Automata and natural language theory are topics lying at the heart of computer science. Both are linked to computational complexity and together, these disciplines help define the parameters of what constitutes a computer, the structure of programs, which problems are solvable by computers, and a range of other crucial aspects of the practice of computer science. In this important volume, two respected authors/editors in the field offer accessible, practice-oriented coverage of these issues with an emphasis on refining core problem solving skills.
Autorentext
DING-ZHU DU, PhD, is Professor of Computer Science at the University of Minnesota.
KER-I KO, PhD, is Professor of Computer Science at the State University of New York at Stony Brook. The two are also coauthors of Theory of Computational Complexity (Wiley).
Klappentext
A practical introduction to essential topics at the core of computer science
Automata, formal language, and complexity theory are central to the understanding of computer science. This book provides, in an accessible, practically oriented style, a thorough grounding in these topics for practitioners and students on all levels.
Based on the authors' belief that the problem-solving approach is the most effective, Problem Solving in Automata, Languages, and Complexity collects a rich variety of worked examples, questions, and exercises designed to ensure understanding and mastery of the subject matter. Building from the fundamentals for beginning engineers to more advanced concepts, the book examines the most common topics in the field, including:
- Finite-state automata
- Context-free grammars
- Turing machines
- Recursive and recursively enumerable languages
- Computability theory
- Complexity classes
- NP-completeness
Focused, practical, and versatile, Problem Solving in Automata, Languages, and Complexity gives students and engineers a solid grounding in essential areas in computer science.
Inhalt
Preface.
Leverages.
Finite Automata.
Context-Free Languages.
Turing Machines.
Computability Theory.
Computational Complexity.
NP-Completeness.
References.
Index.