Fundamentals Of The Theory Of Computation Principles And Practice

This innovative textbook presents the key foundational concepts for a one-semester undergraduate course in the theory of computation. It offers the most accessible and motivational course material available for undergraduate computer theory classes. Directed at undergraduates who may have difficulty understanding the relevance of the course to their future careers, the text helps make them more comfortable with the techniques required for the deeper study of computer science. The text motivates students by clarifying complex theory with many examples, exercises and detailed proofs.

This innovative textbook presents the key foundational concepts for a one-semester undergraduate course in the theory of computation. It offers the most accessible and motivational course material available for undergraduate computer theory classes. Directed at undergraduates who may have difficulty understanding the relevance of the course to their future careers, the text helps make them more comfortable with the techniques required for the deeper study of computer science. The text motivates students by clarifying complex theory with many examples, exercises and detailed proofs. * This book is shorter and more accessible than the books now being used in core computer theory courses. * Theory of computing is a standard, required course in all computer science departments.

This book constitutes the refereed proceedings of the 13th International Symposium Fundamentals of Computation Theory, FCT 2001, as well as of the International Workshop on Efficient Algorithms, WEA 2001, held in Riga, Latvia, in August 2001. The 28 revised full FCT papers and 15 short papers presented together with six invited contributions and 8 revised full WEA papers as well as three invited WEA contributions have been carefully reviewed and selected. Among the topics addressed are a broad variety of topics from theoretical computer science, algorithmics and programming theory. The WEA papers deal with graph and network algorithms, flow and routing problems, scheduling and approximation algorithms, etc.

This textbook discusses the most fundamental and puzzling questions about the foundations of computing. In 23 lecture-sized chapters it provides an exciting tour through the most important results in the field of computability and time complexity, including the Halting Problem, Rice's Theorem, Kleene's Recursion Theorem, the Church-Turing Thesis, Hierarchy Theorems, and Cook-Levin's Theorem. Each chapter contains classroom-tested material, including examples and exercises. Links between adjacent chapters provide a coherent narrative. Fundamental results are explained lucidly by means of programs written in a simple, high-level imperative programming language, which only requires basic mathematical knowledge. Throughout the book, the impact of the presented results on the entire field of computer science is emphasised. Examples range from program analysis to networking, from database programming to popular games and puzzles. Numerous biographical footnotes about the famous scientists who developed the subject are also included. "Limits of Computation" offers a thorough, yet accessible, introduction to computability and complexity for the computer science student of the 21st century.

This textbook is uniquely written with dual purpose. It cover cores material in the foundations of computing for graduate students in computer science and also provides an introduction to some more advanced topics for those intending further study in the area. This innovative text focuses primarily on computational complexity theory: the classification of computational problems in terms of their inherent complexity. The book contains an invaluable collection of lectures for first-year graduates on the theory of computation. Topics and features include more than 40 lectures for first year graduate students, and a dozen homework sets and exercises.

Provides an innovative hands-on introduction to techniques for specifying the behaviour of software components. It is primarily intended for use as a text book for a course in the 2nd or 3rd year of Computer Science and Computer Engineering programs, but it is also suitable for self-study. Using this book will help the reader improve programming skills and gain a sound foundation and motivation for subsequent courses in advanced algorithms and data structures, software design, formal methods, compilers, programming languages, and theory. The presentation is based on numerous examples and case studies appropriate to the level of programming expertise of the intended readership. The main topics covered are techniques for using programmer-friendly assertional notations to specify, develop, and verify small but non-trivial algorithms and data representations, and the use of state diagrams, grammars, and regular expressions to specify and develop recognizers for formal languages.

Programming Language Pragmatics addresses the fundamental principles at work in the most important contemporary languages, highlights the critical relationship between language design and language implementation, and devotes special attention to issues of importance to the expert programmer. Thanks to its rigorous but accessible teaching style, you'll emerge better prepared to choose the best language for particular projects, to make more effective use of languages you already know, and to learn new languages quickly and completely.

"Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Introduction to the Theory of Computation covers the usual topics for this type of text plus it features a solid section on complexity theory--including an entire chapter on space complexity. The final chapter introduces more advanced topics, such as the discussion of complexity classes associated with probabilistic algorithms.