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Would you like a theory of computation text that provides a solid, specialized introduction to algorithms?
Introduces asymptotic analysis and O- notation.
Truncates long proofs and presents them as exercises.
Provides problems after each section to check student comprehension.
Includes extensive discussion of state minimization, the Myhill-Nerode Theorem, string matching, and parsing.
Many combinatorial problems are introduced and analyzed (including variants of satisfiability), and their apparent complexity contrasted.
Would you like to teach NP—completeness, as well as ways of coping with it, in your course?
Extensive section on coping with NP - completeness that covers special cases, approximation algorithms, backtracking, and local search heuristics.
Covers NP - completeness including state minimization problem of nondeterministic finite automata.
Logic coverage has been limited to propositional logic in relation to NP - completeness.
Considers Cook's Theorem again via the tiling problem.
Discusses approximation and its complexity.
Uses the terms recursive and recursively innumerably.
Quantitatively analyzes simulations between machine models.
Introduces and analyzes a model of random access Turing machines, similar to RAMs.
Uses random access Turing machines to bridge the “credibility gap” between Turing machine model and the empirical concept of an algorithm.
Includes some recursion theory (up to Rice's theorem).
Provides an informal, concise development of A-recursive functions.
Lewis and Papadimitriou present this long awaited Second Edition of their best-selling theory of computation. The authors are well-known for their clear presentation that makes the material accessible to a a broad audience and requires no special previous mathematical experience. KEY TOPICS: In this new edition, the authors incorporate a somewhat more informal, friendly writing style to present both classical and contemporary theories of computation. Algorithms, complexity analysis, and algorithmic ideas are introduced informally in Chapter 1, and are pursued throughout the book. Each section is followed by problems.
1. Sets, Relations, and Languages.
2. Finite Automata.
3. Context-free Languages.
4. Turing Machines.
5. Undecidability.
6. Computational Complexity.
7. NP-completeness.
Index.
Would you like a theory of computation text that provides a solid, specialized introduction to algorithms?
Would you like to teach NP—completeness, as well as ways of coping with it, in your course?