Algorithms
Design and Analysis
Price: 1300.00 INR
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ISBN:
9780199456666
Publication date:
29/10/2015
Paperback
768 pages
Price: 1300.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780199456666
Publication date:
29/10/2015
Paperback
768 pages
Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. It helps the students to understand the fundamentals and applications of algorithms. The book will serve as a useful reference for researchers and practising programmers in the field of algorithm designing. It is also indented for students preparing for interviews and competitive examinations.
Suitable for: Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications.
Rights: World Rights
Description
Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. It helps the students to understand the fundamentals and applications of algorithms. The book will serve as a useful reference for researchers and practising programmers in the field of algorithm designing. It is also indented for students preparing for interviews and competitive examinations. The book has been divided into four sections: Algorithm Basics, Data Structures, Design Techniques and Advanced Topics. The first section explains the importance of algorithms, growth of functions, recursion and analysis of algorithms. The second section covers the data structures basics, trees, graphs, sorting in linear and quadratic time. Section three discusses the various design techniques namely, divide and conquer, greedy approach, dynamic approach, backtracking, branch and bound and randomized algorithms used for solving problems in detail in separate chapters. The fourth section includes the advanced topics such as transform and conquer, decrease and conquer, number thoeretics, string matching, computational geometry, complexity classes, approximation algorithms, and parallel algorithms. Finally, the applications of algorithms in Machine Learning and Computational Biology areas are dealt with in the subsequent chapters. This section will be useful for those interested in advanced courses in algorithms. Appendixes of the book include topics such as probability, matrix operations, Red-black tress, linear programming, DFT, scheduling, a reprise of sorting, searching and amortized analysis, and problems based on writing algorithms. The concepts and algorithms in the book are explained with the help of examples which are solved using more than one method for better understanding. Each chapter of the book includes a variety of end-chapter exercises in the form of MCQs with answers, review questions, and programming exercises to help readers test their knowledge.
Table of contents
Chapter 1: Introduction to Algorithms Chapter 2: Growth of Functions Chapter 3: Recursion Chapter 4: Analysis of Algorithms Chapter 5: Basic Data Structures Chapter 6: Trees Chapter 7: Graphs Chapter 8: Sorting in Linear and Quadratic Time Chapter 9: Divide and Conquer Chapter 10: Greedy Algorithms Chapter 11: Dynamic Programmin Chapter 12: Backtracking Chapter 13: Branch and Bound Chapter 14: An Introduction to Randomized Algorithms Chapter 15: Transform and Conquer Chapter 16: Decrease and Conquer Chapter 17: Number Theoretic Algorithms Chapter 18: String Matching Chapter 19: Complexity Classes Chapter 20: An Introduction to PSpace Chapter 21: Approximation Algorithms Chapter 22: Parallel Algorithms Chapter 23: An Introduction to Machine Learning Approaches Chapter 24: Computational Biology and Bioinformatics
Features
• Offers in-depth treatment of topics such as complexity analysis, design paradigms, data structures, and machine learning algorithms. • Introduces topics like Decrease and Conquer, Transform and Conquer and PSpace along with standards paradigms. • Explains numerical methods including Euclid's theorem and Chinese Remainder Theorem and also reviews essential mathematical concepts • Provides points-to-remember and a list of key terms at the end of each chapter which will help readers to quickly recollect important concepts. • Exercises given at the end of each chapter and in the Appendix 10 would help students prepare for their examinations and job interviews. Online resources For Faculty: • PowerPoint Presentations • Solution Manual for chapter-end problems • Assignment Questions with answers For Students: • Additional MCQs for Test Generator (with answers) for each chapter • C language implementation of algorithms • Interview Questions with answers
Description
Algorithms: Design and Analysis of is a textbook designed for the undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. It helps the students to understand the fundamentals and applications of algorithms. The book will serve as a useful reference for researchers and practising programmers in the field of algorithm designing. It is also indented for students preparing for interviews and competitive examinations. The book has been divided into four sections: Algorithm Basics, Data Structures, Design Techniques and Advanced Topics. The first section explains the importance of algorithms, growth of functions, recursion and analysis of algorithms. The second section covers the data structures basics, trees, graphs, sorting in linear and quadratic time. Section three discusses the various design techniques namely, divide and conquer, greedy approach, dynamic approach, backtracking, branch and bound and randomized algorithms used for solving problems in detail in separate chapters. The fourth section includes the advanced topics such as transform and conquer, decrease and conquer, number thoeretics, string matching, computational geometry, complexity classes, approximation algorithms, and parallel algorithms. Finally, the applications of algorithms in Machine Learning and Computational Biology areas are dealt with in the subsequent chapters. This section will be useful for those interested in advanced courses in algorithms. Appendixes of the book include topics such as probability, matrix operations, Red-black tress, linear programming, DFT, scheduling, a reprise of sorting, searching and amortized analysis, and problems based on writing algorithms. The concepts and algorithms in the book are explained with the help of examples which are solved using more than one method for better understanding. Each chapter of the book includes a variety of end-chapter exercises in the form of MCQs with answers, review questions, and programming exercises to help readers test their knowledge.
Read MoreTable of contents
Chapter 1: Introduction to Algorithms Chapter 2: Growth of Functions Chapter 3: Recursion Chapter 4: Analysis of Algorithms Chapter 5: Basic Data Structures Chapter 6: Trees Chapter 7: Graphs Chapter 8: Sorting in Linear and Quadratic Time Chapter 9: Divide and Conquer Chapter 10: Greedy Algorithms Chapter 11: Dynamic Programmin Chapter 12: Backtracking Chapter 13: Branch and Bound Chapter 14: An Introduction to Randomized Algorithms Chapter 15: Transform and Conquer Chapter 16: Decrease and Conquer Chapter 17: Number Theoretic Algorithms Chapter 18: String Matching Chapter 19: Complexity Classes Chapter 20: An Introduction to PSpace Chapter 21: Approximation Algorithms Chapter 22: Parallel Algorithms Chapter 23: An Introduction to Machine Learning Approaches Chapter 24: Computational Biology and Bioinformatics
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