Название: The fundamentals of algorithmic processesАвтор: Sourabh Pal
Издательство: Arcler Press
Год: 2023
Страниц: 271
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
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.
This book provides a comprehensive summary of contemporary research on computer algorithms. The book contains a detailed discussion of several sorts of computer algorithms. The information on algorithm creation and analysis is meant for a broad audience. A fundamental understanding of programming languages and mathematics are required to conduct algorithmic analysis. Knowledge of algorithms enables us to focus on the difficult task of solving a particular problem, rather than on the technical aspects of instructing a computer to perform a particular task. The purpose of this book on algorithms and data structures is to acquaint readers with the theoretical underpinnings of the abilities required to develop computer programs and algorithms. This book is an attempt to familiarize readers with a variety of related fields, such as algorithmic complexity and computability, which should be studied in conjunction with developing applied programming skills.
Typically, the book is comprised of self-contained material that demonstrates a comprehensive understanding of core programming and mathematical concepts. The book’s fundamentals are predicated on the introduction of algorithms and data structures in relation to various algorithmic issues. The book explores the many sorts of algorithms available for issue solving. I believe in associate learning, which entails associating one subject with another, i.e., one subject leads to another, and so on. This book comprises topics that are inextricably related. The intention was not to create a comprehensive compendium of everything known about algorithms, but rather to provide a collection of fundamental ingredients and key building blocks upon which algorithms can be built.
Grasp algorithmic challenges require a solid understanding of the underlying concepts of algorithms and data structures. Chapter 1 of the book provides a thorough overview of the fundamental concepts of algorithms and data structures. Algorithms of many types are being researched at the moment. Chapter 2 discusses the categorization of many sorts of algorithms. At the moment, substantial research is being conducted on the subject of search algorithms. Chapters 3 and 4 offer in-depth discussions of complex algorithms such as algorithmic search and quantum walk. On the other hand, Chapter 5 discusses the essential ideas and forms of heuristic algorithms.
In the real-time environment, several algorithms are used to tackle various types of issues. For instance, machine learning algorithms are used to investigate the problems of supervised and unsupervised learning, as discussed in Chapter 6. Chapter 7 discusses the fundamentals of approximation algorithms. Every field of science and technology is governed by some rules. Similarly, algorithmic systems are also governed by certain rules and regulations. Chapter 8 discusses the analytical framework of algorithmic governance and the potential risks and limitations associated with the algorithms and their applicability.
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