Skip to main content

Probabilistische Algorithmen

oder es dem Zufall überlassen

  • Chapter
Algorithmik
  • 2097 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 34.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 49.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Kapitel 11

  1. R. Motwani und P. Raghavan, Randomized Algorithms, Cambridge University Press, 1995.

    Google Scholar 

  2. N. Alon und J. H. Spencer, The Probabilistic Method, 2. Auflage, John Wiley & Sons, 2000.

    Google Scholar 

  3. M. Hofri, Probabilistic Analysis of Algorithms, Springer-Verlag, 1987.

    Google Scholar 

  4. D. Lehmann und M. O. Rabin, „The Advantages of Free Choice: A Symmetric and Fully Distributed Solution to the Dining Philosophers Problem,“ Proc. 8th ACM Symp. on Principles of Programming Languages, ACM Press, S. 133–8, 1981.

    Google Scholar 

  5. V. R. Pratt, „Every Prime has a Succint Certificate,“ SIAM J. Comput. 4 (1975), S. 214–20.

    Article  MATH  MathSciNet  Google Scholar 

  6. G. L. Miller, „Riemann’s Hypothesis and Tests for Primality,“ J. Comput. Syst. Sci. 13 (1976), S. 300–17.

    MATH  Google Scholar 

  7. L. Adelman, C. Pomerance und R. S. Rumely, „On Distinguishing Prime Numbers from Composite Numbers,“ Ann. Math. 117 (1983), S. 173–206.

    Article  Google Scholar 

  8. M. Agrawal, N. Kayal und N. Saxena, „PRIMES is in P,“ Manuskript, August 2002.

    Google Scholar 

  9. M. O. Rabin, „Probabilistic Algorithm for Testing Primality,“ J. Number Theory 12 (1980), S. 128–38.

    Article  MATH  MathSciNet  Google Scholar 

  10. R. Solovay und V. Strassen, „A Fast Monte-Carlo Test for Primality,“ SIAM J. Comput. 6 (1977), S. 84–5.

    Article  MATH  MathSciNet  Google Scholar 

  11. M. O. Rabin, „Probabilistic Algorithms,“ in Algorithms and Complexity: Recent Results and New Directions, J. F. Traub, Hrsg., Academic Press, S. 21–40, 1976.

    Google Scholar 

  12. D. E. Knuth, J. H. Morris und V. R. Pratt, „Fast Pattern Matching in Strings,“ SIAMJ. Comput. 6 (1977). S. 323–50.

    Article  MATH  MathSciNet  Google Scholar 

  13. R. S. Boyer und J. S. Moore, „A Fast String Searching Algorithm,“ Comm. Assoc. Comput. Mach. 20 (1977), S. 762–72.

    Google Scholar 

  14. R. M. Karp und M. O. Rabin, „Efncient Randomized Pattern-Matching Algorithms,“ IBM J. Res. Dev. 31 (1987), S. 249–60.

    MATH  MathSciNet  Google Scholar 

  15. A. V. Aho, „Algorithms for Finding Patterns in Strings,“ in Handbook of Theoretical Computer Science, Vol. A, J. van Leeuwen, Hrsg., Elsevier und MIT Press, 1990, S. 255–300.

    Google Scholar 

  16. R. M. Karp, „Combinatorics, Complexity, and Randomness,“ Comm. Assoc. Comput. Mach. 29 (1986), S. 98–109.

    MATH  MathSciNet  Google Scholar 

  17. J. Gill, „Computational Complexity of Probabilistic Turing Machines,“ SIAMJ. Comput. 6 (1977), S. 675–95.

    Article  MATH  MathSciNet  Google Scholar 

  18. D. S. Johnson, „A Catalog of Complexity Classes,“ in Handbook of Theoretical Computer Science, Vol. A, J. van Leeuwen, Hrsg., Elsevier und MIT Press, 1990, S. 67–161.

    Google Scholar 

  19. S. Zachos, „Robustness of Probabilistic Computational Complexity Classes under Definitional Perturbations,“ Inf. and Cont. 54 (1982), S. 143–54.

    Article  MATH  MathSciNet  Google Scholar 

  20. U. Schöning, Complexity and Structure, Lecture Notes in Computer Science, Vol. 211, Springer-Verlag, 1986.

    Google Scholar 

  21. D. Kozen, „Semantics of Probabilistic Programs,“ J. Comput. Syst. Sci. 22 (1981), S. 328–50.

    Article  MATH  MathSciNet  Google Scholar 

  22. D. Lehmann und S. Shelah, „Reasoning with Time and Chance,“ Inf. and Cont. 53 (1982), S. 165–98.

    Article  MATH  MathSciNet  Google Scholar 

  23. S. Hart, M. Sharir und A. Pnueli, „Termination of Probabilistic Concurrent Programs,“ ACM Trans. Prog. Lang. Syst. 5 (1983), S. 352–80.

    Article  Google Scholar 

  24. Y. A. Feldman und D. Harel, „A Probabilistic Dynamic Logic,“ J. Comput. Syst. Sci. 28 (1984), S. 193–215.

    Article  MATH  MathSciNet  Google Scholar 

  25. Y. A. Feldman, „A Decidable Prepositional Probabilistic Dynamic Logic with Explicit Probabilities,“ Inf. and Cont. 63 (1984), S. 11–38.

    Article  MATH  Google Scholar 

  26. L. Adleman, „Two Theorems on Random Polynomial Time,“ Proc. 19th IEEE Symp. on Foundations of Computer Science, IEEE Press, S. 75–83, 1978.

    Google Scholar 

  27. A. C. Yao, „Probabilistic Computations: Towards a Unified Measure of Complexity,“ Proc. 18th IEEE Symp. on Foundations of Computer Science, IEEE Press, S. 222–7, 1977.

    Google Scholar 

  28. A. Kolmogorov, „Three Approaches to the Concept of the Amount of Information,“ Probl. Inf. Transm. 1 (1965), S. 1–7.

    MATH  Google Scholar 

  29. A. Shamir, „On the Generation of Cryptographically Secure Pseudo-Random Sequences,“ ACM Transactions on Computer Systems 1 (1983), S. 38–44.

    Article  Google Scholar 

  30. A. C. Yao, „Theory and Applications of Trapdoor Functions,“ Proc. 23rd IEEE Symp. on Foundations of Computer Science, IEEE Press, S. 80–91, 1982.

    Google Scholar 

  31. M. Blum und S. Micali, „How to Generate Cryptographically Strong Sequences of Pseudo-Random Bits,“ SIAM J. Comput. 13 (1984), S. 850–64.

    Article  MATH  MathSciNet  Google Scholar 

  32. D. E. Knuth, The Art of Computer Programming, Vol. 2: Seminumerical Algorithms, 3. Auflage, Addison-Wesley, 1997.

    Google Scholar 

  33. O. Goldreich und L. Lovasz, Modern Cryptography, Probabilistic Proofs and Pseudo-randomness, Springer-Verlag, 1999.

    Google Scholar 

  34. G. J. Chaitin, „Randomness and Mathematical Proof,“ Scientific American 232:5 (1975), S. 47–52.

    Article  Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

(2006). Probabilistische Algorithmen. In: Algorithmik. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-37437-X_11

Download citation

Publish with us

Policies and ethics