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Applications of Continuous Mathematics to Computer Science

Aus der Reihe Theory and Decision Library B
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.12.2010

Abbildungen

XII, 419 p.

Verlag

Springer Netherland

Seitenzahl

419

Maße (L/B/H)

27,9/21/2,4 cm

Gewicht

1056 g

Auflage

Softcover reprint of hardcover 1st edition 1997

Sprache

Englisch

ISBN

978-90-481-4901-8

Beschreibung

Rezension

H.T. Nguyen and V. Kreinovich


Applications of Continuous Mathematics to Computer Science


"The book represents a fine state-of-the-art description of combinatorial optimization. The book presents not only a lot of well-known solutions but also a row of new results and demonstrates how to formulate and to answer original questions. The comprehensive book covers the scope of a normal textbook on combinatorial optimization and goes beyond the contents of such a book in several aspects, e.g.; this book contains complete proofs. To read this is very stimulating for all the researchers, practitioners, and students in combinatorial optimization."—
OR-NEWS

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.12.2010

Abbildungen

XII, 419 p.

Verlag

Springer Netherland

Seitenzahl

419

Maße (L/B/H)

27,9/21/2,4 cm

Gewicht

1056 g

Auflage

Softcover reprint of hardcover 1st edition 1997

Sprache

Englisch

ISBN

978-90-481-4901-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Applications of Continuous Mathematics to Computer Science
  • Produktbild: Applications of Continuous Mathematics to Computer Science
  • Preface. 1. Algorithm Complexity: Two Simple Examples. 2. Solving General Linear Functional Equations: An Application to Algorithm Complexity. 3. Program Testing: A Problem. 4. Optimal Program Testing. 5. Optimal Choice of a Penalty Function: Simplest Case of Algorithm Design. 6. Solving General Linear Differential Equations with Constant Coefficients: An Application to Constrained Optimization. 7. Simulated Annealing: `Smooth' (Local) Discrete Optimization. 8. Genetic Algorithms: `Non-Smooth' Discrete Optimization. 9. RISC Computer Architecture and Internet Growth: Two Applications of Extrapolation. 10. Systems of Differential Equations and Their Use in Computer-Related Extrapolation Problems. 11. Network Congestion: An Example of Non-Linear Extrapolation. 12. Neural Networks: A General Form of Non-Linear Extrapolation. 13. Expert Systems and the Basics of Fuzzy Logic. 14. Intelligent and Fuzzy Control. 15. Randomness, Chaos, and Fractals. A: Simulated Annealing Revisited. B: Software Cost Estimation. C: Electronic Engineering: How to Describe PN-Junctions. D: Log-Normal Distribution Justified: An Application to Computational Statistics. E: Optimal Robust Statistical Methods. F: How to Avoid Paralysis of Neural Networks. G: Estimating Computer Prices. H: Allocating Bandwidth on Computer Networks. I: Algorithm Complexity Revisited. J: How Can a Robot Avoid Obstacles: Case Study of Real-Time Optimization. K: Discounting in Robot Control: A Case Study of Dynamic Optimization. Index.