Some books on algorithms are thorough but fall short; others are dense but fall short. The combination of rigor and thoroughness in Introduction to Algorithms is unmatched. With self-contained chapters and algorithms in pseudocode, it covers a wide range of algorithms in depth while making their design and analysis understandable to readers of all reading levels.
The book Computer Vision: Algorithms and Applications examines the various methods frequently employed for picture analysis and interpretation. It also discusses difficult real-world uses of vision that are successful, including specialist applications like medical imaging and enjoyable, consumer-level activities like picture editing and stitching that students may use on their own images and films.
List of contents Case Study Chapter Code of Origin: Errata: found a problem? You should have a clear understanding of the caliber and tone of our book after reading the sample chapter. Make sure you are familiar with the level and our Python coding style in particular. This book focuses on providing answers to challenging data structure and algorithmic difficulties.
Efficient algorithms must be used to create durable software, but programmers rarely consider them until a problem arises. With just enough arithmetic to enable you to understand and analyze algorithm performance, this revised edition of Algorithms in a Nutshell presents a wide number of existing algorithms for addressing a range of problems and aids you in choosing and implementing the best algorithm for your purposes.
Have you ever questioned how your GPS can choose the quickest route from what seems like an infinite number of options in a matter of seconds? When you make an online transaction, how is your credit card account number protected? The solution is an algorithm.
The book Computer Vision: Principles, Algorithms, Applications, Learning (previously Computer and Machine Vision) presents the fundamental methodology of computer vision in a clear and organized manner, covering the key concepts while putting an emphasis on algorithmic and real-world design constraints.
a primer emphasizing the fundamental principles of algorithms that underpin developments in bioinformatics. The algorithmic concepts that underpin developments in bioinformatics are succinctly explained in this introductory text. It maintains a unique balance between rigorous mathematics and useful strategies, emphasizing the ideas underlying algorithms rather than providing a collection of seemingly unrelated tasks. It is accessible to students in biology and computer science.
This widely recognized textbook offers a thorough introduction to the current field of computer algorithm research. It provides in-depth coverage of a wide variety of algorithms while keeping the design and analysis understandable to readers of all skill levels. Every chapter covers an algorithm, a design method, an application domain, or a related subject and is mostly self-contained. Anybody with some programming experience may understand the descriptions and designs of the algorithms. The explanations have been kept simple without compromising mathematical rigor or depth of material.
The fundamental data structures and programming methods that are frequently employed in effective algorithms are introduced in The Design and Analysis of Computer Algorithms. The usage of lists, queues, push-down stacks, trees, and graphs are all covered. This literature helps you comprehend the basic ideas behind algorithms, which are at the core of computer science. It presents the fundamental programming concepts and data structures that are frequently seen in effective algorithms. includes usage of queues, trees, graphs, push-down stacks, and lists. Subsequent chapters cover the Schonhage-Strassen integer multiplication method, string-matching algorithms, and algorithms for sorting, searching, and graphing. Several graded exercises are provided at the conclusion of every chapter.
Lafore has updated every program in this Second Edition to enhance its functionality, make the algorithms it demonstrates more understandable, and take use of the most recent Java 2 SDK releases. Additionally, Lafore has included fresh activities and questions at the conclusion of each chapter. As a result, students acquire a thorough understanding of the methods and best practices used today to manipulate almost any type of data using Java.
The theoretical and algorithmic approaches to structural dynamics are discussed in this book. Systems with one or more degrees of freedom are covered. To provide the reader a better understanding of the practicalities of the field, numerous case studies are provided along with solutions that are expressed in terms of real-time and frequency in both geometric and modal spaces. Seismic loading is another topic that is emphasized. Since the material is based on multiple lectures on structural dynamics given at various universities, it will be an easily understood and useful tool for students studying the subject.
The book is intended to be used as a textbook for a single semester course taken by postgraduate students studying computer applications at Rajiv Gandhi Proudyogiki Vishwavidyalaya and undergraduate students studying computer science and engineering and information technology. This extremely structured and ordered text presents algorithm design strategies in an easy-to-understand manner. To help with a better knowledge of algorithms, it explains the entire creation of a variety of algorithms along with their self-explanatory pseudocodes backed by functional examples.
Although there aren't many students enrolled in VLSI design automation classes, it's a highly sought-after elective, particularly for individuals hoping to work in the microelectronics sector. Because the book covers every stage of the design process, reviewers are already gushing about how well it matches the material in their courses compared to competitors. It includes a significant number of solved questions and pictures. It's a solid "list-builder" title that fits in with our approach of emphasizing subjects that are in the intersection of computer science and electrical engineering.
How to really implement data structures and algorithms is a topic that is frequently ignored, yet it is covered in this article. The term "Algorithm Engineering" refers to the authors' methodology, which emphasizes that creating and utilizing algorithms requires more than simply algorithm theory. It also incorporates software usability and robustness difficulties, as well as engineering design concepts like object-orient design patterns and abstract data types.
The primary objective of this book is to provide a contemporary method from the perspective of implementation. There are practical programming exercises for key methods all throughout. Background information on information and decision theory, as well as the new iteratively-decode codes (such as Turbo codes) and classical coding techniques (such as Reed Solomon codes and trellis codes) are all presented in the book.
This book is perfect for an introductory data structures and algorithms course since it has information and a style that are up to date with modern programming. It only requires knowledge of a high-level programming language, such as Pascal. The book is a thorough text for both graduate and undergraduate students, covering a wide range of topics in a unified manner.
The author of this best-selling book has improved and refined his creative approach to algorithms and data structures in the second edition. He illustrates fundamental themes with a C implementation, emphasizing ADTs and the examination of algorithms for efficiency, performance, and running time.
By applying the "survival of the fittest" principle, genetic algorithms enable engineers to optimize their designs by simulating the natural process of evolution. Genetic algorithms are particularly well-suited for VLSI, and this thorough book explains how to achieve the optimum outcomes. You will learn how to use genetic algorithms to a wide range of test automation, layout, and VLSI design jobs.
The basic information structures and programming techniques that are frequently employed in effective algorithms are introduced in The Design and Analysis of Computer Algorithms. The usage of lists, queues, push-down stacks, trees, and graphs are all covered. This literature helps you comprehend the basic ideas behind algorithms, which are at the core of computer science. It covers basic programming concepts and data structures that are frequently seen in efficient algorithms. includes usage of queues, trees, graphs, push-down stacks, and lists. Subsequent chapters cover the Schonhage-Strassen integer multiplication method, string-matching algorithms, and algorithms for sorting, searching, and graphing. Several graded activities are provided at the end of every chapter.
The foundation of many significant fields of contemporary technology, including bioinformatics, computational neuroscience, communication, signal processing, data mining, machine learning, pattern recognition, and cryptography, is information theory and inference, which are taught together in this fascinating textbook. The theory is introduced in the book along with some applications. Practical communication systems like arithmetic coding for data compression and sparse-graph codes for error-correction are taught alongside information theory. Applications to neural networks, convolutional codes, clustering, independent component analysis, and so on are developed alongside inference techniques, such as variational approximations, Monte Carlo methods, and message-passing algorithms. The book is unique in that it covers the most recent error-correcting codes, such as digital fountain codes, turbo codes, and low-density-parity-check codes, which are the standards for disk drives, satellite communications, and data broadcast in the twenty-first century. The book is well-illustrated, has over 400 exercises with detailed solutions, worked examples, and is perfect for both undergraduate and graduate courses as well as self-study. Additionally, it offers professionals in fields as diverse as computational biology, financial engineering, and machine learning an unmatched entry point.
