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An Algorithm is a sequence of steps to solve a problem. Design and Analysis of Algorithm is very important for designing algorithm to solve different types of problems in the branch of computer science and information technology. This tutorial introduces the fundamental concepts of Designing Strategies, Complexity analysis of Algorithms, followed by problems on Graph Theory and Sorting methods. This tutorial also includes the basic concepts on Complexity theory.

Audience

This tutorial has been designed for students pursuing a degree in any computer science, engineering, and/or information technology related fields. It attempts to help students to grasp the essential concepts involved in algorithm design.

Prerequisites

The readers should have basic knowledge of programming and mathematics. The readers should know data structure very well. Moreover, it is preferred if the readers have basic understanding of Formal Language and Automata Theory.

Chapters 

Basics of Algorithms

DAA - Introduction

DAA - Analysis of Algorithms

DAA - Methodology of Analysis

Asymptotic Notations & Apriori Analysis

DAA - Space Complexities

Design Strategies

DAA - Divide & Conquer

DAA - Max-Min Problem

DAA - Merge Sort

DAA - Binary Search

Strassen’s Matrix Multiplication

DAA - Greedy Method

DAA - Fractional Knapsack

DAA - Job Sequencing with Deadline

DAA - Optimal Merge Pattern

DAA - Dynamic Programming

DAA - 0-1 Knapsack

Longest Common Subsequence

Graph Theory

DAA - Spanning Tree

DAA - Shortest Paths

DAA - Multistage Graph

Travelling Salesman Problem

Optimal Cost Binary Search Trees

Heap Algorithms

DAA - Binary Heap

DAA - Insert Method

DAA - Heapify Method

DAA - Extract Method

Sorting Methods

DAA - Bubble Sort

DAA - Insertion Sort

DAA - Selection Sort

DAA - Quick Sort

DAA - Radix Sort

Complexity Theory

Deterministic vs. Nondeterministic Computations

DAA - Max Cliques

DAA - Vertex Cover

DAA - P and NP Class

DAA - Cook’s Theorem

NP Hard & NP-Complete Classes

DAA - Hill Climbing Algorithm

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