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What Can Be Computed? : A Practical Guide to the Theory of Computation /
What Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundam...
| Autor principal: | |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, New Jersey :
Princeton University Press,
[2018]
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| Colección: | Book collections on Project MUSE.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a MacCormick, John, |d 1972- |e author. | |
| 245 | 1 | 0 | |a What Can Be Computed? : |b A Practical Guide to the Theory of Computation / |c John MacCormick. |
| 264 | 1 | |a Princeton, New Jersey : |b Princeton University Press, |c [2018] | |
| 264 | 3 | |a Baltimore, Md. : |b Project MUSE, |c 2023 | |
| 264 | 4 | |c ©[2018] | |
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| 505 | 0 | |a Overview -- 1. Introduction: what can and cannot be computed? -- Part I: computability theory -- 2. What is a computer program? -- 3. Some impossible python programs -- 4. What is a computational problem? -- 5. Turing machines: the simplest computers -- 6. Universal computer programs: programs that can do anything -- 7. Reductions: how to prove a problem is hard -- 8. Nondeterminism: magic or reality? -- 9. Finite automata: computing with limited resources -- Part II: computational complexity theory -- 10. Complexity theory: when efficiency does matter -- 11. Poly and expo: the two most fundamental complexity classes -- 12. PolyCheck and NPoly: hard problems that are easy to identify -- 13. Polynomial-time mapping reductions: proving x is as easy as proving y -- 14. NP-completeness: most hard problems are equally hard -- Part III: origins and applications -- 15. The original Turing machine -- 16. You can't prove everything that's true -- 17. Karp's 21 problems -- 18. Conclusion: what will be computed?. | |
| 520 | |a What Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundamentals of computer science theory and features a practical approach that uses real computer programs (Python and Java) and encourages active experimentation. It is also ideal for self-study and reference. The book covers the standard topics in the theory of computation, including Turing machines and finite automata, universal computation, nondeterminism, Turing and Karp reductions, undecidability, time-complexity classes such as P and NP, and NP-completeness, including the Cook-Levin Theorem. But the book also provides a broader view of computer science and its historical development, with discussions of Turing's original 1936 computing machines, the connections between undecidability and Gödel's incompleteness theorem, and Karp's famous set of twenty-one NP-complete problems. Throughout, the book recasts traditional computer science concepts by considering how computer programs are used to solve real problems. Standard theorems are stated and proven with full mathematical rigor, but motivation and understanding are enhanced by considering concrete implementations. The book's examples and other content allow readers to view demonstrations of--and to experiment with--a wide selection of the topics it covers. The result is an ideal text for an introduction to the theory of computation. | ||
| 588 | |a Description based on print version record. | ||
| 650 | 7 | |a Computers and IT. |2 ukslc | |
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| 650 | 0 | |a Computer science |x History. | |
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| 945 | |a Project MUSE - 2023 Annual Backfile - Unpurchased | ||