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Thomas H. Cormen
Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called "Divide-and-Conquer"), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. As of the third edition, this textbook is published exclusively by the MIT Press.
Gayle Laakmann McDowell
Now in the 5th edition, the book gives you the interview preparation you need to get the top software developer jobs. This is a deeply technical book and focuses on the software engineering skills to ace your interview. The book includes 150 programming interview questions and answers, as well as other advice.
Michael Sipser's emphasis on unifying computer science theory - rather than offering a collection of low-level details - sets the book apart, as do his intuitive explanations. Throughout the book, Sipser builds students' knowledge of conceptual tools used in computer science, the aesthetic sense they need to create elegant systems, and the ability to think through problems on their own.
String algorithms are a traditional area of study in computer science. In recent years their importance has grown dramatically with the huge increase of electronically stored text and of molecular sequence data (DNA or protein sequences) produced by various genome projects. This 1997 book is a general text on computer algorithms for string processing. In addition to pure computer science, the book contains extensive discussions on biological problems that are cast as string problems, and on methods developed to solve them. It emphasises the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics. Its discussion of current algorithms and techniques also makes it a reference for professionals.
Stephane Faroult, Pascal L'Hermite
This practical book helps SQL developers dramatically improve database performance through good practices and effective design. It explains methods to change code and deliver maximum performance without altering the application's purpose.
John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman
This classic book on formal languages, automata theory, and computational complexity has been updated to present theoretical concepts in a concise and straightforward manner with the increase of hands-on, practical applications. This new edition comes with Gradiance, an online assessment tool developed for computer science. Gradiance is the most advanced online assessment tool developed for the computer science discipline. With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students. By using a series of “root questions” and hints, it not only tests a student’s capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material. Through the programming labs, instructors are capable of testing, tracking, and honing their students’ skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered.
Thomas Cormen, Charles Leiserson, Ronald Rivest, Clifford Stein
The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition, this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects. In its new edition, Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity, and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage. As in the classic first edition, this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further, the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds. Each chapter presents an algorithm, a design technique, an application area, or a related topic. The chapters are not dependent on one another, so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally, the new edition offers a 25% increase over the first edition in the number of problems, giving the book 155 problems and over 900 exercises that reinforce the concepts the students are learning.
Jonathan Chaffer, Karl Swedberg
Mark Allen Weiss
In this text, readers are able to look at specific problems and see how careful implementations can reduce the time constraint for large amounts of data from several years to less than a second. This new edition contains all the enhancements of the new Java 5.0 code including detailed examples and an implementation of a large subset of the Java 5.0 Collections API. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amount of efficiency. Readers should have some knowledge of intermediate programming, including topics as object-based programming and recursion, and some background in discrete math.
A complete source of information on almost all aspects of parallel computing from introduction, to architectures, to programming paradigms, to algorithms, to programming standards. It covers traditional Computer Science algorithms, scientific computing algorithms and data intensive algorithms.