As there can be more than one algorithm for the same problem, designing and analyzing an algorithm becomes important in order to make it as efficient and robust as possible. This book will serve as a guide to design and analysis of computer algorithms. Chapter One provides an overview of different algorithm design techniques and the various applications of such techniques. Chapter Two reviews the divide and conquer strategy and the algorithm types that employ it. Chapter Three explores greedy algorithms and some problems that can be solved with this approach. Chapter Four discusses in depth the dynamic programming approach. Chapter Five provides a solution to the N-Queens problem utilizing a backtracking approach. Chapter Six elucidates the reader to branch and bound techniques and provides three solutions to problems implementing them. Part II of this book begins with Chapter Seven, where two different approaches to the analysis of algorithms are discussed. Chapter Eight reviews randomized algorithms through an empirical lens. Chapter Nine discusses Master Theorem and the many kinds of problems this Theorem can solve. Chapter Ten, the final chapter, provides notes on the empirical complexity analysis of algorithms.
Author(s): Soubhik Chakraborty, Prashant Pranav, Naghma Khatoon, Sandip Dutta
Series: Computer Science, Technology and Applications
Publisher: Nova Science Publishers
Year: 2022
Language: English
Pages: 122
City: New York
Contents
Preface
Chapter 1
Introduction to the Design of Algorithms
1.1. Brute Force Approach
1.2. Divide and Conquer
1.3. Greedy Technique
1.4. Dynamic Programming
1.5. Branch and Bound
1.6. Randomized Algorithms
1.7. Backtracking Algorithm
Chapter 2
Divide and Conquer
2.1. Recurrence Relation
2.2. Binary Search
2.3. Merge Sort
Chapter 3
Greedy Algorithms
3.1. Job Sequencing Problem with Deadline
3.2. Dijkstra Algorithm
Chapter 4
Dynamic Programming
4.1. Stagecoach Problem
4.2. Optimal Binary Search Tree (Optimal BST)
4.3. Subset Sum Problem
4.4. 0/1 Knapsack Problem
Chapter 5
Backtracking
5.1. N – Queens Problem
Chapter 6
Branch and Bound
6.1. Assignment Problem
6.1.1. Branch and Bound Technique to Solve Assignment Problem
6.2. O/1 Knapsack Problem
6.3. Travelling Salesman Problem
Chapter 7
Introduction to the Analysis of Algorithms
7.1. Asymptotic Analysis
7.1.1. Big – Oh
7.1.2. Big - Omega
7.1.3. Theta
7.2. Empirical Analysis of Computer Algorithms: Why Statistics?
7.2.1. Computer Experiments and Algorithmic Complexity
7.2.2. Statistical (Complexity) Bound (Definition)
Chapter 8
Randomized Algorithms
8.1. Randomized Quick Sort
8.2. Randomized Binary Search
Chapter 9
Master Theorem
9.1. Master Theorem for Decreasing Function
9.2. Limitations of Master Theorem
Chapter 10
A Note on Empirical Complexity Analysis
10.1. The Fundamental Theorem of Finite Difference
10.2. Empirical Complexity of Merge Sort
10.3. Empirical Complexity of Quick Sort
10.4. Empirical Complexity of Bubble Sort
10.5. Empirical Complexity of Selection Sort
References
About the Authors
Index
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