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173
Mersenne Twister: A 623dimensionally equidistributed uniform pseudorandom number generator
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Good Parameters And Implementations For Combined Multiple Recursive Random Number Generators
, 1998
"... this paper is to provide good CMRGs of different sizes, selected via the spectral test up to 32 (or 24) dimensions, and a faster implementation than in L'Ecuyer (1996) using floatingpoint arithmetic. Why do we need different parameter sets? Firstly, different types of implementations require d ..."
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Cited by 93 (21 self)
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this paper is to provide good CMRGs of different sizes, selected via the spectral test up to 32 (or 24) dimensions, and a faster implementation than in L'Ecuyer (1996) using floatingpoint arithmetic. Why do we need different parameter sets? Firstly, different types of implementations require different constraints on the modulus and multipliers. For example, a floatingpoint implementation with 53 bits of precision allows moduli of more than 31 bits and this can be exploited to increase the period length for free. Secondly, as 64bit computers get more widespread, there is demand for generators implemented in 64bit integer arithmetic. Tables of good parameters for such generators must be made available. Thirdly, RNGs are somewhat like cars: a single model and single size for the entire world is not the most satisfactory solution. Some people want a fast and relatively small RNG, while others prefer a bigger and more robust one, with longer period and good equidistribution properties in larger dimensions. Naively, one could think that an RNG with period length near 2
Maximally Equidistributed Combined Tausworthe Generators
, 1996
"... Tausworthe random number generators based on a primitive trinomial allow an easy and fast implementation when their parameters obey certain restrictions. However, such generators, with those restrictions, have bad statistical properties unless we combine them. A generator is called maximally equidis ..."
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Cited by 92 (24 self)
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Tausworthe random number generators based on a primitive trinomial allow an easy and fast implementation when their parameters obey certain restrictions. However, such generators, with those restrictions, have bad statistical properties unless we combine them. A generator is called maximally equidistributed if its vectors of successive values have the best possible equidistribution in all dimensions. This paper shows how to find maximally equidistributed combinations in an efficient manner, and gives a list of generators with that property. Such generators have a strong theoretical support and lend themselves to very fast software implementations.
TestU01: A C library for empirical testing of random number generators
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 2007
"... We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several ot ..."
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Cited by 80 (4 self)
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We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widelyused software. The tests can be applied to instances of the generators predefined in the library, or to userdefined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the paper provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.
Recent Advances In Randomized QuasiMonte Carlo Methods
"... We survey some of the recent developments on quasiMonte Carlo (QMC) methods, which, in their basic form, are a deterministic counterpart to the Monte Carlo (MC) method. Our main focus is the applicability of these methods to practical problems that involve the estimation of a highdimensional inte ..."
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Cited by 78 (15 self)
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We survey some of the recent developments on quasiMonte Carlo (QMC) methods, which, in their basic form, are a deterministic counterpart to the Monte Carlo (MC) method. Our main focus is the applicability of these methods to practical problems that involve the estimation of a highdimensional integral. We review several QMC constructions and dierent randomizations that have been proposed to provide unbiased estimators and for error estimation. Randomizing QMC methods allows us to view them as variance reduction techniques. New and old results on this topic are used to explain how these methods can improve over the MC method in practice. We also discuss how this methodology can be coupled with clever transformations of the integrand in order to reduce the variance further. Additional topics included in this survey are the description of gures of merit used to measure the quality of the constructions underlying these methods, and other related techniques for multidimensional integration.
Variance Reduction via Lattice Rules
 Management Science
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 64 (13 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Improved longperiod generators based on linear recurrences modulo 2
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 2006
"... Fast uniform random number generators with extremely long periods have been defined and implemented based on linear recurrences modulo 2. The twisted GFSR and the Mersenne twister are famous recent examples. Besides the period length, the statistical quality of these generators is usually assessed v ..."
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Cited by 59 (8 self)
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Fast uniform random number generators with extremely long periods have been defined and implemented based on linear recurrences modulo 2. The twisted GFSR and the Mersenne twister are famous recent examples. Besides the period length, the statistical quality of these generators is usually assessed via their equidistribution properties. The hugeperiod generators proposed so far are not quite optimal in that respect. In this paper, we propose new generators of that form, with better equidistribution and “bitmixing ” properties for equivalent period length and speed. The state of our new generators evolves in a more chaotic way than for the Mersenne twister. We illustrate how this can reduce the impact of persistent dependencies among successive output values, which can be observed in certain parts of the period of gigantic generators such as the Mersenne twister.
SIMDoriented fast Mersenne twister: A 128bit pseudorandom number generator
 and QuasiMonte Carlo Methods 2006
, 2007
"... Summary. Mersenne Twister (MT) is a widelyused fast pseudorandom number generator (PRNG) with a long period of 2 19937 − 1, designed 10 years ago based on 32bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) opera ..."
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Cited by 46 (4 self)
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Summary. Mersenne Twister (MT) is a widelyused fast pseudorandom number generator (PRNG) with a long period of 2 19937 − 1, designed 10 years ago based on 32bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) operations (i.e., 128bit operations) and multistage pipelines. Here we propose a 128bit based PRNG, named SIMDoriented Fast Mersenne Twister (SFMT), which is analogous to MT but making full use of these features. Its recursion fits pipeline processing better than MT, and it is roughly twice as fast as optimised MT using SIMD operations. Moreover, the dimension of equidistribution of SFMT is better than MT. We also introduce a blockgeneration function, which fills an array of 32bit integers in one call. It speeds up the generation by a factor of two. A speed comparison with other modern generators, such as multiplicative recursive generators, shows an advantage of SFMT. The implemented Ccodes are downloadable from
The Numerical Reliability of Econometric Software
, 1999
"... Numerical software is central to our computerized society; it is used... to analyze future options for financial markets and the economy. It is essential that it be of high ..."
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Cited by 41 (10 self)
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Numerical software is central to our computerized society; it is used... to analyze future options for financial markets and the economy. It is essential that it be of high
Inversive Congruential Pseudorandom Numbers: Distribution Of Triples
, 1997
"... This paper deals with the inversive congruential method with power of two modulus m for generating uniform pseudorandom numbers. Statistical independence properties of the generated sequences are studied based on the distribution of triples of successive pseudorandom numbers. It is shown that, on th ..."
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Cited by 40 (0 self)
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This paper deals with the inversive congruential method with power of two modulus m for generating uniform pseudorandom numbers. Statistical independence properties of the generated sequences are studied based on the distribution of triples of successive pseudorandom numbers. It is shown that, on the average over the parameters in the inversive congruential method, the discrepancy of the corresponding point sets in the unit cube is of an order of magnitude between m \Gamma1=2 and m \Gamma1=2 (log m)³. The method of proof relies on a detailed discussion of the properties of certain exponential sums.