
- Discrete Mathematics - Home
- Discrete Mathematics Introduction
- Mathematical Statements and Operations
- Atomic and Molecular Statements
- Implications
- Predicates and Quantifiers
- Sets
- Sets and Notations
- Relations
- Operations on Sets
- Venn Diagrams on Sets
- Functions
- Surjection and Bijection Functions
- Image and Inverse-Image
- Mathematical Logic
- Propositional Logic
- Logical Equivalence
- Deductions
- Predicate Logic
- Proof by Contrapositive
- Proof by Contradiction
- Proof by Cases
- Rules of Inference
- Group Theory
- Operators & Postulates
- Group Theory
- Algebric Structure for Groups
- Abelian Group
- Semi Group
- Monoid
- Rings and Subring
- Properties of Rings
- Integral Domain
- Fields
- Counting & Probability
- Counting Theory
- Combinatorics
- Additive and Multiplicative Principles
- Counting with Sets
- Inclusion and Exclusion
- Bit Strings
- Lattice Path
- Binomial Coefficients
- Pascal's Triangle
- Permutations and Combinations
- Pigeonhole Principle
- Probability Theory
- Probability
- Sample Space, Outcomes, Events
- Conditional Probability and Independence
- Random Variables in Probability Theory
- Distribution Functions in Probability Theory
- Variance and Standard Deviation
- Mathematical & Recurrence
- Mathematical Induction
- Formalizing Proofs for Mathematical Induction
- Strong and Weak Induction
- Recurrence Relation
- Linear Recurrence Relations
- Non-Homogeneous Recurrence Relations
- Solving Recurrence Relations
- Master's Theorem
- Generating Functions
- Graph Theory
- Graph & Graph Models
- More on Graphs
- Planar Graphs
- Non-Planar Graphs
- Polyhedra
- Introduction to Trees
- Properties of Trees
- Rooted and Unrooted Trees
- Spanning Trees
- Graph Coloring
- Coloring Theory in General
- Coloring Edges
- Euler Paths and Circuits
- Hamiltonion Path
- Boolean Algebra
- Boolean Expressions & Functions
- Simplification of Boolean Functions
- Advanced Topics
- Number Theory
- Divisibility
- Remainder Classes
- Properties of Congruence
- Solving Linear Diophantine Equation
- Useful Resources
- Quick Guide
- Useful Resources
- Discussion
Discrete Mathematics - Introduction
Discrete mathematics is a branch of mathematics that deals with objects that can assume only distinct values. This is in contrast to continuous mathematics that focuses on continuous, smooth functions. Discrete mathematics shows structures that are fundamentally discrete. The term "discrete" refers to the fact that these structures can be counted in whole numbers, unlike continuous structures that can take on any value within a range.
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.
Importance of Discrete Mathematics
There are several reasons why discrete mathematics plays an important role in computer science. Let us analyze some of the important reasons in this section.
Foundation for Programming and Algorithms
Discrete mathematics provides the logical foundation for many programming concepts:
- Boolean Logic − Used in conditional statements and logical operations.
- Set Theory − Applied in database management and data structures.
- Graph Theory − Utilized in network design and pathfinding algorithms.
- Combinatorics − Essential for analyzing algorithm efficiency and complexity.
Data Structures and Algorithms
Many data structures and algorithms are based on the concepts of discrete mathematics:
- Trees and Graphs − Used in file systems, network routing, and social network analysis.
- Hash Tables − Based on discrete math principles for efficient data retrieval.
- Sorting and Searching Algorithms − Rely on discrete math for their design and analysis.
Cryptography and Security
Discrete mathematics plays a fundamental role in cryptography and computer security:
- Number Theory − Used in public-key cryptography systems like RSA.
- Finite Fields − Applied in many encryption algorithms.
- Combinatorics − Used in designing secure communication protocols.
Artificial Intelligence and Machine Learning
Discrete mathematics finds its application in advanced concepts like AI and ML too.
- Graph Theory − Used in neural networks and decision trees.
- Probability Theory − Essential for statistical learning algorithms.
- Logic − Applied in knowledge representation and reasoning systems.
Computer Architecture and Digital Logic
Discrete mathematics forms the basis of computer hardware design:
- Boolean Algebra − Used in designing logic circuits.
- Number Systems − Binary, octal, and hexadecimal systems are fundamental to computer architecture.
Key Concepts in Discrete Mathematics
Let us see some key concepts in discrete mathematics that are particularly relevant to Computer Science −
Set Theory
Sets are collections of distinct objects. In computer science, sets are used to model data structures and databases. For example,
A = {1, 2, 3, 4, 5}
B = {2, 4, 6, 8, 10}
Intersection − A ∩ B = {2, 4}
Union − A ∪ B = {1, 2, 3, 4, 5, 6, 8, 10}
Logic
Logic works with true/false statements and is fundamental to programming. Take a look at the following example:
- p − "It is raining"
- q − "The ground is wet"
- p → q − "If it is raining, then the ground is wet"
Graph Theory
The graphs consist of vertices (nodes) and edges (connections). They are used to model networks, relationships, and more. For example,
- V = {A, B, C, D}
- E = {(A,B), (B,C), (C,D), (D,A)}
This defines a simple cycle graph with 4 vertices.
Combinatorics
Combinatorics works with counting and arranging objects. And this is used for algorithm analysis. For example, the number of ways to arrange n distinct objects is n! (n factorial).
For 5 objects: 5! = 5 × 4 × 3 × 2 × 1 = 120 arrangements.
Number Theory
Number theory focuses on the properties of integers. It is essential for cryptography. For example, the Euclidean algorithm for finding the greatest common divisor (GCD):
- GCD(48, 18):
- 48 = 2 × 18 + 12
- 18 = 1 × 12 + 6
- 12 = 2 × 6 + 0
- GCD(48, 18) = 6
We will discuss each of these concepts in the subsequent chapters of this tutorial.
Conclusion
In this chapter, we touched upon the fundamental concepts of discrete mathematics and its crucial importance in computer science. We explained how discrete mathematics provides the foundation for various areas of computer science, including programming, data structures, algorithms, cryptography, and artificial intelligence.
In addition, we provided a brief overview of some key concepts like set theory, logic, graph theory, combinatorics, and number theory, providing examples for each. In the subsequent chapters of this tutorial, we will elaborate these concepts in detail.