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Preprints of papers presented at the 14th national meeting of the Association for Computing Machinery on - ACM '59, 1959
One requirement common to all Monte Carlo computer simulations is an abundant and automatic supply of random numbers. For most purposes it generally suffices to draw this supply from the uniform distribution in the unit interval. The mathematical tricks used to convert these samplings to samplings from other distributions are well-known.
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One requirement common to all Monte Carlo computer simulations is an abundant and automatic supply of random numbers. For most purposes it generally suffices to draw this supply from the uniform distribution in the unit interval. The mathematical tricks used to convert these samplings to samplings from other distributions are well-known.
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Generating random regular graphs
Proceedings of the thirty-fifth ACM symposium on Theory of computing - STOC '03, 2003Random regular graphs play a central role in combinatorics and theoretical computer science. In this paper, we analyze a simple algorithm introduced by Steger and Wormald [10] and prove that it produces an asymptotically uniform random regular graph in a polynomial time.
Jeong Han Kim, Van H. Vu
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24th Annual Symposium on Foundations of Computer Science (sfcs 1983), 1983
We present a randomized solution for the Byzantine Generals Problems. The solution works in the synchronous as well as the asynchronous case and produces Byzantine Agreement within a fixed small expected number of computational rounds, independent of the number n of processes and the bound t on the number of faulty processes.
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We present a randomized solution for the Byzantine Generals Problems. The solution works in the synchronous as well as the asynchronous case and produces Byzantine Agreement within a fixed small expected number of computational rounds, independent of the number n of processes and the bound t on the number of faulty processes.
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ACM SIGSIM Simulation Digest, 1973
This note gives APL functions for generating random variables from 21 common statistical distributions:
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This note gives APL functions for generating random variables from 21 common statistical distributions:
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Random Fractals Generated by Random Cutouts
Mathematische Nachrichten, 1984From the \(R^ N\) an independent sequence of stationary random open sets is ''cutted out''. There are given conditions on these ''cutouts'' under which the Hausdorff Besicovitch dimension of the random remainder set of the \(R^ N\) can be computed. These conditions force a very weak variant of ''random similarity'' between the cutouts.
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A Random Activity Network Generator
Operations Research, 1993Exact and heuristic procedures are often developed to obtain optimal and near-optimal solutions to decision problems modeled as activity networks. Testing the accuracy and efficiency of these procedures requires the use of activity networks with various sizes, structures, and parameters. The size of the network is determined by its number of nodes and
Erik Demeulemeester +2 more
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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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Random Generation Models for NFAs
J. Autom. Lang. Comb., 2004Journal of Automata, Languages and Combinatorics, Volume 9, Numbers 2-3, 2004, 203 ...
Jean-Marc Champarnaud +3 more
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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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Generating Random Unlabelled Graphs
SIAM Journal on Computing, 1987Unlabelled graphs on n vertices can be generated uniformly at random, without calculating the total numbers of such graphs, but using asymptotic enumeration results. For large n the process is very efficient, taking O(n 2) steps on average. By similar methods one can generate random unlabelled graphs with n vertices and m edges in expected time for ...
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