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Random number generators are chaotic
ACM SIGPLAN Notices, 1989We observe that pseudo-random number generators, familiar to all programmers, are examples of deterministic chaotic dynamical systems. We discuss the implications of this finding and compare computer generation of pseudo-random numbers to the theoretical ideal of a (noncomputable) random sequence.
Charles Herring, Julian I. Palmore
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Quantum random number generator vs. random number generator
2016 International Conference on Communications (COMM), 2016A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic.
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Randomized algorithms and pseudorandom numbers
Proceedings of the twentieth annual ACM symposium on Theory of computing - STOC '88, 1988Summary: Randomized algorithms are analyzed as if unlimited amounts of perfect randomness were available, while pseudorandom number generation is usually studied from the perspective of cryptographic security or for the statistical properties of the numbers generated. \textit{E. Bach} [J. Comput. Syst. Sci. 42, No.
Howard J. Karloff, Prabhakar Raghavan
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Sci. Ann. Cuza Univ., 1994
Summary: Random strings have been studied in many papers. A natural number represented in a basis of numeration can be considered as a string consisting of its digits. Our present aim is to develop a framework and the necessarily tools for deciding if a natural number is random or not.
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Summary: Random strings have been studied in many papers. A natural number represented in a basis of numeration can be considered as a string consisting of its digits. Our present aim is to develop a framework and the necessarily tools for deciding if a natural number is random or not.
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Uniform Random Number Generators
Journal of the ACM, 1965Abstract : This paper discusses the testing of methods for generating uniform numbers in a computer--the commonly used multiplicative and mixed congruential generators as well as two methods. Tests proposed here are more stringent than those usually applied, because the usual tests for randomness have passed several of the commonly used pprocedures ...
M. Donald MacLaren, George Marsaglia
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Journal of Mathematical Sciences
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Calculations with Pseudo-Random Numbers
Journal of the ACM, 1964Two pseudo-random number generators are considered, the multiplicative congruential method and the mixed congruential method. Some properties of the generated sequences are derived, and several algorithms are developed for the evaluation of x i = @@@@( i
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On the Choice Number of Random Hypergraphs
Combinatorics, Probability and Computing, 2000Summary: We generalize the notion of choice number from graphs to hypergraphs and estimate the sharp order of magnitude of the choice number of random hypergraphs. It turns out that the choice number and the chromatic number of a random hypergraph have the same order of magnitude, almost surely.
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On the Distribution of the Maximum of a Random Number of Random Variables
Theory of Probability & Its Applications, 1992See the review Zbl 0748.60029.
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