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On the Density of the Probabilistic Polynomial Classes

  • José D. P. Rolim

Abstract

There are many results on which intractable problems can be solved quickly using randomized algorithms, starting with the classical paper by Rabin [10]. Comparisons of probabilistic and deterministic performances have stimulated several possibilities for randomization by considering the structure of the probabilistic classes [8]. The study of expected complexity has been developed from two basic points of view [13]. In the first one, the so called distributional approach, the input probability must be known and the theory is developed under these input assumptions. The second one, called the randomized approach, allows stochastic moves in the computations. In this paper, we study a distributional randomized approach. By defining density for probabilistic machines, we consider probability on the inputs and we adopt the probabilistic Turing machine as our formal model of computation.

Keywords

Error Probability Turing Machine Complexity Class Computation Path Uniform Probability Distribution 
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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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • José D. P. Rolim
    • 1
  1. 1.Centre Universitaire d’InformatiqueUniversité de GenèveGenèveSwitzerland

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