Here is the listing of Best reference books on Algorithms.
|1. “Introduction to Algorithms” by T. H. Cormen, C. L. Leiserson, R. L. Rivest, and C. Stein
Book Review: This book contains a number of algorithms and provides in-depth analysis and design of those algorithms which is well understood by readers at all levels. In addition, the algorithms also contain pseudo code which is easily understood by the readers. There are chapters which cover new topics like linear programming, randomized algorithms, probabilistic analysis and multithreaded algorithms. Improvised algorithms on Dynamic Programming and Greedy Techniques are also included in the book. The book also contains special algorithms on string matching, computational geometry and number theory.
|2. “Algorithm Design” by J. Kleinberg and E. Tardos
Book Review: This book contains several methods which identify the problems across a range of computing applications, develops algorithm design techniques for those problems and identifies efficient solutions to those problems. The book establishes a relationship between the algorithmic ideas and formulated problems for which algorithmic design and analysis takes place. The book also offers insight on identifying the problems in complex situations from different computing areas thereby designing efficient algorithms for those problems.
|3. “Data Structures and Their Algorithms” by Harry R. Lewis and Larry Denenberg|
|4. “Algorithmic Graph Theory” by A. Gibbons
Book Review: The book contains the concepts of pure and applied graph theory that include spanning trees, network flow, connectivity, traversals and matching. It also covers a range of classical algorithms thereby focusing on algorithms and their complexity. The algorithms are categorized based on the ones which are intractable and the ones which have known efficient solutions. Efficient approximation algorithms are being given for intractable problems. Programming languages like Pascal are used to describe algorithmic problems.
|5. “Algorithm Design: Foundations, Analysis and Internet Examples” by Michael T. Goodrich and Roberto Tamassia
Book Review: The book deals with the design and analysis of algorithms and data structures. The various topics included in the book are algorithmic design patterns and frameworks, graph and combinatorial algorithms, numerical algorithms, internet algorithms and geometric algorithms. The book also provides practical and theoretical techniques as well as experimental methods for the engineering of algorithms.
|6. “The Algorithm Design Manual” by Steven S Skiena|
|7. “Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles” by Narasimha Karumanchi|
|8. “Grokking Algorithms: An illustrated guide for programmers and other curious people” by Aditya Bhargava|
|9. ” Algorithms in a Nutshell: A Practical Guide” by George T. Heineman and Gary Pollice|
|10. ” Cracking the Coding Interview: 189 Programming Questions and Solutions” by Gayle Laakmann McDowell|
|11. “Randomized Algorithms” by Rajeev Motwani and Prabhakar Raghavan
Book Review: This book provides an introduction of the basic concepts and theories that is needed for the design and analysis of randomized algorithms. The book provides algorithmic examples to illustrate the use of tools in concrete setting. Each chapter in the book focuses on the essential area to which randomized algorithms can be applied thereby providing a comprehensive and representative selection of algorithms which can be used in those areas. This book is very useful for graduate and undergraduate students and is a very good reference book for researchers and professionals.
|12. “Online Computation and Competitive Analysis” by Allan Borodin and Ran El-Yaniv
Book Review: The book provides detailed presentation of competitive analysis with the help of an attractive framework in which the analysis and solving of problems is done. The algorithm quality is analysed within the framework and is measured in relation with the best possible performance of an algorithm. The book starts with basic definitions of competitive analysis model along with the description of important techniques through many examples. These examples include list accessing, paging in virtual memory system, routing and load balancing in network systems. The book also contains various concepts of game theory and decision theory. This book is useful for researchers and graduate students in the field of operations research.
|13. “Distributed Algorithms” by Nancy Lynch
Book Review: This book deals with the designing, implementation and analysis of distributed algorithms. The book contains important algorithms which are proved correct and have their complexity analysed according to the defined complexity measures. The various problems covered in the book include resource allocation, distributed process consensus, data consistency, deadlock detection and many others. The book also deals with important algorithms, problems and results. The provides the readers with mathematical tools for new algorithm design and results. The book also provides tactics to carefully reason the algorithms, provide specifications for their behavior, prove their correctness and measure their performance.
|14. “Data Structures and Network Algorithms” by Robert Endre Tarjan
Book Review: This book presents the rapid growth that is taking place in the field of combinatorial algorithms. The combinatorial algorithms are based on not only the outcomes of combinatorics and graph theory, but also on the development of new data structures and technologies for analyzing algorithms. The book covers four network optimization problems which includes development of the data structures they use as well as the analysis of their running time. The book provides the readers with the practical understanding of the concepts of data structures and network algorithms thereby facilitating easy implementation and an appreciation of the depth and beauty of the field of graph algorithms.
|15. “An Introduction to Computational Learning Theory” by M. Kearns and U. Vazirani
Book Review: The book presents various topics in the field of theory of computational learning which is very useful for researchers and students in the field of neural networks, artificial intelligence, statistics and computer science theory. The book examines the formal models of induction thereby inventing common methods that lie below efficient learning algorithms thereby identifying the computational impediments to learning. The book also presents new proofs of established theorems and presentations of standard proofs. Various other topics covered in the book are motivation, definitions and basic results which can be either positive or negative. The book also presents the equivalence of strong and weak learning, relationship between learning and cryptography, limitations of efficient learning and many algorithms for finite automata learning.
|16. ” Cracking the C, C++, and Java Interview ” by S G Ganesh, Subhash K U|
Sanfoundry Global Education & Learning Series – Best Reference Books!