \subset Y $) if every element of X is an element of set Y and $X \lt Y$.
Example − Let, $X = \lbrace 1, 2, 3, 4, 5, 6 \rbrace$ and $Y = \lbrace 1, 2 \rbrace$. Here set $Y \subset X$ since all elements in $Y$ are contained in $X$ too and $X$ has at least one element is more than set $Y$.
Universal Set
It is a collection of all elements in a particular context or application. All the sets in that context or application are essentially subsets of this universal set. Universal sets are represented as $U$.
Example − We may define $U$ as the set of all animals on earth. In this case, set of all mammals is a subset of $U$, set of all fishes is a subset of $U$, set of all insects is a subset of $U$, and so on.
Empty Set or Null Set
An empty set contains no elements. It is denoted by $\emptyset$. As the number of elements in an empty set is finite, empty set is a finite set. The cardinality of empty set or null set is zero.
Example − $S = \lbrace x \: \: x \in N$ and $7 \lt x \lt 8 \rbrace = \emptyset$
Singleton Set or Unit Set
Singleton set or unit set contains only one element. A singleton set is denoted by $\lbrace s \rbrace$.
Example − $S = \lbrace x \: \:x \in N,\ 7 \lt x \lt 9 \rbrace$ = $\lbrace 8 \rbrace$
Equal Set
If two sets contain the same elements they are said to be equal.
Example − If $A = \lbrace 1, 2, 6 \rbrace$ and $B = \lbrace 6, 1, 2 \rbrace$, they are equal as every element of set A is an element of set B and every element of set B is an element of set A.
Equivalent Set
If the cardinalities of two sets are same, they are called equivalent sets.
Example − If $A = \lbrace 1, 2, 6 \rbrace$ and $B = \lbrace 16, 17, 22 \rbrace$, they are equivalent as cardinality of A is equal to the cardinality of B. i.e. $A = B = 3$
Overlapping Set
Two sets that have at least one common element are called overlapping sets.
In case of overlapping sets −
$n(A \cup B) = n(A) + n(B)  n(A \cap B)$
$n(A \cup B) = n(A  B) + n(B  A) + n(A \cap B)$
$n(A) = n(A  B) + n(A \cap B)$
$n(B) = n(B  A) + n(A \cap B)$
Example − Let, $A = \lbrace 1, 2, 6 \rbrace$ and $B = \lbrace 6, 12, 42 \rbrace$. There is a common element ‘6’, hence these sets are overlapping sets.
Disjoint Set
Two sets A and B are called disjoint sets if they do not have even one element in common. Therefore, disjoint sets have the following properties −
$n(A \cap B) = \emptyset$
$n(A \cup B) = n(A) + n(B)$
Example − Let, $A = \lbrace 1, 2, 6 \rbrace$ and $B = \lbrace 7, 9, 14 \rbrace$, there is not a single common element, hence these sets are overlapping sets.
Venn Diagrams
Venn diagram, invented in 1880 by John Venn, is a schematic diagram that shows all possible logical relations between different mathematical sets.
Examples
Set Operations
Set Operations include Set Union, Set Intersection, Set Difference, Complement of Set, and Cartesian Product.
Set Union
The union of sets A and B (denoted by $A \cup B$) is the set of elements which are in A, in B, or in both A and B. Hence, $A \cup B = \lbrace x \: \: x \in A\ OR\ x \in B \rbrace$.
Example − If $A = \lbrace 10, 11, 12, 13 \rbrace$ and B = $\lbrace 13, 14, 15 \rbrace$, then $A \cup B = \lbrace 10, 11, 12, 13, 14, 15 \rbrace$. (The common element occurs only once)
Set Intersection
The intersection of sets A and B (denoted by $A \cap B$) is the set of elements which are in both A and B. Hence, $A \cap B = \lbrace x \:\: x \in A\ AND\ x \in B \rbrace$.
Example − If $A = \lbrace 11, 12, 13 \rbrace$ and $B = \lbrace 13, 14, 15 \rbrace$, then $A \cap B = \lbrace 13 \rbrace$.
Set Difference/ Relative Complement
The set difference of sets A and B (denoted by $A – B$) is the set of elements which are only in A but not in B. Hence, $A  B = \lbrace x \: \: x \in A\ AND\ x \notin B \rbrace$.
Example − If $A = \lbrace 10, 11, 12, 13 \rbrace$ and $B = \lbrace 13, 14, 15 \rbrace$, then $(A  B) = \lbrace 10, 11, 12 \rbrace$ and $(B  A) = \lbrace 14, 15 \rbrace$. Here, we can see $(A  B) \ne (B  A)$
Complement of a Set
The complement of a set A (denoted by $A’$) is the set of elements which are not in set A. Hence, $A' = \lbrace x  x \notin A \rbrace$.
More specifically, $A'= (U  A)$ where $U$ is a universal set which contains all objects.
Example − If $A = \lbrace x \: \: x\ \: {belongs\: to\: set\: of\: odd \:integers} \rbrace$ then $A' = \lbrace y\:  \: y\ \: {does\: not\: belong\: to\: set\: of\: odd\: integers } \rbrace$
Cartesian Product / Cross Product
The Cartesian product of n number of sets $A_1, A_2, \dots A_n$ denoted as $A_1 \times A_2 \dots \times A_n$ can be defined as all possible ordered pairs $(x_1, x_2, \dots x_n)$ where $x_1 \in A_1, x_2 \in A_2, \dots x_n \in A_n$
Example − If we take two sets $A = \lbrace a, b \rbrace$ and $B = \lbrace 1, 2 \rbrace$,
The Cartesian product of A and B is written as − $A \times B = \lbrace (a, 1), (a, 2), (b, 1), (b, 2)\rbrace$
The Cartesian product of B and A is written as − $B \times A = \lbrace (1, a), (1, b), (2, a), (2, b)\rbrace$
Power Set
Power set of a set S is the set of all subsets of S including the empty set. The cardinality of a power set of a set S of cardinality n is $2^n$. Power set is denoted as $P(S)$.
Example −
For a set $S = \lbrace a, b, c, d \rbrace$ let us calculate the subsets −
Subsets with 0 elements − $\lbrace \emptyset \rbrace$ (the empty set)
Subsets with 1 element − $\lbrace a \rbrace, \lbrace b \rbrace, \lbrace c \rbrace, \lbrace d \rbrace$
Subsets with 2 elements − $\lbrace a, b \rbrace, \lbrace a,c \rbrace, \lbrace a, d \rbrace, \lbrace b, c \rbrace, \lbrace b,d \rbrace,\lbrace c,d \rbrace$
Subsets with 3 elements − $\lbrace a ,b, c\rbrace,\lbrace a, b, d \rbrace, \lbrace a,c,d \rbrace,\lbrace b,c,d \rbrace$
Subsets with 4 elements − $\lbrace a, b, c, d \rbrace$
Hence, $P(S)=$
$\lbrace \quad \lbrace \emptyset \rbrace, \lbrace a \rbrace, \lbrace b \rbrace, \lbrace c \rbrace, \lbrace d \rbrace, \lbrace a,b \rbrace, \lbrace a,c \rbrace, \lbrace a,d \rbrace, \lbrace b,c \rbrace, \lbrace b,d \rbrace, \lbrace c,d \rbrace, \lbrace a,b,c \rbrace, \lbrace a,b,d \rbrace, \lbrace a,c,d \rbrace, \lbrace b,c,d \rbrace, \lbrace a,b,c,d \rbrace \quad \rbrace$
$ P(S)  = 2^4 = 16$
Note − The power set of an empty set is also an empty set.
$ P (\lbrace \emptyset \rbrace)  = 2^0 = 1$
Partitioning of a Set
Partition of a set, say S, is a collection of n disjoint subsets, say $P_1, P_2, \dots P_n$ that satisfies the following three conditions −
$P_i$ does not contain the empty set.
$\lbrack P_i \ne \lbrace \emptyset \rbrace\ for\ all\ 0 \lt i \le n \rbrack$
The union of the subsets must equal the entire original set.
$\lbrack P_1 \cup P_2 \cup \dots \cup P_n = S \rbrack$
The intersection of any two distinct sets is empty.
$\lbrack P_a \cap P_b = \lbrace \emptyset \rbrace,\ for\ a \ne b\ where\ n \ge a,\: b \ge 0 \rbrack$
Example
Let $S = \lbrace a, b, c, d, e, f, g, h \rbrace$
One probable partitioning is $\lbrace a \rbrace, \lbrace b, c, d \rbrace, \lbrace e, f, g, h \rbrace$
Another probable partitioning is $\lbrace a, b \rbrace, \lbrace c, d \rbrace, \lbrace e, f, g, h \rbrace$
Bell Numbers
Bell numbers give the count of the number of ways to partition a set. They are denoted by $B_n$ where n is the cardinality of the set.
Example −
Let $S = \lbrace 1, 2, 3\rbrace$, $n = S = 3$
The alternate partitions are −
1. $\emptyset , \lbrace 1, 2, 3 \rbrace$
2. $\lbrace 1 \rbrace , \lbrace 2, 3 \rbrace$
3. $\lbrace 1, 2 \rbrace , \lbrace 3 \rbrace$
4. $\lbrace 1, 3 \rbrace , \lbrace 2 \rbrace$
5. $\lbrace 1 \rbrace , \lbrace 2 \rbrace , \lbrace 3 \rbrace$
Hence $B_3 = 5$
Discrete Mathematics  Relations
Whenever sets are being discussed, the relationship between the elements of the sets is the next thing that comes up. Relations may exist between objects of the same set or between objects of two or more sets.
Definition and Properties
A binary relation R from set x to y (written as $xRy$ or $R(x,y)$) is a subset of the Cartesian product $x \times y$. If the ordered pair of G is reversed, the relation also changes.
Generally an nary relation R between sets $A_1, \dots ,\ and\ A_n$ is a subset of the nary product $A_1 \times \dots \times A_n$. The minimum cardinality of a relation R is Zero and maximum is $n^2$ in this case.
A binary relation R on a single set A is a subset of $A \times A$.
For two distinct sets, A and B, having cardinalities m and n respectively, the maximum cardinality of a relation R from A to B is mn.
Domain and Range
If there are two sets A and B, and relation R have order pair (x, y), then −
The domain of R, Dom(R), is the set $\lbrace x \: \: (x, y) \in R \:for\: some\: y\: in\: B \rbrace$
The range of R, Ran(R), is the set $\lbrace y\: \: (x, y) \in R \:for\: some\: x\: in\: A\rbrace$
Examples
Let, $A = \lbrace 1, 2, 9 \rbrace $ and $ B = \lbrace 1, 3, 7 \rbrace$
Case 1 − If relation R is 'equal to' then $R = \lbrace (1, 1), (3, 3) \rbrace$
Dom(R) = $\lbrace 1, 3 \rbrace , Ran(R) = \lbrace 1, 3 \rbrace$
Case 2 − If relation R is 'less than' then $R = \lbrace (1, 3), (1, 7), (2, 3), (2, 7) \rbrace$
Dom(R) = $\lbrace 1, 2 \rbrace , Ran(R) = \lbrace 3, 7 \rbrace$
Case 3 − If relation R is 'greater than' then $R = \lbrace (2, 1), (9, 1), (9, 3), (9, 7) \rbrace$
Dom(R) = $\lbrace 2, 9 \rbrace , Ran(R) = \lbrace 1, 3, 7 \rbrace$
Representation of Relations using Graph
A relation can be represented using a directed graph.
The number of vertices in the graph is equal to the number of elements in the set from which the relation has been defined. For each ordered pair (x, y) in the relation R, there will be a directed edge from the vertex ‘x’ to vertex ‘y’. If there is an ordered pair (x, x), there will be self loop on vertex ‘x’.
Suppose, there is a relation $R = \lbrace (1, 1), (1,2), (3, 2) \rbrace$ on set $S = \lbrace 1, 2, 3 \rbrace$, it can be represented by the following graph −
Types of Relations
The Empty Relation between sets X and Y, or on E, is the empty set $\emptyset$
The Full Relation between sets X and Y is the set $X \times Y$
The Identity Relation on set X is the set $\lbrace (x, x)  x \in X \rbrace$
The Inverse Relation R' of a relation R is defined as − $R' = \lbrace (b, a)  (a, b) \in R \rbrace$
Example − If $R = \lbrace (1, 2), (2, 3) \rbrace$ then $R' $ will be $\lbrace (2, 1), (3, 2) \rbrace$
A relation R on set A is called Reflexive if $\forall a \in A$ is related to a (aRa holds)
Example − The relation $R = \lbrace (a, a), (b, b) \rbrace$ on set $X = \lbrace a, b \rbrace$ is reflexive.
A relation R on set A is called Irreflexive if no $a \in A$ is related to a (aRa does not hold).
Example − The relation $R = \lbrace (a, b), (b, a) \rbrace$ on set $X = \lbrace a, b \rbrace$ is irreflexive.
A relation R on set A is called Symmetric if $xRy$ implies $yRx$, $\forall x \in A$ and $\forall y \in A$.
Example − The relation $R = \lbrace (1, 2), (2, 1), (3, 2), (2, 3) \rbrace$ on set $A = \lbrace 1, 2, 3 \rbrace$ is symmetric.
A relation R on set A is called AntiSymmetric if $xRy$ and $yRx$ implies $x = y \: \forall x \in A$ and $\forall y \in A$.
Example − The relation $R = \lbrace (x, y)\to N \:x \leq y \rbrace$ is antisymmetric since $x \leq y$ and $y \leq x$ implies $x = y$.
A relation R on set A is called Transitive if $xRy$ and $yRz$ implies $xRz, \forall x,y,z \in A$.
Example − The relation $R = \lbrace (1, 2), (2, 3), (1, 3) \rbrace$ on set $A = \lbrace 1, 2, 3 \rbrace$ is transitive.
A relation is an Equivalence Relation if it is reflexive, symmetric, and transitive.
Example − The relation $R = \lbrace (1, 1), (2, 2), (3, 3), (1, 2), (2,1), (2,3), (3,2), (1,3), (3,1) \rbrace$ on set $A = \lbrace 1, 2, 3 \rbrace$ is an equivalence relation since it is reflexive, symmetric, and transitive.
Discrete Mathematics  Functions
A Function assigns to each element of a set, exactly one element of a related set. Functions find their application in various fields like representation of the computational complexity of algorithms, counting objects, study of sequences and strings, to name a few. The third and final chapter of this part highlights the important aspects of functions.
Function  Definition
A function or mapping (Defined as $f: X \rightarrow Y$) is a relationship from elements of one set X to elements of another set Y (X and Y are nonempty sets). X is called Domain and Y is called Codomain of function ‘f’.
Function ‘f’ is a relation on X and Y such that for each $x \in X$, there exists a unique $y \in Y$ such that $(x,y) \in R$. ‘x’ is called preimage and ‘y’ is called image of function f.
A function can be one to one or many to one but not one to many.
Injective / Onetoone function
A function $f: A \rightarrow B$ is injective or onetoone function if for every $b \in B$, there exists at most one $a \in A$ such that $f(s) = t$.
This means a function f is injective if $a_1 \ne a_2$ implies $f(a1) \ne f(a2)$.
Example
$f: N \rightarrow N, f(x) = 5x$ is injective.
$f: N \rightarrow N, f(x) = x^2$ is injective.
$f: R\rightarrow R, f(x) = x^2$ is not injective as $(x)^2 = x^2$
Surjective / Onto function
A function $f: A \rightarrow B$ is surjective (onto) if the image of f equals its range. Equivalently, for every $b \in B$, there exists some $a \in A$ such that $f(a) = b$. This means that for any y in B, there exists some x in A such that $y = f(x)$.
Example
$f : N \rightarrow N, f(x) = x + 2$ is surjective.
$f : R \rightarrow R, f(x) = x^2$ is not surjective since we cannot find a real number whose square is negative.
Bijective / Onetoone Correspondent
A function $f: A \rightarrow B$ is bijective or onetoone correspondent if and only if f is both injective and surjective.
Problem
Prove that a function $f: R \rightarrow R$ defined by $f(x) = 2x – 3$ is a bijective function.
Explanation − We have to prove this function is both injective and surjective.
If $f(x_1) = f(x_2)$, then $2x_1 – 3 = 2x_2 – 3 $ and it implies that $x_1 = x_2$.
Hence, f is injective.
Here, $2x – 3= y$
So, $x = (y+5)/3$ which belongs to R and $f(x) = y$.
Hence, f is surjective.
Since f is both surjective and injective, we can say f is bijective.
Inverse of a Function
The inverse of a onetoone corresponding function $f : A \rightarrow B$, is the function $g : B \rightarrow A$, holding the following property −
$f(x) = y \Leftrightarrow g(y) = x$
The function f is called invertible, if its inverse function g exists.
Example
A Function $f : Z \rightarrow Z, f(x)=x+5$, is invertible since it has the inverse function $ g : Z \rightarrow Z, g(x)= x5$.
A Function $f : Z \rightarrow Z, f(x)=x^2$ is not invertiable since this is not onetoone as $(x)^2=x^2$.
Composition of Functions
Two functions $f: A \rightarrow B$ and $g: B \rightarrow C$ can be composed to give a composition $g o f$. This is a function from A to C defined by $(gof)(x) = g(f(x))$
Example
Let $f(x) = x + 2$ and $g(x) = 2x + 1$, find $( f o g)(x)$ and $( g o f)(x)$.
Solution
$(f o g)(x) = f (g(x)) = f(2x + 1) = 2x + 1 + 2 = 2x + 3$
$(g o f)(x) = g (f(x)) = g(x + 2) = 2 (x+2) + 1 = 2x + 5$
Hence, $(f o g)(x) \neq (g o f)(x)$
Some Facts about Composition
If f and g are onetoone then the function $(g o f)$ is also onetoone.
If f and g are onto then the function $(g o f)$ is also onto.
Composition always holds associative property but does not hold commutative property.
Discrete Mathematics  Propositional Logic
The rules of mathematical logic specify methods of reasoning mathematical statements. Greek philosopher, Aristotle, was the pioneer of logical reasoning. Logical reasoning provides the theoretical base for many areas of mathematics and consequently computer science. It has many practical applications in computer science like design of computing machines, artificial intelligence, definition of data structures for programming languages etc.
Propositional Logic is concerned with statements to which the truth values, “true” and “false”, can be assigned. The purpose is to analyze these statements either individually or in a composite manner.
Prepositional Logic – Definition
A proposition is a collection of declarative statements that has either a truth value "true” or a truth value "false". A propositional consists of propositional variables and connectives. We denote the propositional variables by capital letters (A, B, etc). The connectives connect the propositional variables.
Some examples of Propositions are given below −
 "Man is Mortal", it returns truth value “TRUE”
 "12 + 9 = 3 – 2", it returns truth value “FALSE”
The following is not a Proposition −
"A is less than 2". It is because unless we give a specific value of A, we cannot say whether the statement is true or false.
Connectives
In propositional logic generally we use five connectives which are −
OR ($\lor$)
AND ($\land$)
Negation/ NOT ($\lnot$)
Implication / ifthen ($\rightarrow$)
If and only if ($\Leftrightarrow$).
OR ($\lor$) − The OR operation of two propositions A and B (written as $A \lor B$) is true if at least any of the propositional variable A or B is true.
The truth table is as follows −
A  B  A ∨ B 

True  True  True 
True  False  True 
False  True  True 
False  False  False 
AND ($\land$) − The AND operation of two propositions A and B (written as $A \land B$) is true if both the propositional variable A and B is true.
The truth table is as follows −
A  B  A ∧ B 

True  True  True 
True  False  False 
False  True  False 
False  False  False 
Negation ($\lnot$) − The negation of a proposition A (written as $\lnot A$) is false when A is true and is true when A is false.
The truth table is as follows −
A  ¬ A 

True  False 
False  True 
Implication / ifthen ($\rightarrow$) − An implication $A \rightarrow B$ is the proposition “if A, then B”. It is false if A is true and B is false. The rest cases are true.
The truth table is as follows −
A  B  A → B 

True  True  True 
True  False  False 
False  True  True 
False  False  True 
If and only if ($ \Leftrightarrow $) − $A \Leftrightarrow B$ is biconditional logical connective which is true when p and q are same, i.e. both are false or both are true.
The truth table is as follows −
A  B  A ⇔ B 

True  True  True 
True  False  False 
False  True  False 
False  False  True 
Tautologies
A Tautology is a formula which is always true for every value of its propositional variables.
Example − Prove $\lbrack (A \rightarrow B) \land A \rbrack \rightarrow B$ is a tautology
The truth table is as follows −
A  B  A → B  (A → B) ∧ A  [( A → B ) ∧ A] → B 

True  True  True  True  True 
True  False  False  False  True 
False  True  True  False  True 
False  False  True  False  True 
As we can see every value of $\lbrack (A \rightarrow B) \land A \rbrack \rightarrow B$ is "True", it is a tautology.
Contradictions
A Contradiction is a formula which is always false for every value of its propositional variables.
Example − Prove $(A \lor B) \land \lbrack ( \lnot A) \land (\lnot B) \rbrack$ is a contradiction
The truth table is as follows −
A  B  A ∨ B  ¬ A  ¬ B  (¬ A) ∧ ( ¬ B)  (A ∨ B) ∧ [( ¬ A) ∧ (¬ B)] 

True  True  True  False  False  False  False 
True  False  True  False  True  False  False 
False  True  True  True  False  False  False 
False  False  False  True  True  True  False 
As we can see every value of $(A \lor B) \land \lbrack ( \lnot A) \land (\lnot B) \rbrack$ is “False”, it is a contradiction.
Contingency
A Contingency is a formula which has both some true and some false values for every value of its propositional variables.
Example − Prove $(A \lor B) \land (\lnot A)$ a contingency
The truth table is as follows −
A  B  A ∨ B  ¬ A  (A ∨ B) ∧ (¬ A) 

True  True  True  False  False 
True  False  True  False  False 
False  True  True  True  True 
False  False  False  True  False 
As we can see every value of $(A \lor B) \land (\lnot A)$ has both “True” and “False”, it is a contingency.
Propositional Equivalences
Two statements X and Y are logically equivalent if any of the following two conditions hold −
The truth tables of each statement have the same truth values.
The biconditional statement $X \Leftrightarrow Y$ is a tautology.
Example − Prove $\lnot (A \lor B) and \lbrack (\lnot A) \land (\lnot B) \rbrack$ are equivalent
Testing by 1^{st} method (Matching truth table)
A  B  A ∨ B  ¬ (A ∨ B)  ¬ A  ¬ B  [(¬ A) ∧ (¬ B)] 

True  True  True  False  False  False  False 
True  False  True  False  False  True  False 
False  True  True  False  True  False  False 
False  False  False  True  True  True  True 
Here, we can see the truth values of $\lnot (A \lor B) and \lbrack (\lnot A) \land (\lnot B) \rbrack$ are same, hence the statements are equivalent.
Testing by 2^{nd} method (Biconditionality)
A  B  ¬ (A ∨ B )  [(¬ A) ∧ (¬ B)]  [¬ (A ∨ B)] ⇔ [(¬ A ) ∧ (¬ B)] 

True  True  False  False  True 
True  False  False  False  True 
False  True  False  False  True 
False  False  True  True  True 
As $\lbrack \lnot (A \lor B) \rbrack \Leftrightarrow \lbrack (\lnot A ) \land (\lnot B) \rbrack$ is a tautology, the statements are equivalent.
Inverse, Converse, and Contrapositive
Implication / ifthen $(\rightarrow)$ is also called a conditional statement. It has two parts −
 Hypothesis, p
 Conclusion, q
As mentioned earlier, it is denoted as $p \rightarrow q$.
Example of Conditional Statement − “If you do your homework, you will not be punished.” Here, "you do your homework" is the hypothesis, p, and "you will not be punished" is the conclusion, q.
Inverse − An inverse of the conditional statement is the negation of both the hypothesis and the conclusion. If the statement is “If p, then q”, the inverse will be “If not p, then not q”. Thus the inverse of $p \rightarrow q$ is $ \lnot p \rightarrow \lnot q$.
Example − The inverse of “If you do your homework, you will not be punished” is “If you do not do your homework, you will be punished.”
Converse − The converse of the conditional statement is computed by interchanging the hypothesis and the conclusion. If the statement is “If p, then q”, the converse will be “If q, then p”. The converse of $p \rightarrow q$ is $q \rightarrow p$.
Example − The converse of "If you do your homework, you will not be punished" is "If you will not be punished, you do your homework”.
Contrapositive − The contrapositive of the conditional is computed by interchanging the hypothesis and the conclusion of the inverse statement. If the statement is “If p, then q”, the contrapositive will be “If not q, then not p”. The contrapositive of $p \rightarrow q$ is $\lnot q \rightarrow \lnot p$.
Example − The Contrapositive of " If you do your homework, you will not be punished” is "If you are punished, you did not do your homework”.
Duality Principle
Duality principle states that for any true statement, the dual statement obtained by interchanging unions into intersections (and vice versa) and interchanging Universal set into Null set (and vice versa) is also true. If dual of any statement is the statement itself, it is said selfdual statement.
Example − The dual of $(A \cap B ) \cup C$ is $(A \cup B) \cap C$
Normal Forms
We can convert any proposition in two normal forms −
 Conjunctive normal form
 Disjunctive normal form
Conjunctive Normal Form
A compound statement is in conjunctive normal form if it is obtained by operating AND among variables (negation of variables included) connected with ORs. In terms of set operations, it is a compound statement obtained by Intersection among variables connected with Unions.
Examples
$(A \lor B) \land (A \lor C) \land (B \lor C \lor D)$
$(P \cup Q) \cap (Q \cup R)$
Disjunctive Normal Form
A compound statement is in conjunctive normal form if it is obtained by operating OR among variables (negation of variables included) connected with ANDs. In terms of set operations, it is a compound statement obtained by Union among variables connected with Intersections.
Examples
$(A \land B) \lor (A \land C) \lor (B \land C \land D)$
$(P \cap Q) \cup (Q \cap R)$
Discrete Mathematics  Predicate Logic
Predicate Logic deals with predicates, which are propositions containing variables.
Predicate Logic – Definition
A predicate is an expression of one or more variables defined on some specific domain. A predicate with variables can be made a proposition by either assigning a value to the variable or by quantifying the variable.
The following are some examples of predicates −
 Let E(x, y) denote "x = y"
 Let X(a, b, c) denote "a + b + c = 0"
 Let M(x, y) denote "x is married to y"
Well Formed Formula
Well Formed Formula (wff) is a predicate holding any of the following −
All propositional constants and propositional variables are wffs
If x is a variable and Y is a wff, $\forall x Y$ and $\exists x Y$ are also wff
Truth value and false values are wffs
Each atomic formula is a wff
All connectives connecting wffs are wffs
Quantifiers
The variable of predicates is quantified by quantifiers. There are two types of quantifier in predicate logic − Universal Quantifier and Existential Quantifier.
Universal Quantifier
Universal quantifier states that the statements within its scope are true for every value of the specific variable. It is denoted by the symbol $\forall$.
$\forall x P(x)$ is read as for every value of x, P(x) is true.
Example − "Man is mortal" can be transformed into the propositional form $\forall x P(x)$ where P(x) is the predicate which denotes x is mortal and the universe of discourse is all men.
Existential Quantifier
Existential quantifier states that the statements within its scope are true for some values of the specific variable. It is denoted by the symbol $\exists $.
$\exists x P(x)$ is read as for some values of x, P(x) is true.
Example − "Some people are dishonest" can be transformed into the propositional form $\exists x P(x)$ where P(x) is the predicate which denotes x is dishonest and the universe of discourse is some people.
Nested Quantifiers
If we use a quantifier that appears within the scope of another quantifier, it is called nested quantifier.
Example
$\forall\ a\: \exists b\: P (x, y)$ where $P (a, b)$ denotes $a + b = 0$
$\forall\ a\: \forall\: b\: \forall\: c\: P (a, b, c)$ where $P (a, b)$ denotes $a + (b + c) = (a + b) + c$
Note − $\forall\: a\: \exists b\: P (x, y) \ne \exists a\: \forall b\: P (x, y)$
Discrete Mathematics  Rules of Inference
To deduce new statements from the statements whose truth that we already know, Rules of Inference are used.
What are Rules of Inference for?
Mathematical logic is often used for logical proofs. Proofs are valid arguments that determine the truth values of mathematical statements.
An argument is a sequence of statements. The last statement is the conclusion and all its preceding statements are called premises (or hypothesis). The symbol “$\therefore$”, (read therefore) is placed before the conclusion. A valid argument is one where the conclusion follows from the truth values of the premises.
Rules of Inference provide the templates or guidelines for constructing valid arguments from the statements that we already have.
Table of Rules of Inference
Rule of Inference  Name  Rule of Inference  Name 

$$\begin{matrix} P \\ \hline \therefore P \lor Q \end{matrix}$$ 
Addition 
$$\begin{matrix} P \lor Q \\ \lnot P \\ \hline \therefore Q \end{matrix}$$ 
Disjunctive Syllogism 
$$\begin{matrix} P \\ Q \\ \hline \therefore P \land Q \end{matrix}$$ 
Conjunction 
$$\begin{matrix} P \rightarrow Q \\ Q \rightarrow R \\ \hline \therefore P \rightarrow R \end{matrix}$$ 
Hypothetical Syllogism 
$$\begin{matrix} P \land Q\\ \hline \therefore P \end{matrix}$$ 
Simplification 
$$\begin{matrix} ( P \rightarrow Q ) \land (R \rightarrow S) \\ P \lor R \\ \hline \therefore Q \lor S \end{matrix}$$ 
Constructive Dilemma 
$$\begin{matrix} P \rightarrow Q \\ P \\ \hline \therefore Q \end{matrix}$$ 
Modus Ponens 
$$\begin{matrix} (P \rightarrow Q) \land (R \rightarrow S) \\ \lnot Q \lor \lnot S \\ \hline \therefore \lnot P \lor \lnot R \end{matrix}$$ 
Destructive Dilemma 
$$\begin{matrix} P \rightarrow Q \\ \lnot Q \\ \hline \therefore \lnot P \end{matrix}$$ 
Modus Tollens 
Addition
If P is a premise, we can use Addition rule to derive $ P \lor Q $.
$$\begin{matrix} P \\ \hline \therefore P \lor Q \end{matrix}$$
Example
Let P be the proposition, “He studies very hard” is true
Therefore − "Either he studies very hard Or he is a very bad student." Here Q is the proposition “he is a very bad student”.
Conjunction
If P and Q are two premises, we can use Conjunction rule to derive $ P \land Q $.
$$\begin{matrix} P \\ Q \\ \hline \therefore P \land Q \end{matrix}$$
Example
Let P − “He studies very hard”
Let Q − “He is the best boy in the class”
Therefore − "He studies very hard and he is the best boy in the class"
Simplification
If $P \land Q$ is a premise, we can use Simplification rule to derive P.
$$\begin{matrix} P \land Q\\ \hline \therefore P \end{matrix}$$
Example
"He studies very hard and he is the best boy in the class", $P \land Q$
Therefore − "He studies very hard"
Modus Ponens
If P and $P \rightarrow Q$ are two premises, we can use Modus Ponens to derive Q.
$$\begin{matrix} P \rightarrow Q \\ P \\ \hline \therefore Q \end{matrix}$$
Example
"If you have a password, then you can log on to facebook", $P \rightarrow Q$
"You have a password", P
Therefore − "You can log on to facebook"
Modus Tollens
If $P \rightarrow Q$ and $\lnot Q$ are two premises, we can use Modus Tollens to derive $\lnot P$.
$$\begin{matrix} P \rightarrow Q \\ \lnot Q \\ \hline \therefore \lnot P \end{matrix}$$
Example
"If you have a password, then you can log on to facebook", $P \rightarrow Q$
"You cannot log on to facebook", $\lnot Q$
Therefore − "You do not have a password "
Disjunctive Syllogism
If $\lnot P$ and $P \lor Q$ are two premises, we can use Disjunctive Syllogism to derive Q.
$$\begin{matrix} \lnot P \\ P \lor Q \\ \hline \therefore Q \end{matrix}$$
Example
"The ice cream is not vanilla flavored", $\lnot P$
"The ice cream is either vanilla flavored or chocolate flavored", $P \lor Q$
Therefore − "The ice cream is chocolate flavored”
Hypothetical Syllogism
If $P \rightarrow Q$ and $Q \rightarrow R$ are two premises, we can use Hypothetical Syllogism to derive $P \rightarrow R$
$$\begin{matrix} P \rightarrow Q \\ Q \rightarrow R \\ \hline \therefore P \rightarrow R \end{matrix}$$
Example
"If it rains, I shall not go to school”, $P \rightarrow Q$
"If I don't go to school, I won't need to do homework", $Q \rightarrow R$
Therefore − "If it rains, I won't need to do homework"
Constructive Dilemma
If $( P \rightarrow Q ) \land (R \rightarrow S)$ and $P \lor R$ are two premises, we can use constructive dilemma to derive $Q \lor S$.
$$\begin{matrix} ( P \rightarrow Q ) \land (R \rightarrow S) \\ P \lor R \\ \hline \therefore Q \lor S \end{matrix}$$
Example
“If it rains, I will take a leave”, $( P \rightarrow Q )$
“If it is hot outside, I will go for a shower”, $(R \rightarrow S)$
“Either it will rain or it is hot outside”, $P \lor R$
Therefore − "I will take a leave or I will go for a shower"
Destructive Dilemma
If $(P \rightarrow Q) \land (R \rightarrow S)$ and $ \lnot Q \lor \lnot S $ are two premises, we can use destructive dilemma to derive $\lnot P \lor \lnot R$.
$$\begin{matrix} (P \rightarrow Q) \land (R \rightarrow S) \\ \lnot Q \lor \lnot S \\ \hline \therefore \lnot P \lor \lnot R \end{matrix}$$
Example
“If it rains, I will take a leave”, $(P \rightarrow Q )$
“If it is hot outside, I will go for a shower”, $(R \rightarrow S)$
“Either I will not take a leave or I will not go for a shower”, $\lnot Q \lor \lnot S$
Therefore − "Either it does not rain or it is not hot outside"
Operators & Postulates
Group Theory is a branch of mathematics and abstract algebra that defines an algebraic structure named as group. Generally, a group comprises of a set of elements and an operation over any two elements on that set to form a third element also in that set.
In 1854, Arthur Cayley, the British Mathematician, gave the modern definition of group for the first time −
“A set of symbols all of them different, and such that the product of any two of them (no matter in what order), or the product of any one of them into itself, belongs to the set, is said to be a group. These symbols are not in general convertible [commutative], but are associative.”
In this chapter, we will know about operators and postulates that form the basics of set theory, group theory and Boolean algebra.
Any set of elements in a mathematical system may be defined with a set of operators and a number of postulates.
A binary operator defined on a set of elements is a rule that assigns to each pair of elements a unique element from that set. For example, given the set $ A = \lbrace 1, 2, 3, 4, 5 \rbrace $, we can say $\otimes$ is a binary operator for the operation $c = a \otimes b$, if it specifies a rule for finding c for the pair of $(a,b)$, such that $a,b,c \in A$.
The postulates of a mathematical system form the basic assumptions from which rules can be deduced. The postulates are −
Closure
A set is closed with respect to a binary operator if for every pair of elements in the set, the operator finds a unique element from that set.
Example
Let $A = \lbrace 0, 1, 2, 3, 4, 5, \dots \rbrace$
This set is closed under binary operator into $(\ast)$, because for the operation $c = a \ast b$, for any $a, b \in A$, the product $c \in A$.
The set is not closed under binary operator divide $(\div)$, because, for the operation $c = a \div b$, for any $a, b \in A$, the product c may not be in the set A. If $a = 7, b = 2$, then $c = 3.5$. Here $a,b \in A$ but $c \notin A$.
Associative Laws
A binary operator $\otimes$ on a set A is associative when it holds the following property −
$(x \otimes y) \otimes z = x \otimes (y \otimes z)$, where $x, y, z \in A $
Example
Let $A = \lbrace 1, 2, 3, 4 \rbrace$
The operator plus $( + )$ is associative because for any three elements, $x,y,z \in A$, the property $(x + y) + z = x + ( y + z )$ holds.
The operator minus $(  )$ is not associative since
$$( x  y )  z \ne x  ( y  z )$$
Commutative Laws
A binary operator $\otimes$ on a set A is commutative when it holds the following property −
$x \otimes y = y \otimes x$, where $x, y \in A$
Example
Let $A = \lbrace 1, 2, 3, 4 \rbrace$
The operator plus $( + )$ is commutative because for any two elements, $x,y \in A$, the property $x + y = y + x$ holds.
The operator minus $(  )$ is not associative since
$$x  y \ne y  x$$
Distributive Laws
Two binary operators $\otimes$ and $\circledast$ on a set A, are distributive over operator $\circledast$ when the following property holds −
$x \otimes (y \circledast z) = (x \otimes y) \circledast (x \otimes z)$, where $x, y, z \in A $
Example
Let $A = \lbrace 1, 2, 3, 4 \rbrace$
The operators into $( * )$ and plus $( + )$ are distributive over operator + because for any three elements, $x,y,z \in A$, the property $x * ( y + z ) = ( x * y ) + ( x * z )$ holds.
However, these operators are not distributive over $*$ since
$$x + ( y * z ) \ne ( x + y ) * ( x + z )$$
Identity Element
A set A has an identity element with respect to a binary operation $\otimes$ on A, if there exists an element $e \in A$, such that the following property holds −
$e \otimes x = x \otimes e$, where $x \in A$
Example
Let $Z = \lbrace 0, 1, 2, 3, 4, 5, \dots \rbrace$
The element 1 is an identity element with respect to operation $*$ since for any element $x \in Z$,
$$1 * x = x * 1$$
On the other hand, there is no identity element for the operation minus $(  )$
Inverse
If a set A has an identity element $e$ with respect to a binary operator $\otimes $, it is said to have an inverse whenever for every element $x \in A$, there exists another element $y \in A$, such that the following property holds −
$$x \otimes y = e$$
Example
Let $A = \lbrace \dots 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, \dots \rbrace$
Given the operation plus $( + )$ and $e = 0$, the inverse of any element x is $(x)$ since $x + (x) = 0$
De Morgan's Law
De Morgan’s Laws gives a pair of transformations between union and intersection of two (or more) sets in terms of their complements. The laws are −
$$(A \cup B)' = A' \cap B'$$
$$(A \cap B)' = A' \cup B'$$
Example
Let $A = \lbrace 1, 2, 3, 4 \rbrace ,B = \lbrace 1, 3, 5, 7 \rbrace$, and
Universal set $U = \lbrace 1, 2, 3, \dots, 9, 10 \rbrace$
$A' = \lbrace 5, 6, 7, 8, 9, 10 \rbrace$
$B' = \lbrace 2, 4, 6, 8, 9, 10 \rbrace$
$A \cup B = \lbrace 1, 2, 3, 4, 5, 7 \rbrace$
$A \cap B = \lbrace 1, 3 \rbrace $
$(A \cup B)' = \lbrace 6, 8, 9, 10 \rbrace$
$A' \cap B' = \lbrace 6, 8, 9, 10 \rbrace$
Thus, we see that $(A \cup B)' = A' \cap B'$
$(A \cap B)' = \lbrace 2, 4, 5, 6, 7, 8, 9, 10 \rbrace$
$A' \cup B' = \lbrace 2, 4, 5, 6, 7, 8, 9, 10 \rbrace$
Thus, we see that $(A \cap B)' = A' \cup B'$
Discrete Mathematics  Group Theory
Semigroup
A finite or infinite set $‘S’$ with a binary operation $‘\omicron’$ (Composition) is called semigroup if it holds following two conditions simultaneously −
Closure − For every pair $(a, b) \in S, \:(a \omicron b)$ has to be present in the set $S$.
Associative − For every element $a, b, c \in S, (a \omicron b) \omicron c = a \omicron (b \omicron c)$ must hold.
Example
The set of positive integers (excluding zero) with addition operation is a semigroup. For example, $ S = \lbrace 1, 2, 3, \dots \rbrace $
Here closure property holds as for every pair $(a, b) \in S, (a + b)$ is present in the set S. For example, $1 + 2 = 3 \in S]$
Associative property also holds for every element $a, b, c \in S, (a + b) + c = a + (b + c)$. For example, $(1 + 2) + 3 = 1 + (2 + 3) = 5$
Monoid
A monoid is a semigroup with an identity element. The identity element (denoted by $e$ or E) of a set S is an element such that $(a \omicron e) = a$, for every element $a \in S$. An identity element is also called a unit element. So, a monoid holds three properties simultaneously − Closure, Associative, Identity element.
Example
The set of positive integers (excluding zero) with multiplication operation is a monoid. $S = \lbrace 1, 2, 3, \dots \rbrace $
Here closure property holds as for every pair $(a, b) \in S, (a \times b)$ is present in the set S. [For example, $1 \times 2 = 2 \in S$ and so on]
Associative property also holds for every element $a, b, c \in S, (a \times b) \times c = a \times (b \times c)$ [For example, $(1 \times 2) \times 3 = 1 \times (2 \times 3) = 6$ and so on]
Identity property also holds for every element $a \in S, (a \times e) = a$ [For example, $(2 \times 1) = 2, (3 \times 1) = 3$ and so on]. Here identity element is 1.
Group
A group is a monoid with an inverse element. The inverse element (denoted by I) of a set S is an element such that $(a \omicron I) = (I \omicron a) = a$, for each element $a \in S$. So, a group holds four properties simultaneously  i) Closure, ii) Associative, iii) Identity element, iv) Inverse element. The order of a group G is the number of elements in G and the order of an element in a group is the least positive integer n such that an is the identity element of that group G.
Examples
The set of $N \times N$ nonsingular matrices form a group under matrix multiplication operation.
The product of two $N \times N$ nonsingular matrices is also an $N \times N$ nonsingular matrix which holds closure property.
Matrix multiplication itself is associative. Hence, associative property holds.
The set of $N \times N$ nonsingular matrices contains the identity matrix holding the identity element property.
As all the matrices are nonsingular they all have inverse elements which are also nonsingular matrices. Hence, inverse property also holds.
Abelian Group
An abelian group G is a group for which the element pair $(a,b) \in G$ always holds commutative law. So, a group holds five properties simultaneously  i) Closure, ii) Associative, iii) Identity element, iv) Inverse element, v) Commutative.
Example
The set of positive integers (including zero) with addition operation is an abelian group. $G = \lbrace 0, 1, 2, 3, \dots \rbrace$
Here closure property holds as for every pair $(a, b) \in S, (a + b)$ is present in the set S. [For example, $1 + 2 = 2 \in S$ and so on]
Associative property also holds for every element $a, b, c \in S, (a + b) + c = a + (b + c)$ [For example, $(1 +2) + 3 = 1 + (2 + 3) = 6$ and so on]
Identity property also holds for every element $a \in S, (a \times e) = a$ [For example, $(2 \times 1) = 2, (3 \times 1) = 3$ and so on]. Here, identity element is 1.
Commutative property also holds for every element $a \in S, (a \times b) = (b \times a)$ [For example, $(2 \times 3) = (3 \times 2) = 3$ and so on]
Cyclic Group and Subgroup
A cyclic group is a group that can be generated by a single element. Every element of a cyclic group is a power of some specific element which is called a generator. A cyclic group can be generated by a generator ‘g’, such that every other element of the group can be written as a power of the generator ‘g’.
Example
The set of complex numbers $\lbrace 1,1, i, i \rbrace$ under multiplication operation is a cyclic group.
There are two generators − $i$ and $–i$ as $i^1 = i, i^2 = 1, i^3 = i, i^4 = 1$ and also $(–i)^1 = i, (–i)^2 = 1, (–i)^3 = i, (–i)^4 = 1$ which covers all the elements of the group. Hence, it is a cyclic group.
Note − A cyclic group is always an abelian group but not every abelian group is a cyclic group. The rational numbers under addition is not cyclic but is abelian.
A subgroup H is a subset of a group G (denoted by $H ≤ G$) if it satisfies the four properties simultaneously − Closure, Associative, Identity element, and Inverse.
A subgroup H of a group G that does not include the whole group G is called a proper subgroup (Denoted by $H < G$). A subgroup of a cyclic group is cyclic and a abelian subgroup is also abelian.
Example
Let a group $G = \lbrace 1, i, 1, i \rbrace$
Then some subgroups are $H_1 = \lbrace 1 \rbrace, H_2 = \lbrace 1,1 \rbrace$,
This is not a subgroup − $H_3 = \lbrace 1, i \rbrace$ because that $(i)^{1} = i$ is not in $H_3$
Partially Ordered Set (POSET)
A partially ordered set consists of a set with a binary relation which is reflexive, antisymmetric and transitive. "Partially ordered set" is abbreviated as POSET.
Examples
The set of real numbers under binary operation less than or equal to $(\le)$ is a poset.
Let the set $S = \lbrace 1, 2, 3 \rbrace$ and the operation is $\le$
The relations will be $\lbrace(1, 1), (2, 2), (3, 3), (1, 2), (1, 3), (2, 3)\rbrace$
This relation R is reflexive as $\lbrace (1, 1), (2, 2), (3, 3)\rbrace \in R$
This relation R is antisymmetric, as
$\lbrace (1, 2), (1, 3), (2, 3) \rbrace \in R\ and\ \lbrace (1, 2), (1, 3), (2, 3) \rbrace ∉ R$
This relation R is also transitive as $\lbrace (1,2), (2,3), (1,3)\rbrace \in R$.
Hence, it is a poset.
The vertex set of a directed acyclic graph under the operation ‘reachability’ is a poset.
Hasse Diagram
The Hasse diagram of a poset is the directed graph whose vertices are the element of that poset and the arcs covers the pairs (x, y) in the poset. If in the poset $x < y$, then the point x appears lower than the point y in the Hasse diagram. If $x<y<z$ in the poset, then the arrow is not shown between x and z as it is implicit.
Example
The poset of subsets of $\lbrace 1, 2, 3 \rbrace = \lbrace \emptyset, \lbrace 1 \rbrace, \lbrace 2 \rbrace, \lbrace 3 \rbrace, \lbrace 1, 2 \rbrace, \lbrace 1, 3 \rbrace, \lbrace 2, 3 \rbrace, \lbrace 1, 2, 3 \rbrace \rbrace$ is shown by the following Hasse diagram −
Linearly Ordered Set
A Linearly ordered set or Total ordered set is a partial order set in which every pair of element is comparable. The elements $a, b \in S$ are said to be comparable if either $a \le b$ or $b \le a$ holds. Trichotomy law defines this total ordered set. A totally ordered set can be defined as a distributive lattice having the property $\lbrace a \lor b, a \land b \rbrace = \lbrace a, b \rbrace$ for all values of a and b in set S.
Example
The powerset of $\lbrace a, b \rbrace$ ordered by \subseteq is a totally ordered set as all the elements of the power set $P = \lbrace \emptyset, \lbrace a \rbrace, \lbrace b \rbrace, \lbrace a, b\rbrace \rbrace$ are comparable.
Example of nontotal order set
A set $S = \lbrace 1, 2, 3, 4, 5, 6 \rbrace$ under operation x divides y is not a total ordered set.
Here, for all $(x, y) \in S, x  y$ have to hold but it is not true that 2  3, as 2 does not divide 3 or 3 does not divide 2. Hence, it is not a total ordered set.
Lattice
A lattice is a poset $(L, \le)$ for which every pair $\lbrace a, b \rbrace \in L$ has a least upper bound (denoted by $a \lor b$) and a greatest lower bound (denoted by $a \land b$). LUB $(\lbrace a,b \rbrace)$ is called the join of a and b. GLB $(\lbrace a,b \rbrace )$ is called the meet of a and b.
Example
This above figure is a lattice because for every pair $\lbrace a, b \rbrace \in L$, a GLB and a LUB exists.
This above figure is a not a lattice because $GLB (a, b)$ and $LUB (e, f)$ does not exist.
Some other lattices are discussed below −
Bounded Lattice
A lattice L becomes a bounded lattice if it has a greatest element 1 and a least element 0.
Complemented Lattice
A lattice L becomes a complemented lattice if it is a bounded lattice and if every element in the lattice has a complement. An element x has a complement x’ if $\exists x(x \land x’=0 and x \lor x’ = 1)$
Distributive Lattice
If a lattice satisfies the following two distribute properties, it is called a distributive lattice.
$a \lor (b \land c) = (a \lor b) \land (a \lor c)$
$a \land (b \lor c) = (a \land b) \lor (a \land c)$
Modular Lattice
If a lattice satisfies the following property, it is called modular lattice.
$a \land( b \lor (a \land d)) = (a \land b) \lor (a \land d)$
Properties of Lattices
Idempotent Properties
$a \lor a = a$
$a \land a = a$
Absorption Properties
$a \lor (a \land b) = a$
$a \land (a \lor b) = a$
Commutative Properties
$a \lor b = b \lor a$
$a \land b = b \land a$
Associative Properties
$a \lor (b \lor c) = (a \lor b) \lor c$
$a \land (b \land c) = (a \land b) \land c$
Dual of a Lattice
The dual of a lattice is obtained by interchanging the '$\lor$' and '$\land$' operations.
Example
The dual of $\lbrack a \lor (b \land c) \rbrack\ is\ \lbrack a \land (b \lor c) \rbrack$
Discrete Mathematics  Counting Theory
In daily lives, many a times one needs to find out the number of all possible outcomes for a series of events. For instance, in how many ways can a panel of judges comprising of 6 men and 4 women be chosen from among 50 men and 38 women? How many different 10 lettered PAN numbers can be generated such that the first five letters are capital alphabets, the next four are digits and the last is again a capital letter. For solving these problems, mathematical theory of counting are used. Counting mainly encompasses fundamental counting rule, the permutation rule, and the combination rule.
The Rules of Sum and Product
The Rule of Sum and Rule of Product are used to decompose difficult counting problems into simple problems.
The Rule of Sum − If a sequence of tasks $T_1, T_2, \dots, T_m$ can be done in $w_1, w_2, \dots w_m$ ways respectively (the condition is that no tasks can be performed simultaneously), then the number of ways to do one of these tasks is $w_1 + w_2 + \dots +w_m$. If we consider two tasks A and B which are disjoint (i.e. $A \cap B = \emptyset$), then mathematically $A \cup B = A + B$
The Rule of Product − If a sequence of tasks $T_1, T_2, \dots, T_m$ can be done in $w_1, w_2, \dots w_m$ ways respectively and every task arrives after the occurrence of the previous task, then there are $w_1 \times w_2 \times \dots \times w_m$ ways to perform the tasks. Mathematically, if a task B arrives after a task A, then $A \times B = A\timesB$
Example
Question − A boy lives at X and wants to go to School at Z. From his home X he has to first reach Y and then Y to Z. He may go X to Y by either 3 bus routes or 2 train routes. From there, he can either choose 4 bus routes or 5 train routes to reach Z. How many ways are there to go from X to Z?
Solution − From X to Y, he can go in $3 + 2 = 5$ ways (Rule of Sum). Thereafter, he can go Y to Z in $4 + 5 = 9$ ways (Rule of Sum). Hence from X to Z he can go in $5 \times 9 = 45$ ways (Rule of Product).
Permutations
A permutation is an arrangement of some elements in which order matters. In other words a Permutation is an ordered Combination of elements.
Examples
From a set S ={x, y, z} by taking two at a time, all permutations are −
$ xy, yx, xz, zx, yz, zy $.
We have to form a permutation of three digit numbers from a set of numbers $S = \lbrace 1, 2, 3 \rbrace$. Different three digit numbers will be formed when we arrange the digits. The permutation will be = 123, 132, 213, 231, 312, 321
Number of Permutations
The number of permutations of ‘n’ different things taken ‘r’ at a time is denoted by $n_{P_{r}}$
$$n_{P_{r}} = \frac{n!}{(n  r)!}$$
where $n! = 1.2.3. \dots (n  1).n$
Proof − Let there be ‘n’ different elements.
There are n number of ways to fill up the first place. After filling the first place (n1) number of elements is left. Hence, there are (n1) ways to fill up the second place. After filling the first and second place, (n2) number of elements is left. Hence, there are (n2) ways to fill up the third place. We can now generalize the number of ways to fill up rth place as [n – (r–1)] = n–r+1
So, the total no. of ways to fill up from first place up to rthplace −
$n_{ P_{ r } } = n (n1) (n2)..... (nr + 1)$
$= [n(n1)(n2) ... (nr + 1)] [(nr)(nr1) \dots 3.2.1] / [(nr)(nr1) \dots 3.2.1]$
Hence,
$n_{ P_{ r } } = n! / (nr)!$
Some important formulas of permutation
If there are n elements of which $a_1$ are alike of some kind, $a_2$ are alike of another kind; $a_3$ are alike of third kind and so on and $a_r$ are of $r^{th}$ kind, where $(a_1 + a_2 + ... a_r) = n$.
Then, number of permutations of these n objects is = $n! / [(a_1!(a_2!) \dots (a_r!)]$.
Number of permutations of n distinct elements taking n elements at a time = $n_{P_n} = n!$
The number of permutations of n dissimilar elements taking r elements at a time, when x particular things always occupy definite places = $nx_{p_{rx}}$
The number of permutations of n dissimilar elements when r specified things always come together is − $r! (n−r+1)!$
The number of permutations of n dissimilar elements when r specified things never come together is − $n!–[r! (n−r+1)!]$
The number of circular permutations of n different elements taken x elements at time = $^np_{x}/x$
The number of circular permutations of n different things = $^np_{n}/n$
Some Problems
Problem 1 − From a bunch of 6 different cards, how many ways we can permute it?
Solution − As we are taking 6 cards at a time from a deck of 6 cards, the permutation will be $^6P_{6} = 6! = 720$
Problem 2 − In how many ways can the letters of the word 'READER' be arranged?
Solution − There are 6 letters word (2 E, 1 A, 1D and 2R.) in the word 'READER'.
The permutation will be $= 6! /\: [(2!) (1!)(1!)(2!)] = 180.$
Problem 3 − In how ways can the letters of the word 'ORANGE' be arranged so that the consonants occupy only the even positions?
Solution − There are 3 vowels and 3 consonants in the word 'ORANGE'. Number of ways of arranging the consonants among themselves $= ^3P_{3} = 3! = 6$. The remaining 3 vacant places will be filled up by 3 vowels in $^3P_{3} = 3! = 6$ ways. Hence, the total number of permutation is $6 \times 6 = 36$
Combinations
A combination is selection of some given elements in which order does not matter.
The number of all combinations of n things, taken r at a time is −
$$^nC_{ { r } } = \frac { n! } { r!(nr)! }$$
Problem 1
Find the number of subsets of the set $\lbrace1, 2, 3, 4, 5, 6\rbrace$ having 3 elements.
Solution
The cardinality of the set is 6 and we have to choose 3 elements from the set. Here, the ordering does not matter. Hence, the number of subsets will be $^6C_{3} = 20$.
Problem 2
There are 6 men and 5 women in a room. In how many ways we can choose 3 men and 2 women from the room?
Solution
The number of ways to choose 3 men from 6 men is $^6C_{3}$ and the number of ways to choose 2 women from 5 women is $^5C_{2}$
Hence, the total number of ways is − $^6C_{3} \times ^5C_{2} = 20 \times 10 = 200$
Problem 3
How many ways can you choose 3 distinct groups of 3 students from total 9 students?
Solution
Let us number the groups as 1, 2 and 3
For choosing 3 students for 1^{st} group, the number of ways − $^9C_{3}$
The number of ways for choosing 3 students for 2^{nd} group after choosing 1st group − $^6C_{3}$
The number of ways for choosing 3 students for 3^{rd} group after choosing 1^{st} and 2^{nd} group − $^3C_{3}$
Hence, the total number of ways $= ^9C_{3} \times ^6C_{3} \times ^3C_{3} = 84 \times 20 \times 1 = 1680$
Pascal's Identity
Pascal's identity, first derived by Blaise Pascal in 17^{th} century, states that the number of ways to choose k elements from n elements is equal to the summation of number of ways to choose (k1) elements from (n1) elements and the number of ways to choose elements from n1 elements.
Mathematically, for any positive integers k and n: $^nC_{k} = ^n{^}^1C_{k1} + ^n{^}^1{C_k}$
Proof −
$$^n{^}^1C_{k1} + ^n{^}^1{C_k}$$
$= \frac{ (n1)! } { (k1)!(nk)! } + \frac{ (n1)! } { k!(nk1)! }$
$= (n1)!(\frac{ k } { k!(nk)! } + \frac{ nk } { k!(nk)! } )$
$= (n1)! \frac{ n } { k!(nk)! }$
$= \frac{ n! } { k!(nk)! }$
$= n_{ C_{ k } }$
Pigeonhole Principle
In 1834, German mathematician, Peter Gustav Lejeune Dirichlet, stated a principle which he called the drawer principle. Now, it is known as the pigeonhole principle.
Pigeonhole Principle states that if there are fewer pigeon holes than total number of pigeons and each pigeon is put in a pigeon hole, then there must be at least one pigeon hole with more than one pigeon. If n pigeons are put into m pigeonholes where n > m, there's a hole with more than one pigeon.
Examples
Ten men are in a room and they are taking part in handshakes. If each person shakes hands at least once and no man shakes the same man’s hand more than once then two men took part in the same number of handshakes.
There must be at least two people in a class of 30 whose names start with the same alphabet.
The InclusionExclusion principle
The Inclusionexclusion principle computes the cardinal number of the union of multiple nondisjoint sets. For two sets A and B, the principle states −
$A \cup B = A + B  A \cap B$
For three sets A, B and C, the principle states −
$A \cup B \cup C  = A + B + C  A \cap B  A \cap C  B \cap C + A \cap B \cap C $
The generalized formula 
$\bigcup_{i=1}^{n}A_i=\sum\limits_{1\leq i<j<k\leq n}A_i \cap A_j+\sum\limits_{1\leq i<j<k\leq n}A_i \cap A_j \cap A_k \dots +(1)^{\pi1}A_1 \cap \dots \cap A_2$
Problem 1
How many integers from 1 to 50 are multiples of 2 or 3 but not both?
Solution
From 1 to 100, there are $50/2 = 25$ numbers which are multiples of 2.
There are $50/3 = 16$ numbers which are multiples of 3.
There are $50/6 = 8$ numbers which are multiples of both 2 and 3.
So, $A=25$, $B=16$ and $A \cap B= 8$.
$A \cup B = A + B  A \cap B = 25 + 16  8 = 33$
Problem 2
In a group of 50 students 24 like cold drinks and 36 like hot drinks and each student likes at least one of the two drinks. How many like both coffee and tea?
Solution
Let X be the set of students who like cold drinks and Y be the set of people who like hot drinks.
So, $ X \cup Y  = 50$, $X = 24$, $Y = 36$
$X \cap Y = X + Y  X \cup Y = 24 + 36  50 = 60  50 = 10$
Hence, there are 10 students who like both tea and coffee.
Discrete Mathematics  Probability
Closely related to the concepts of counting is Probability. We often try to guess the results of games of chance, like card games, slot machines, and lotteries; i.e. we try to find the likelihood or probability that a particular result with be obtained.
Probability can be conceptualized as finding the chance of occurrence of an event. Mathematically, it is the study of random processes and their outcomes. The laws of probability have a wide applicability in a variety of fields like genetics, weather forecasting, opinion polls, stock markets etc.
Basic Concepts
Probability theory was invented in the 17th century by two French mathematicians, Blaise Pascal and Pierre de Fermat, who were dealing with mathematical problems regarding of chance.
Before proceeding to details of probability, let us get the concept of some definitions.
Random Experiment − An experiment in which all possible outcomes are known and the exact output cannot be predicted in advance is called a random experiment. Tossing a fair coin is an example of random experiment.
Sample Space − When we perform an experiment, then the set S of all possible outcomes is called the sample space. If we toss a coin, the sample space $S = \left \{ H, T \right \}$
Event − Any subset of a sample space is called an event. After tossing a coin, getting Head on the top is an event.
The word "probability" means the chance of occurrence of a particular event. The best we can say is how likely they are to happen, using the idea of probability.
$Probability\:of\:occurence\:of\:an\:event = \frac{Total\:number\:of\:favourable \: outcome}{Total\:number\:of\:Outcomes}$
As the occurrence of any event varies between 0% and 100%, the probability varies between 0 and 1.
Steps to find the probability
Step 1 − Calculate all possible outcomes of the experiment.
Step 2 − Calculate the number of favorable outcomes of the experiment.
Step 3 − Apply the corresponding probability formula.
Tossing a Coin
If a coin is tossed, there are two possible outcomes − Heads $(H)$ or Tails $(T)$
So, Total number of outcomes = 2
Hence, the probability of getting a Head $(H)$ on top is 1/2 and the probability of getting a Tails $(T)$ on top is 1/2
Throwing a Dice
When a dice is thrown, six possible outcomes can be on the top − $1, 2, 3, 4, 5, 6$.
The probability of any one of the numbers is 1/6
The probability of getting even numbers is 3/6 = 1/2
The probability of getting odd numbers is 3/6 = 1/2
Taking Cards From a Deck
From a deck of 52 cards, if one card is picked find the probability of an ace being drawn and also find the probability of a diamond being drawn.
Total number of possible outcomes − 52
Outcomes of being an ace − 4
Probability of being an ace = 4/52 = 1/13
Probability of being a diamond = 13/52 = 1/4
Probability Axioms
The probability of an event always varies from 0 to 1. $[0 \leq P(x) \leq 1]$
For an impossible event the probability is 0 and for a certain event the probability is 1.
If the occurrence of one event is not influenced by another event, they are called mutually exclusive or disjoint.
If $A_1, A_2....A_n$ are mutually exclusive/disjoint events, then $P(A_i \cap A_j) = \emptyset $ for $i \ne j$ and $P(A_1 \cup A_2 \cup.... A_n) = P(A_1) + P(A_2)+..... P(A_n)$
Properties of Probability
If there are two events $x$ and $\overline{x}$which are complementary, then the probability of the complementary event is −
$$p(\overline{x}) = 1p(x)$$
For two nondisjoint events A and B, the probability of the union of two events −
$P(A \cup B) = P(A) + P(B)$
If an event A is a subset of another event B (i.e. $A \subset B$), then the probability of A is less than or equal to the probability of B. Hence, $A \subset B$ implies $P(A) \leq p(B)$
Conditional Probability
The conditional probability of an event B is the probability that the event will occur given an event A has already occurred. This is written as $P(BA)$.
Mathematically − $ P(BA) = P(A \cap B)/ P(A)$
If event A and B are mutually exclusive, then the conditional probability of event B after the event A will be the probability of event B that is $P(B)$.
Problem 1
In a country 50% of all teenagers own a cycle and 30% of all teenagers own a bike and cycle. What is the probability that a teenager owns bike given that the teenager owns a cycle?
Solution
Let us assume A is the event of teenagers owning only a cycle and B is the event of teenagers owning only a bike.
So, $P(A) = 50/100 = 0.5$ and $P(A \cap B) = 30/100 = 0.3$ from the given problem.
$P(BA) = P(A \cap B)/ P(A) = 0.3/ 0.5 = 0.6$
Hence, the probability that a teenager owns bike given that the teenager owns a cycle is 60%.
Problem 2
In a class, 50% of all students play cricket and 25% of all students play cricket and volleyball. What is the probability that a student plays volleyball given that the student plays cricket?
Solution
Let us assume A is the event of students playing only cricket and B is the event of students playing only volleyball.
So, $P(A) = 50/100 =0.5$ and $P(A \cap B) = 25/ 100 =0.25$ from the given problem.
$P\lgroup B\rvert A \rgroup= P\lgroup A\cap B\rgroup/P\lgroup A \rgroup =0.25/0.5=0.5$
Hence, the probability that a student plays volleyball given that the student plays cricket is 50%.
Problem 3
Six good laptops and three defective laptops are mixed up. To find the defective laptops all of them are tested onebyone at random. What is the probability to find both of the defective laptops in the first two pick?
Solution
Let A be the event that we find a defective laptop in the first test and B be the event that we find a defective laptop in the second test.
Hence, $P(A \cap B) = P(A)P(BA) =3/9 \times 2/8 = 1/12$
Bayes' Theorem
Theorem − If A and B are two mutually exclusive events, where $P(A)$ is the probability of A and $P(B)$ is the probability of B, $P(A  B)$ is the probability of A given that B is true. $P(B  A)$ is the probability of B given that A is true, then Bayes’ Theorem states −
$$P(AB) = \frac{P(BA) P(A)}{\sum_{i = 1}^{n}P(BAi)P(Ai)}$$
Application of Bayes' Theorem
In situations where all the events of sample space are mutually exclusive events.
In situations where either $P( A_i \cap B )$ for each $A_i$ or $P( A_i )$ and $P(BA_i)$ for each $A_i$ is known.
Problem
Consider three penstands. The first penstand contains 2 red pens and 3 blue pens; the second one has 3 red pens and 2 blue pens; and the third one has 4 red pens and 1 blue pen. There is equal probability of each penstand to be selected. If one pen is drawn at random, what is the probability that it is a red pen?
Solution
Let $A_i$ be the event that i^{th} penstand is selected.
Here, i = 1,2,3.
Since probability for choosing a penstand is equal, $P(A_i) = 1/3$
Let B be the event that a red pen is drawn.
The probability that a red pen is chosen among the five pens of the first penstand,
$P(BA_1) = 2/5$
The probability that a red pen is chosen among the five pens of the second penstand,
$P(BA_2) = 3/5$
The probability that a red pen is chosen among the five pens of the third penstand,
$P(BA_3) = 4/5$
According to Bayes' Theorem,
$P(B) = P(A_1).P(BA_1) + P(A_2).P(BA_2) + P(A_3).P(BA_3)$
$= 1/3 . 2/5\: +\: 1/3 . 3/5\: +\: 1/3 . 4/5$
$= 3/5$
Mathematical Induction
Mathematical induction, is a technique for proving results or establishing statements for natural numbers. This part illustrates the method through a variety of examples.
Definition
Mathematical Induction is a mathematical technique which is used to prove a statement, a formula or a theorem is true for every natural number.
The technique involves two steps to prove a statement, as stated below −
Step 1(Base step) − It proves that a statement is true for the initial value.
Step 2(Inductive step) − It proves that if the statement is true for the n^{th} iteration (or number n), then it is also true for (n+1)^{th} iteration ( or number n+1).
How to Do It
Step 1 − Consider an initial value for which the statement is true. It is to be shown that the statement is true for n = initial value.
Step 2 − Assume the statement is true for any value of n = k. Then prove the statement is true for n = k+1. We actually break n = k+1 into two parts, one part is n = k (which is already proved) and try to prove the other part.
Problem 1
$3^n1$ is a multiple of 2 for n = 1, 2, ...
Solution
Step 1 − For $n = 1, 3^11 = 31 = 2$ which is a multiple of 2
Step 2 − Let us assume $3^n1$ is true for $n=k$, Hence, $3^k 1$ is true (It is an assumption)
We have to prove that $3^{k+1}1$ is also a multiple of 2
$3^{k+1}  1 = 3 \times 3^k  1 = (2 \times 3^k) + (3^k  1)$
The first part $(2 \times 3k)$ is certain to be a multiple of 2 and the second part $(3k 1)$ is also true as our previous assumption.
Hence, $3^{k+1} – 1$ is a multiple of 2.
So, it is proved that $3^n – 1$ is a multiple of 2.
Problem 2
$1 + 3 + 5 + ... + (2n1) = n^2$ for $n = 1, 2, \dots $
Solution
Step 1 − For $n=1, 1 = 1^2$, Hence, step 1 is satisfied.
Step 2 − Let us assume the statement is true for $n=k$.
Hence, $1 + 3 + 5 + \dots + (2k1) = k^2$ is true (It is an assumption)
We have to prove that $1 + 3 + 5 + ... + (2(k+1)1) = (k+1)^2$ also holds
$1 + 3 + 5 + \dots + (2(k+1)  1)$
$= 1 + 3 + 5 + \dots + (2k+2  1)$
$= 1 + 3 + 5 + \dots + (2k + 1)$
$= 1 + 3 + 5 + \dots + (2k  1) + (2k + 1)$
$= k^2 + (2k + 1)$
$= (k + 1)^2$
So, $1 + 3 + 5 + \dots + (2(k+1)  1) = (k+1)^2$ hold which satisfies the step 2.
Hence, $1 + 3 + 5 + \dots + (2n  1) = n^2$ is proved.
Problem 3
Prove that $(ab)^n = a^nb^n$ is true for every natural number $n$
Solution
Step 1 − For $n=1, (ab)^1 = a^1b^1 = ab$, Hence, step 1 is satisfied.
Step 2 − Let us assume the statement is true for $n=k$, Hence, $(ab)^k = a^kb^k$ is true (It is an assumption).
We have to prove that $(ab)^{k+1} = a^{k+1}b^{k+1}$ also hold
Given, $(ab)^k = a^k b^k$
Or, $(ab)^k (ab) = (a^k b^k ) (ab)$ [Multiplying both side by 'ab']
Or, $(ab)^{k+1} = (aa^k) ( bb^k)$
Or, $(ab)^{k+1} = (a^{k+1}b^{k+1})$
Hence, step 2 is proved.
So, $(ab)^n = a^nb^n$ is true for every natural number n.
Strong Induction
Strong Induction is another form of mathematical induction. Through this induction technique, we can prove that a propositional function, $P(n)$ is true for all positive integers, $n$, using the following steps −
Step 1(Base step) − It proves that the initial proposition $P(1)$ true.
Step 2(Inductive step) − It proves that the conditional statement $[P(1) \land P(2) \land P(3) \land \dots \land P(k)] → P(k + 1)$ is true for positive integers $k$.
Discrete Mathematics  Recurrence Relation
In this chapter, we will discuss how recursive techniques can derive sequences and be used for solving counting problems. The procedure for finding the terms of a sequence in a recursive manner is called recurrence relation. We study the theory of linear recurrence relations and their solutions. Finally, we introduce generating functions for solving recurrence relations.
Definition
A recurrence relation is an equation that recursively defines a sequence where the next term is a function of the previous terms (Expressing $F_n$ as some combination of $F_i$ with $i < n$).
Example − Fibonacci series − $F_n = F_{n1} + F_{n2}$, Tower of Hanoi − $F_n = 2F_{n1} + 1$
Linear Recurrence Relations
A linear recurrence equation of degree k or order k is a recurrence equation which is in the format $x_n= A_1 x_{n1}+ A_2 x_{n1}+ A_3 x_{n1}+ \dots A_k x_{nk} $($A_n$ is a constant and $A_k \neq 0$) on a sequence of numbers as a firstdegree polynomial.
These are some examples of linear recurrence equations −
Recurrence relations  Initial values  Solutions 

F_{n} = F_{n1} + F_{n2}  a_{1} = a_{2} = 1  Fibonacci number 
F_{n} = F_{n1} + F_{n2}  a_{1} = 1, a_{2} = 3  Lucas Number 
F_{n} = F_{n2} + F_{n3}  a_{1} = a_{2} = a_{3} = 1  Padovan sequence 
F_{n} = 2F_{n1} + F_{n2}  a_{1} = 0, a_{2} = 1  Pell number 
How to solve linear recurrence relation
Suppose, a two ordered linear recurrence relation is − $F_n = AF_{n1} +BF_{n2}$ where A and B are real numbers.
The characteristic equation for the above recurrence relation is −
$$x^2  Ax  B = 0$$
Three cases may occur while finding the roots −
Case 1 − If this equation factors as $(x x_1)(x x_1) = 0$ and it produces two distinct real roots $x_1$ and $x_2$, then $F_n = ax_1^n+ bx_2^n$ is the solution. [Here, a and b are constants]
Case 2 − If this equation factors as $(x x_1)^2 = 0$ and it produces single real root $x_1$, then $F_n = a x_1^n+ bn x_1^n$ is the solution.
Case 3 − If the equation produces two distinct complex roots, $x_1$ and $x_2$ in polar form $x_1 = r \angle \theta$ and $x_2 = r \angle( \theta)$, then $F_n = r^n (a cos(n\theta)+ b sin(n\theta))$ is the solution.
Problem 1
Solve the recurrence relation $F_n = 5F_{n1}  6F_{n2}$ where $F_0 = 1$ and $F_1 = 4$
Solution
The characteristic equation of the recurrence relation is −
$$x^2  5x + 6 = 0,$$
So, $(x  3) (x  2) = 0$
Hence, the roots are −
$x_1 = 3$ and $x_2 = 2$
The roots are real and distinct. So, this is in the form of case 1
Hence, the solution is −
$$F_n = ax_1^n + bx_2^n$$
Here, $F_n = a3^n + b2^n\ (As\ x_1 = 3\ and\ x_2 = 2)$
Therefore,
$1 = F_0 = a3^0 + b2^0 = a+b$
$4 = F_1 = a3^1 + b2^1 = 3a+2b$
Solving these two equations, we get $ a = 2$ and $b = 1$
Hence, the final solution is −
$$F_n = 2.3^n + (1) . 2^n = 2.3^n  2^n $$
Problem 2
Solve the recurrence relation − $F_n = 10F_{n1}  25F_{n2}$ where $F_0 = 3$ and $F_1 = 17$
Solution
The characteristic equation of the recurrence relation is −
$$ x^2  10x 25 = 0$$
So $(x  5)^2 = 0$
Hence, there is single real root $x_1 = 5$
As there is single real valued root, this is in the form of case 2
Hence, the solution is −
$F_n = ax_1^n + bnx_1^n$
$3 = F_0 = a.5^0 + b.0.5^0 = a$
$17 = F_1 = a.5^1 + b.1.5^1 = 5a+5b$
Solving these two equations, we get $a = 3$ and $b = 2/5$
Hence, the final solution is − $F_n = 3.5^n +( 2/5) .n.2^n $
Problem 3
Solve the recurrence relation $F_n = 2F_{n1}  2F_{n2}$ where $F_0 = 1$ and $F_1 = 3$
Solution
The characteristic equation of the recurrence relation is −
$$x^2 2x 2 = 0$$
Hence, the roots are −
$x_1 = 1 + i$ and $x_2 = 1  i$
In polar form,
$x_1 = r \angle \theta$ and $x_2 = r \angle( \theta),$ where $r = \sqrt 2$ and $\theta = \frac{\pi}{4}$
The roots are imaginary. So, this is in the form of case 3.
Hence, the solution is −
$F_n = (\sqrt 2 )^n (a cos(n .\sqcap /4) + b sin(n .\sqcap /4))$
$1 = F_0 = (\sqrt 2 )^0 (a cos(0 .\sqcap /4) + b sin(0 .\sqcap /4) ) = a$
$3 = F_1 = (\sqrt 2 )^1 (a cos(1 .\sqcap /4) + b sin(1 . \sqcap /4) ) = \sqrt 2 ( a/ \sqrt 2 + b/ \sqrt 2)$
Solving these two equations we get $a = 1$ and $b = 2$
Hence, the final solution is −
$F_n = (\sqrt 2 )^n (cos(n .\pi /4 ) + 2 sin(n .\pi /4 ))$
NonHomogeneous Recurrence Relation and Particular Solutions
A recurrence relation is called nonhomogeneous if it is in the form
$F_n = AF_{n1} + BF_{n2} + f(n)$ where $f(n) \ne 0$
Its associated homogeneous recurrence relation is $F_n = AF_{n–1} + BF_{n2}$
The solution $(a_n)$ of a nonhomogeneous recurrence relation has two parts.
First part is the solution $(a_h)$ of the associated homogeneous recurrence relation and the second part is the particular solution $(a_t)$.
$$a_n=a_h+a_t$$
Solution to the first part is done using the procedures discussed in the previous section.
To find the particular solution, we find an appropriate trial solution.
Let $f(n) = cx^n$ ; let $x^2 = Ax + B$ be the characteristic equation of the associated homogeneous recurrence relation and let $x_1$ and $x_2$ be its roots.
If $x \ne x_1$ and $x \ne x_2$, then $a_t = Ax^n$
If $x = x_1$, $x \ne x_2$, then $a_t = Anx^n$
If $x = x_1 = x_2$, then $a_t = An^2x^n$
Example
Let a nonhomogeneous recurrence relation be $F_n = AF_{n–1} + BF_{n2} + f(n)$ with characteristic roots $x_1 = 2$ and $x_2 = 5$. Trial solutions for different possible values of $f(n)$ are as follows −
f(n)  Trial solutions 

4  A 
5.2^{n}  An2^{n} 
8.5^{n}  An5^{n} 
4^{n}  A4^{n} 
2n^{2}+3n+1  An^{2}+Bn+C 
Problem
Solve the recurrence relation $F_n = 3F_{n1} + 10F_{n2} + 7.5^n$ where $F_0 = 4$ and $F_1 = 3$
Solution
This is a linear nonhomogeneous relation, where the associated homogeneous equation is $F_n=3F_{n1}+10F_{n2}$ and $f(n)=7.5^n$
The characteristic equation of its associated homogeneous relation is −
$$x^2  3x 10 = 0$$
Or, $(x  5)(x + 2) = 0$
Or, $x_1= 5$ and $x_2 = 2$
Hence $a_h = a.5^n + b.(2)^n$ , where a and b are constants.
Since $f(n) = 7.5^n$, i.e. of the form $c.x^n$, a reasonable trial solution of at will be $Anx^n$
$a_t = Anx^n = An5^n$
After putting the solution in the recurrence relation, we get −
$An5^n = 3A(n – 1)5^{n1} + 10A(n – 2)5^{n2} + 7.5^n$
Dividing both sides by $5^{n2}$, we get
$An5^2 = 3A(n  1)5 + 10A(n  2)5^0 + 7.5^2$
Or, $25An = 15An  15A + 10An  20A + 175$
Or, $35A = 175$
Or, $A = 5$
So, $F_n = An5^n= 5n5^n=n5^{n+1}$
The solution of the recurrence relation can be written as −
$F_n = a_h + a_t$
$=a.5^n+b.(2)^n+n5^{n+1}$
Putting values of $F_0 = 4$ and $F_1 = 3$, in the above equation, we get $a = 2$ and $b = 6$
Hence, the solution is −
$F_n = n5^{n+1} + 6.(2)^n 2.5^n$
Generating Functions
Generating Functions represents sequences where each term of a sequence is expressed as a coefficient of a variable x in a formal power series.
Mathematically, for an infinite sequence, say $a_0, a_1, a_2,\dots, a_k,\dots,$ the generating function will be −
$$G_x=a_0+a_1x+a_2x^2+ \dots +a_kx^k+ \dots = \sum_{k=0}^{\infty}a_kx^k$$
Some Areas of Application
Generating functions can be used for the following purposes −
For solving a variety of counting problems. For example, the number of ways to make change for a Rs. 100 note with the notes of denominations Rs.1, Rs.2, Rs.5, Rs.10, Rs.20 and Rs.50
For solving recurrence relations
For proving some of the combinatorial identities
For finding asymptotic formulae for terms of sequences
Problem 1
What are the generating functions for the sequences $\lbrace {a_k} \rbrace$ with $a_k = 2$ and $a_k = 3k$?
Solution
When $a_k = 2$, generating function, $G(x) = \sum_{k = 0}^{\infty }2x^{k} = 2 + 2x + 2x^{2} + 2x^{3} + \dots$
When $a_{k} = 3k, G(x) = \sum_{k = 0}^{\infty }3kx^{k} = 0 + 3x + 6x^{2} + 9x^{3} + \dots\dots$
Problem 2
What is the generating function of the infinite series; $1, 1, 1, 1, \dots$?
Solution
Here, $a_k = 1$, for $0 \le k \le \infty$
Hence, $G(x) = 1 + x + x^{2} + x^{3}+ \dots \dots= \frac{1}{(1  x)}$
Some Useful Generating Functions
For $a_k = a^{k}, G(x) = \sum_{k = 0}^{\infty }a^{k}x^{k} = 1 + ax + a^{2}x^{2} +\dots \dots \dots = 1/ (1  ax)$
For $a_{k} = (k + 1), G(x) = \sum_{k = 0}^{\infty }(k + 1)x^{k} = 1 + 2x + 3x^{2} \dots \dots \dots =\frac{1}{(1  x)^{2}}$
For $a_{k} = c_{k}^{n}, G(x) = \sum_{k = 0}^{\infty} c_{k}^{n}x^{k} = 1+c_{1}^{n}x + c_{2}^{n}x^{2} + \dots \dots \dots + x^{2} = (1 + x)^{n}$
For $a_{k} = \frac{1}{k!}, G(x) = \sum_{k = 0}^{\infty }\frac{x^{k}}{k!} = 1 + x + \frac{x^{2}}{2!} + \frac{x^{3}}{3!}\dots \dots \dots = e^{x}$
Graph & Graph Models
The previous part brought forth the different tools for reasoning, proofing and problem solving. In this part, we will study the discrete structures that form the basis of formulating many a reallife problem.
The two discrete structures that we will cover are graphs and trees. A graph is a set of points, called nodes or vertices, which are interconnected by a set of lines called edges. The study of graphs, or graph theory is an important part of a number of disciplines in the fields of mathematics, engineering and computer science.
What is a Graph?
Definition − A graph (denoted as $G = (V, E)$) consists of a nonempty set of vertices or nodes V and a set of edges E.
Example − Let us consider, a Graph is $G = (V, E)$ where $V = \lbrace a, b, c, d \rbrace $ and $E = \lbrace \lbrace a, b \rbrace, \lbrace a, c \rbrace, \lbrace b, c \rbrace, \lbrace c, d \rbrace \rbrace$
Degree of a Vertex − The degree of a vertex V of a graph G (denoted by deg (V)) is the number of edges incident with the vertex V.
Vertex  Degree  Even / Odd 

a  2  even 
b  2  even 
c  3  odd 
d  1  odd 
Even and Odd Vertex − If the degree of a vertex is even, the vertex is called an even vertex and if the degree of a vertex is odd, the vertex is called an odd vertex.
Degree of a Graph − The degree of a graph is the largest vertex degree of that graph. For the above graph the degree of the graph is 3.
The Handshaking Lemma − In a graph, the sum of all the degrees of all the vertices is equal to twice the number of edges.
Types of Graphs
There are different types of graphs, which we will learn in the following section.
Null Graph
A null graph has no edges. The null graph of $n$ vertices is denoted by $N_n$
Simple Graph
A graph is called simple graph/strict graph if the graph is undirected and does not contain any loops or multiple edges.
MultiGraph
If in a graph multiple edges between the same set of vertices are allowed, it is called Multigraph. In other words, it is a graph having at least one loop or multiple edges.
Directed and Undirected Graph
A graph $G = (V, E)$ is called a directed graph if the edge set is made of ordered vertex pair and a graph is called undirected if the edge set is made of unordered vertex pair.
Connected and Disconnected Graph
A graph is connected if any two vertices of the graph are connected by a path; while a graph is disconnected if at least two vertices of the graph are not connected by a path. If a graph G is disconnected, then every maximal connected subgraph of $G$ is called a connected component of the graph $G$.
Regular Graph
A graph is regular if all the vertices of the graph have the same degree. In a regular graph G of degree $r$, the degree of each vertex of $G$ is r.
Complete Graph
A graph is called complete graph if every two vertices pair are joined by exactly one edge. The complete graph with n vertices is denoted by $K_n$
Cycle Graph
If a graph consists of a single cycle, it is called cycle graph. The cycle graph with n vertices is denoted by $C_n$
Bipartite Graph
If the vertexset of a graph G can be split into two disjoint sets, $V_1$ and $V_2$, in such a way that each edge in the graph joins a vertex in $V_1$ to a vertex in $V_2$, and there are no edges in G that connect two vertices in $V_1$ or two vertices in $V_2$, then the graph $G$ is called a bipartite graph.
Complete Bipartite Graph
A complete bipartite graph is a bipartite graph in which each vertex in the first set is joined to every single vertex in the second set. The complete bipartite graph is denoted by $K_{x,y}$ where the graph $G$ contains $x$ vertices in the first set and $y$ vertices in the second set.
Representation of Graphs
There are mainly two ways to represent a graph −
 Adjacency Matrix
 Adjacency List
Adjacency Matrix
An Adjacency Matrix $A[V][V]$ is a 2D array of size $V \times V$ where $V$ is the number of vertices in a undirected graph. If there is an edge between $V_x$ to $V_y$ then the value of $A[V_x][V_y]=1$ and $A[V_y][V_x]=1$, otherwise the value will be zero. And for a directed graph, if there is an edge between $V_x$ to $V_y$, then the value of $A[V_x][V_y]=1$, otherwise the value will be zero.
Adjacency Matrix of an Undirected Graph
Let us consider the following undirected graph and construct the adjacency matrix −
Adjacency matrix of the above undirected graph will be −
a 
b 
c 
d 

a 
0 
1 
1 
0 
b 
1 
0 
1 
0 
c 
1 
1 
0 
1 
d 
0 
0 
1 
0 
Adjacency Matrix of a Directed Graph
Let us consider the following directed graph and construct its adjacency matrix −
Adjacency matrix of the above directed graph will be −
a 
b 
c 
d 

a 
0 
1 
1 
0 
b 
0 
0 
1 
0 
c 
0 
0 
0 
1 
d 
0 
0 
0 
0 
Adjacency List
In adjacency list, an array $(A[V])$ of linked lists is used to represent the graph G with $V$ number of vertices. An entry $A[V_x]$ represents the linked list of vertices adjacent to the $Vxth$ vertex. The adjacency list of the undirected graph is as shown in the figure below −
Planar vs. Nonplanar graph
Planar graph − A graph $G$ is called a planar graph if it can be drawn in a plane without any edges crossed. If we draw graph in the plane without edge crossing, it is called embedding the graph in the plane.
Nonplanar graph − A graph is nonplanar if it cannot be drawn in a plane without graph edges crossing.
Isomorphism
If two graphs G and H contain the same number of vertices connected in the same way, they are called isomorphic graphs (denoted by $G \cong H$).
It is easier to check nonisomorphism than isomorphism. If any of these following conditions occurs, then two graphs are nonisomorphic −
 The number of connected components are different
 Vertexset cardinalities are different
 Edgeset cardinalities are different
 Degree sequences are different
Example
The following graphs are isomorphic −
Homomorphism
A homomorphism from a graph $G$ to a graph $H$ is a mapping (May not be a bijective mapping)$ h: G \rightarrow H$ such that − $(x, y) \in E(G) \rightarrow (h(x), h(y)) \in E(H)$. It maps adjacent vertices of graph $G$ to the adjacent vertices of the graph $H$.
Properties of Homomorphisms
A homomorphism is an isomorphism if it is a bijective mapping.
Homomorphism always preserves edges and connectedness of a graph.
The compositions of homomorphisms are also homomorphisms.
To find out if there exists any homomorphic graph of another graph is a NPcomplete problem.
Euler Graphs
A connected graph $G$ is called an Euler graph, if there is a closed trail which includes every edge of the graph $G$. An Euler path is a path that uses every edge of a graph exactly once. An Euler path starts and ends at different vertices.
An Euler circuit is a circuit that uses every edge of a graph exactly once. An Euler circuit always starts and ends at the same vertex. A connected graph $G$ is an Euler graph if and only if all vertices of $G$ are of even degree, and a connected graph $G$ is Eulerian if and only if its edge set can be decomposed into cycles.
The above graph is an Euler graph as $“a\: 1\: b\: 2\: c\: 3\: d\: 4\: e\: 5\: c\: 6\: f\: 7\: g”$ covers all the edges of the graph.
Hamiltonian Graphs
A connected graph $G$ is called Hamiltonian graph if there is a cycle which includes every vertex of $G$ and the cycle is called Hamiltonian cycle. Hamiltonian walk in graph $G$ is a walk that passes through each vertex exactly once.
If $G$ is a simple graph with n vertices, where $n \geq 3$ If $deg(v) \geq \frac{n}{2}$ for each vertex $v$, then the graph $G$ is Hamiltonian graph. This is called Dirac's Theorem.
If $G$ is a simple graph with $n$ vertices, where $n \geq 2$ if $deg(x) + deg(y) \geq n$ for each pair of nonadjacent vertices x and y, then the graph $G$ is Hamiltonian graph. This is called Ore's theorem.
Discrete Mathematics  More On Graphs
Graph Coloring
Graph coloring is the procedure of assignment of colors to each vertex of a graph G such that no adjacent vertices get same color. The objective is to minimize the number of colors while coloring a graph. The smallest number of colors required to color a graph G is called its chromatic number of that graph. Graph coloring problem is a NP Complete problem.
Method to Color a Graph
The steps required to color a graph G with n number of vertices are as follows −
Step 1 − Arrange the vertices of the graph in some order.
Step 2 − Choose the first vertex and color it with the first color.
Step 3 − Choose the next vertex and color it with the lowest numbered color that has not been colored on any vertices adjacent to it. If all the adjacent vertices are colored with this color, assign a new color to it. Repeat this step until all the vertices are colored.
Example
In the above figure, at first vertex $a$ is colored red. As the adjacent vertices of vertex a are again adjacent, vertex $b$ and vertex $d$ are colored with different color, green and blue respectively. Then vertex $c$ is colored as red as no adjacent vertex of $c$ is colored red. Hence, we could color the graph by 3 colors. Hence, the chromatic number of the graph is 3.
Applications of Graph Coloring
Some applications of graph coloring include −
 Register Allocation
 Map Coloring
 Bipartite Graph Checking
 Mobile Radio Frequency Assignment
 Making time table, etc.
Graph Traversal
Graph traversal is the problem of visiting all the vertices of a graph in some systematic order. There are mainly two ways to traverse a graph.
 Breadth First Search
 Depth First Search
Breadth First Search
Breadth First Search (BFS) starts at starting level0 vertex $X$ of the graph $G$. Then we visit all the vertices that are the neighbors of $X$. After visiting, we mark the vertices as "visited," and place them into level1. Then we start from the level1 vertices and apply the same method on every level1 vertex and so on. The BFS traversal terminates when every vertex of the graph has been visited.
BFS Algorithm
The concept is to visit all the neighbor vertices before visiting other neighbor vertices of neighbor vertices.
Initialize status of all nodes as “Ready”.
Put source vertex in a queue and change its status to “Waiting”.
Repeat the following two steps until queue is empty −
Remove the first vertex from the queue and mark it as “Visited”.
Add to the rear of queue all neighbors of the removed vertex whose status is “Ready”. Mark their status as “Waiting”.
Problem
Let us take a graph (Source vertex is ‘a’) and apply the BFS algorithm to find out the traversal order.
Solution −
Initialize status of all vertices to “Ready”.
Put a in queue and change its status to “Waiting”.
Remove a from queue, mark it as “Visited”.
Add a’s neighbors in “Ready” state b, d and e to end of queue and mark them as “Waiting”.
Remove b from queue, mark it as “Visited”, put its “Ready” neighbor c at end of queue and mark c as “Waiting”.
Remove d from queue and mark it as “Visited”. It has no neighbor in “Ready” state.
Remove e from queue and mark it as “Visited”. It has no neighbor in “Ready” state.
Remove c from queue and mark it as “Visited”. It has no neighbor in “Ready” state.
Queue is empty so stop.
So the traversal order is −
$a \rightarrow b \rightarrow d \rightarrow e \rightarrow c$
The alternate orders of traversal are −
$a \rightarrow b \rightarrow e \rightarrow d \rightarrow c$
Or, $a \rightarrow d \rightarrow b \rightarrow e \rightarrow c$
Or, $a \rightarrow e \rightarrow b \rightarrow d \rightarrow c$
Or, $a \rightarrow b \rightarrow e \rightarrow d \rightarrow c$
Or, $a \rightarrow d \rightarrow e \rightarrow b \rightarrow c$
Application of BFS
 Finding the shortest path
 Minimum spanning tree for unweighted graph
 GPS navigation system
 Detecting cycles in an undirected graph
 Finding all nodes within one connected component
Complexity Analysis
Let $G(V, E)$ be a graph with $V$ number of vertices and $E$ number of edges. If breadth first search algorithm visits every vertex in the graph and checks every edge, then its time complexity would be −
$$O(  V  +  E  ). O(  E  )$$
It may vary between $O(1)$ and $O(V2)$
Depth First Search
Depth First Search (DFS) algorithm starts from a vertex $v$, then it traverses to its adjacent vertex (say x) that has not been visited before and mark as "visited" and goes on with the adjacent vertex of $x$ and so on.
If at any vertex, it encounters that all the adjacent vertices are visited, then it backtracks until it finds the first vertex having an adjacent vertex that has not been traversed before. Then, it traverses that vertex, continues with its adjacent vertices until it traverses all visited vertices and has to backtrack again. In this way, it will traverse all the vertices reachable from the initial vertex $v$.
DFS Algorithm
The concept is to visit all the neighbor vertices of a neighbor vertex before visiting the other neighbor vertices.
Initialize status of all nodes as “Ready”
Put source vertex in a stack and change its status to “Waiting”
Repeat the following two steps until stack is empty −
Pop the top vertex from the stack and mark it as “Visited”
Push onto the top of the stack all neighbors of the removed vertex whose status is “Ready”. Mark their status as “Waiting”.
Problem
Let us take a graph (Source vertex is ‘a’) and apply the DFS algorithm to find out the traversal order.
Solution
Initialize status of all vertices to “Ready”.
Push a in stack and change its status to “Waiting”.
Pop a and mark it as “Visited”.
Push a’s neighbors in “Ready” state e, d and b to top of stack and mark them as “Waiting”.
Pop b from stack, mark it as “Visited”, push its “Ready” neighbor c onto stack.
Pop c from stack and mark it as “Visited”. It has no “Ready” neighbor.
Pop d from stack and mark it as “Visited”. It has no “Ready” neighbor.
Pop e from stack and mark it as “Visited”. It has no “Ready” neighbor.
Stack is empty. So stop.
So the traversal order is −
$a \rightarrow b \rightarrow c \rightarrow d \rightarrow e$
The alternate orders of traversal are −
$a \rightarrow e \rightarrow b \rightarrow c \rightarrow d$
Or, $a \rightarrow b \rightarrow e \rightarrow c \rightarrow d$
Or, $a \rightarrow d \rightarrow e \rightarrow b \rightarrow c$
Or, $a \rightarrow d \rightarrow c \rightarrow e \rightarrow b$
Or, $a \rightarrow d \rightarrow c \rightarrow b \rightarrow e$
Complexity Analysis
Let $G(V, E)$ be a graph with $V$ number of vertices and $E$ number of edges. If DFS algorithm visits every vertex in the graph and checks every edge, then the time complexity is −
$$\circleddash (  V  +  E  )$$
Applications
 Detecting cycle in a graph
 To find topological sorting
 To test if a graph is bipartite
 Finding connected components
 Finding the bridges of a graph
 Finding biconnectivity in graphs
 Solving the Knight’s Tour problem
 Solving puzzles with only one solution
Introduction to Trees
Tree is a discrete structure that represents hierarchical relationships between individual elements or nodes. A tree in which a parent has no more than two children is called a binary tree.
Tree and its Properties
Definition − A Tree is a connected acyclic undirected graph. There is a unique path between every pair of vertices in $G$. A tree with N number of vertices contains $(N1)$ number of edges. The vertex which is of 0 degree is called root of the tree. The vertex which is of 1 degree is called leaf node of the tree and the degree of an internal node is at least 2.
Example − The following is an example of a tree −
Centers and BiCenters of a Tree
The center of a tree is a vertex with minimal eccentricity. The eccentricity of a vertex $X$ in a tree $G$ is the maximum distance between the vertex $X$ and any other vertex of the tree. The maximum eccentricity is the tree diameter. If a tree has only one center, it is called Central Tree and if a tree has only more than one centers, it is called Bicentral Tree. Every tree is either central or bicentral.
Algorithm to find centers and bicenters of a tree
Step 1 − Remove all the vertices of degree 1 from the given tree and also remove their incident edges.
Step 2 − Repeat step 1 until either a single vertex or two vertices joined by an edge is left. If a single vertex is left then it is the center of the tree and if two vertices joined by an edge is left then it is the bicenter of the tree.
Problem 1
Find out the center/bicenter of the following tree −
Solution
At first, we will remove all vertices of degree 1 and also remove their incident edges and get the following tree −
Again, we will remove all vertices of degree 1 and also remove their incident edges and get the following tree −
Finally we got a single vertex ‘c’ and we stop the algorithm. As there is single vertex, this tree has one center ‘c’ and the tree is a central tree.
Problem 2
Find out the center/bicenter of the following tree −
Solution
At first, we will remove all vertices of degree 1 and also remove their incident edges and get the following tree −
Again, we will remove all vertices of degree 1 and also remove their incident edges and get the following tree −
Finally, we got two vertices ‘c’ and ‘d’ left, hence we stop the algorithm. As two vertices joined by an edge is left, this tree has bicenter ‘cd’ and the tree is bicentral.
Labeled Trees
Definition − A labeled tree is a tree the vertices of which are assigned unique numbers from 1 to n. We can count such trees for small values of n by hand so as to conjecture a general formula. The number of labeled trees of n number of vertices is $n^{n2}$. Two labeled trees are isomorphic if their graphs are isomorphic and the corresponding points of the two trees have the same labels.
Example
Unlabeled Trees
Definition − An unlabeled tree is a tree the vertices of which are not assigned any numbers. The number of labeled trees of n number of vertices is $\frac {(2n)!}{ (n+1)!n! }$ (n^{th} Catalan number)
Example
Rooted Tree
A rooted tree $G$ is a connected acyclic graph with a special node that is called the root of the tree and every edge directly or indirectly originates from the root. An ordered rooted tree is a rooted tree where the children of each internal vertex are ordered. If every internal vertex of a rooted tree has not more than m children, it is called an mary tree. If every internal vertex of a rooted tree has exactly m children, it is called a full mary tree. If $m = 2$, the rooted tree is called a binary tree.
Binary Search Tree
Binary Search tree is a binary tree which satisfies the following property −
 $X$ in left subtree of vertex $V, Value(X) \le Value (V)$
 $Y$ in right subtree of vertex $V, Value(Y) \ge Value (V)$
So, the value of all the vertices of the left subtree of an internal node $V$ are less than or equal to $V$ and the value of all the vertices of the right subtree of the internal node $V$ are greater than or equal to $V$. The number of links from the root node to the deepest node is the height of the Binary Search Tree.
Example
Algorithm to search for a key in BST
BST_Search(x, k) if ( x = NIL or k = Value[x] ) return x; if ( k < Value[x]) return BST_Search (left[x], k); else return BST_Search (right[x], k)
Complexity of Binary search tree
Average Case  Worst case  

Space Complexity  O(n)  O(n) 
Search Complexity  O(log n)  O(n) 
Insertion Complexity  O(log n)  O(n) 
Deletion Complexity  O(log n)  O(n) 
Discrete Mathematics  Spanning Trees
A spanning tree of a connected undirected graph $G$ is a tree that minimally includes all of the vertices of $G$. A graph may have many spanning trees.
Example
Minimum Spanning Tree
A spanning tree with assigned weight less than or equal to the weight of every possible spanning tree of a weighted, connected and undirected graph $G$, it is called minimum spanning tree (MST). The weight of a spanning tree is the sum of all the weights assigned to each edge of the spanning tree.
Example
Kruskal's Algorithm
Kruskal's algorithm is a greedy algorithm that finds a minimum spanning tree for a connected weighted graph. It finds a tree of that graph which includes every vertex and the total weight of all the edges in the tree is less than or equal to every possible spanning tree.
Algorithm
Step 1 − Arrange all the edges of the given graph $G (V,E)$ in nondecreasing order as per their edge weight.
Step 2 − Choose the smallest weighted edge from the graph and check if it forms a cycle with the spanning tree formed so far.
Step 3 − If there is no cycle, include this edge to the spanning tree else discard it.
Step 4 − Repeat Step 2 and Step 3 until $(V1)$ number of edges are left in the spanning tree.
Problem
Suppose we want to find minimum spanning tree for the following graph G using Kruskal’s algorithm.
Solution
From the above graph we construct the following table −
Edge No.  Vertex Pair  Edge Weight 

E1  (a, b)  20 
E2  (a, c)  9 
E3  (a, d)  13 
E4  (b, c)  1 
E5  (b, e)  4 
E6  (b, f)  5 
E7  (c, d)  2 
E8  (d, e)  3 
E9  (d, f)  14 
Now we will rearrange the table in ascending order with respect to Edge weight −
Edge No.  Vertex Pair  Edge Weight 

E4  (b, c)  1 
E7  (c, d)  2 
E8  (d, e)  3 
E5  (b, e)  4 
E6  (b, f)  5 
E2  (a, c)  9 
E3  (a, d)  13 
E9  (d, f)  14 
E1  (a, b)  20 
Since we got all the 5 edges in the last figure, we stop the algorithm and this is the minimal spanning tree and its total weight is $(1 + 2 + 3 + 5 + 9) = 20$.
Prim's Algorithm
Prim's algorithm, discovered in 1930 by mathematicians, Vojtech Jarnik and Robert C. Prim, is a greedy algorithm that finds a minimum spanning tree for a connected weighted graph. It finds a tree of that graph which includes every vertex and the total weight of all the edges in the tree is less than or equal to every possible spanning tree. Prim’s algorithm is faster on dense graphs.
Algorithm
Initialize the minimal spanning tree with a single vertex, randomly chosen from the graph.
Repeat steps 3 and 4 until all the vertices are included in the tree.
Select an edge that connects the tree with a vertex not yet in the tree, so that the weight of the edge is minimal and inclusion of the edge does not form a cycle.
Add the selected edge and the vertex that it connects to the tree.
Problem
Suppose we want to find minimum spanning tree for the following graph G using Prim’s algorithm.
Solution
Here we start with the vertex ‘a’ and proceed.
This is the minimal spanning tree and its total weight is $(1 + 2 + 3 + 5 + 9) = 20$.
Boolean Expressions & Functions
Boolean algebra is algebra of logic. It deals with variables that can have two discrete values, 0 (False) and 1 (True); and operations that have logical significance. The earliest method of manipulating symbolic logic was invented by George Boole and subsequently came to be known as Boolean Algebra.
Boolean algebra has now become an indispensable tool in computer science for its wide applicability in switching theory, building basic electronic circuits and design of digital computers.
Boolean Functions
A Boolean function is a special kind of mathematical function $f: X^n \rightarrow X$ of degree n, where $X = \lbrace {0, 1} \rbrace$ is a Boolean domain and n is a nonnegative integer. It describes the way how to derive Boolean output from Boolean inputs.
Example − Let, $F(A, B) = A’B’$. This is a function of degree 2 from the set of ordered pairs of Boolean variables to the set $\lbrace {0, 1} \rbrace$ where $F(0, 0) = 1, F(0, 1) = 0, F(1, 0) = 0$ and $F(1, 1) = 0$
Boolean Expressions
A Boolean expression always produces a Boolean value. A Boolean expression is composed of a combination of the Boolean constants (True or False), Boolean variables and logical connectives. Each Boolean expression represents a Boolean function.
Example − $AB’C$ is a Boolean expression.
Boolean Identities
Double Complement Law
$\sim(\sim A) = A$
Complement Law
$A + \sim A = 1$ (OR Form)
$A . \sim A = 0$ (AND Form)
Idempotent Law
$A + A = A$ (OR Form)
$A . A = A$ (AND Form)
Identity Law
$A + 0 = A$ (OR Form)
$A . 1 = A$ (AND Form)
Dominance Law
$A + 1 = 1$ (OR Form)
$A . 0 = 0$ (AND Form)
Commutative Law
$A + B = B + A$ (OR Form)
$A. B = B . A$ (AND Form)
Associative Law
$A + (B + C) = (A + B) + C$ (OR Form)
$A. (B . C) = (A . B) . C$ (AND Form)
Absorption Law
$A. (A + B) = A$
$A + (A . B) = A$
Simplification Law
$A . (\sim A + B) = A . B$
$A + (\sim A . B) = A + B$
Distributive Law
$A + (B . C) = (A + B) . (A + C)$
$A . (B + C) = (A . B) + (A . C)$
DeMorgan's Law
$\sim (A . B) = \sim A + \sim B$
$\sim (A+ B) = \sim A . \sim B$
Canonical Forms
For a Boolean expression there are two kinds of canonical forms −
 The sum of minterms (SOM) form
 The product of maxterms (POM) form
The Sum of Minterms (SOM) or Sum of Products (SOP) form
A minterm is a product of all variables taken either in their direct or complemented form. Any Boolean function can be expressed as a sum of its 1minterms and the inverse of the function can be expressed as a sum of its 0minterms. Hence,
F (list of variables) = ∑ (list of 1minterm indices)
and
F' (list of variables) = ∑ (list of 0minterm indices)
A  B  C  Term  Minterm 

0  0  0  x’y’z’  m_{0} 
0  0  1  x’y’z  m_{1} 
0  1  0  x’yz’  m_{2} 
0  1  1  x’yz  m_{3} 
1  0  0  xy’z’  m_{4} 
1  0  1  xy’z  m_{5} 
1  1  0  xyz’  m_{6} 
1  1  1  xyz  m_{7} 
Example
Let, $F(x, y, z) = x' y' z' + x y' z + x y z' + x y z $
Or, $F(x, y, z) = m_0 + m_5 + m_6 + m_7$
Hence,
$F(x, y, z) = \sum (0, 5, 6, 7)$
Now we will find the complement of $F(x, y, z)$
$F' (x, y, z) = x' y z + x' y' z + x' y z' + x y' z'$
Or, $F'(x, y, z) = m_3 + m_1 + m_2 + m_4$
Hence,
$F'(x, y, z) = \sum (3, 1, 2, 4) = \sum (1, 2, 3, 4)$
The Product of Maxterms (POM) or Product of Sums (POS) form
A maxterm is addition of all variables taken either in their direct or complemented form. Any Boolean function can be expressed as a product of its 0maxterms and the inverse of the function can be expressed as a product of its 1maxterms. Hence,
F(list of variables) = $\pi$ (list of 0maxterm indices).
and
F'(list of variables) = $\pi$ (list of 1maxterm indices).
A  B  C  Term  Maxterm 

0  0  0  x + y + z  M_{0} 
0  0  1  x + y + z’  M_{1} 
0  1  0  x + y’ + z  M_{2} 
0  1  1  x + y’ + z’  M_{3} 
1  0  0  x’ + y + z  M_{4} 
1  0  1  x’ + y + z’  M_{5} 
1  1  0  x’ + y’ + z  M_{6} 
1  1  1  x’ + y’ + z’  M_{7} 
Example
Let $F(x, y, z) = (x + y + z) . (x+y+z') . (x+y'+z) . (x'+y+z)$
Or, $F(x, y, z) = M_0 . M_1 . M_2 . M_4$
Hence,
$F (x, y, z) = \pi (0, 1, 2, 4)$
$F''(x, y, z) = (x+y'+z') . (x'+y+z') . (x'+y'+z) . (x'+y'+z')$
Or, $F(x, y, z) = M_3 . M_5 . M_6 . M_7$
Hence,
$F '(x, y, z) = \pi (3, 5, 6, 7)$
Logic Gates
Boolean functions are implemented by using logic gates. The following are the logic gates −
NOT Gate
A NOT gate inverts a single bit input to a single bit of output.
A  ~A 

0  1 
1  0 
AND Gate
An AND gate is a logic gate that gives a high output only if all its inputs are high, otherwise it gives low output. A dot (.) is used to show the AND operation.
A  B  A.B 

0  0  0 
0  1  0 
1  0  0 
1  1  1 
OR Gate
An OR gate is a logic gate that gives high output if at least one of the inputs is high. A plus (+) is used to show the OR operation.
A  B  A+B 

0  0  0 
0  1  1 
1  0  1 
1  1  1 
NAND Gate
A NAND gate is a logic gate that gives a low output only if all its inputs are high, otherwise it gives high output.
A  B  ~(A.B) 

0  0  1 
0  1  1 
1  0  1 
1  1  0 
NOR Gate
An NOR gate is a logic gate that gives high output if both the inputs are low, otherwise it gives low output.
A  B  ~(A+B) 

0  0  1 
0  1  0 
1  0  0 
1  1  0 
XOR (Exclusive OR) Gate
An XOR gate is a logic gate that gives high output if the inputs are different, otherwise it gives low output.
A  B  A⊕B 

0  0  0 
0  1  1 
1  0  1 
1  1  0 
XNOR (Exclusive NOR) Gate
An EXNOR gate is a logic gate that gives high output if the inputs are same, otherwise it gives low output.
A  B  A XNOR B 

0  0  1 
0  1  0 
1  0  0 
1  1  1 
Simplification Of Boolean Functions
Simplification Using Algebraic Functions
In this approach, one Boolean expression is minimized into an equivalent expression by applying Boolean identities.
Problem 1
Minimize the following Boolean expression using Boolean identities −
$$F (A, B, C) = A'B + BC'+ BC + AB'C'$$
Solution
Given,$F (A, B, C) = A'B + BC'+ BC + AB'C'$
Or,$F (A, B, C) = A'B + (BC'+ BC') + BC+ AB'C'$
[By idempotent law, BC’ = BC’ + BC’]
Or,$F (A, B, C) = A'B + (BC'+ BC) + (BC'+ AB'C')$
Or,$F (A, B, C) = A'B + B(C'+ C) + C'(B+ AB')$
[By distributive laws]
Or,$F (A, B, C) = A'B + B.1 + C'(B + A)$
[ (C' + C) = 1 and absorption law (B + AB')= (B + A)]
Or,$F (A, B, C) = A'B + B + C'(B + A)$
[ B.1 = B ]
Or,$F (A, B, C) = B(A'+ 1) + C'(B + A)$
Or,$F (A, B, C) = B.1 + C'(B + A)$
[ (A' + 1) = 1 ]
Or,$F (A, B, C) = B + C'(B + A)$
[ As, B.1 = B ]
Or,$F (A, B, C) = B + BC' + AC'$
Or,$F (A, B, C) = B(1 + C') + AC'$
Or,$F (A, B, C) = B.1 + AC'$
[As, (1 + C') = 1]
Or,$F (A, B, C) = B + AC'$
[As, B.1 = B]
So,$F (A, B, C) = B + AC'$is the minimized form.
Problem 2
Minimize the following Boolean expression using Boolean identities −
$$F (A, B, C) = (A + B) (A + C)$$
Solution
Given, $F (A, B, C) = (A + B) (A + C)$
Or, $F (A, B, C) = A.A + A.C + B.A + B.C$ [Applying distributive Rule]
Or, $F (A, B, C) = A + A.C + B.A + B.C$ [Applying Idempotent Law]
Or, $F (A, B, C) = A(1 + C) + B.A + B.C$ [Applying distributive Law]
Or, $F (A, B, C) = A + B.A + B.C$ [Applying dominance Law]
Or, $F (A, B, C) = (A + 1).A + B.C$ [Applying distributive Law]
Or, $F (A, B, C) = 1.A + B.C$ [Applying dominance Law]
Or, $F (A, B, C) = A + B.C$ [Applying dominance Law]
So, $F (A, B, C) = A + BC$ is the minimized form.
Karnaugh Maps
The Karnaugh map (K–map), introduced by Maurice Karnaughin in 1953, is a gridlike representation of a truth table which is used to simplify boolean algebra expressions. A Karnaugh map has zero and one entries at different positions. It provides grouping together Boolean expressions with common factors and eliminates unwanted variables from the expression. In a Kmap, crossing a vertical or horizontal cell boundary is always a change of only one variable.
Example 1
An arbitrary truth table is taken below −
A  B  A operation B 

0  0  w 
0  1  x 
1  0  y 
1  1  z 
Now we will make a kmap for the above truth table −
Example 2
Now we will make a Kmap for the expression − AB+ A’B’
Simplification Using Kmap
Kmap uses some rules for the simplification of Boolean expressions by combining together adjacent cells into single term. The rules are described below −
Rule 1 − Any cell containing a zero cannot be grouped.
Wrong grouping
Rule 2 − Groups must contain 2n cells (n starting from 1).
Wrong grouping
Rule 3 − Grouping must be horizontal or vertical, but must not be diagonal.
Wrong diagonal grouping
Proper vertical grouping
Proper horizontal grouping
Rule 4 − Groups must be covered as largely as possible.
Insufficient grouping
Proper grouping
Rule 5 − If 1 of any cell cannot be grouped with any other cell, it will act as a group itself.
Proper grouping
Rule 6 − Groups may overlap but there should be as few groups as possible.
Proper grouping
Rule 7 − The leftmost cell/cells can be grouped with the rightmost cell/cells and the topmost cell/cells can be grouped with the bottommost cell/cells.
Proper grouping
Problem
Minimize the following Boolean expression using Kmap −
$$F (A, B, C) = A'BC + A'BC' + AB'C'+ AB'C$$
Solution
Each term is put into kmap and we get the following −
Kmap for F (A, B, C)
Now we will group the cells of 1 according to the rules stated above −
Kmap for F (A, B, C)
We have got two groups which are termed as $A’B$ and $AB’$. Hence, $F (A, B, C) = A’B+ AB’= A \oplus B$. It is the minimized form.