This book focuses on the creative elements of algorithmic design by delving into the phases involved in algorithm creation. The conceptual underpinnings of this creative process are analogous to the invention and development of mathematical theorems that result in the induction of combinatorial algorithms. Numerous issue examples are included in the book. It is intended to enhance readers' problem-solving abilities by imparting a grasp of the fundamental ideas behind algorithmic design. This book summarizes the most widely used computer algorithms and provides a comprehensive overview of algorithms and data structures for searching, sorting, and graph processing. The algorithms presented in this book represent a body of knowledge that has developed over the previous five decades and has become critical not just for professional programmers and computer scientists, but also for all students interested in mathematics, data science, and engineering. The reader of this book is meant to get knowledge about known ways for successfully resolving difficulties. They will become acquainted with various cutting-edge data structures and novel methods for utilizing data structures to improve the efficacy of algorithms. Because the book is virtually self-contained, it may be used as a course book, reference book, or self-study resource.
Author(s): Sourabh Pal
Publisher: Arcler
Year: 2023
Language: English
Pages: 271
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Fundamentals of Algorithms
1.1. Introduction
1.2. Various Types of Issues Solved by Algorithms
1.3. Data Structures
1.4. Algorithms Like a Technology
1.5. Algorithms and Other Technologies
1.6. Getting Started with Algorithm
1.7. Analyzing Algorithms
References
Chapter 2 Classification of Algorithms
2.1. Introduction
2.2. Deterministic and Randomized Algorithms
2.3. Online Vs. Offline Algorithms
2.4. Exact, Approximate, Heuristic, and Operational Algorithms
2.5. Classification According to the Main Concept
References
Chapter 3 Fundamentals of Search Algorithms
3.1. Introduction
3.2. Unordered Linear Search
3.3. Ordered Linear Search
3.4. Chunk Search
3.5. Binary Search
3.6. Searching In Graphs
3.7. Graph Grep
3.8. Searching in Trees
3.9. Searching in Temporal Probabilistic Object Data Model
References
Chapter 4 Algorithmic Search via Quantum Walk
4.1. Introduction
4.2. Quantum Walk
4.3. Search Algorithm Via Quantum Walk
4.4. The Physical Implementation of Quantum Walk Based Search
4.5. Quantum Walk-Based Search in Nature
4.6. Biomimetic Application in Solar Energy
References
Chapter 5 An Introduction to Heuristic Algorithms
5.1. Introduction
5.2. Algorithms and Complexity
5.3. Heuristic Techniques
5.4. Evolutionary Algorithms (EAS)
5.5. Support Vector Machines (SVMS)
5.6. Current Trends
References
Chapter 6 Machine Learning Algorithms
6.1. Introduction
6.2. Supervised Learning Approach
6.3. Unsupervised Learning
6.4. Algorithm Types
References
Chapter 7 Approximation Algorithms
7.1. Introduction
7.2. Approximation Strategies
7.3. The Greedy Method
7.4. Sequential Algorithms
7.5. Randomization
References
Chapter 8 Governance of Algorithms
8.1. Introduction
8.2. Analytical Framework
8.3. Governance Options By Risks
8.4. Limitations of Algorithmic Governance Options
References
Index
Back Cover
