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On the reduction theory for average case complexity

  • Andreas Blass
  • Yuri Gurevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 533)

Abstract

This is an attempt to simplify and justify the notions of deterministic and randomized reductions, an attempt to derive these notions from (more or less) first principles.

Keywords

Random Function Positive Probability Binary String Computable Function Total Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Shai Ben-David, Benny Chor, Oded Goldreich and Michael Luby, “On the Theory of Average Case Complexity”, Symposium on Theory of Computing, ACM, 1989, 204–216.Google Scholar
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    Yuri Gurevich, “Matrix Decomposition Problem is Complete for the Average Case”, Symposium on Foundations of Computer Science, IEEE Computer Society Press, 1990, 802–811.Google Scholar
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    [GJ]Michael R. Garey and David S. Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness”, Freeman, New York, 1979.Google Scholar
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    Russel Impagliazzo and Leonid A. Levin, “No Better Ways to Generate Hard NP Instances than Picking Uniformly at Random”, Symposium on Foundations of Computers Science, IEEE Computer Soviety Press, 1990, 812–821.Google Scholar
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    Leonid A. Levin, “Average Case Complete Problems”, SIAM Journal of Computing, 1986.Google Scholar
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    Ramarathnam Venkatesan and Leonid Levin, “Random Instances of a Graph Coloring Problem are Hard”, Symposium on Theory of Computing, ACM, 1988, 217–222.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Andreas Blass
    • 1
  • Yuri Gurevich
    • 2
  1. 1.Mathematics DepartmentUniversity of MichiganAnn ArborUSA
  2. 2.Electrical Engineering and Computer Science DepartmentUniversity of MichiganAnn ArborUSA

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