Mersenne twister

Matsumoto, Makoto; Nishimura, Takuji

doi:10.1145/272991.272995

Cited by 4,690 publications

(2,045 citation statements)

References 29 publications

Supporting

Mentioning

1,930

Contrasting

Unclassified

Order By: Relevance

“…The soft X-ray diffuser consisted of a pseudo-random array of B40 nm diameter holes in a 140-nm thick tungsten film in the beam path. The holes covered about a quarter of a 95 Â 95 mm 2 field in the tungsten film, with hole positions determined by the Mersenne Twister algorithm 32 to avoid systematic correlations. Random hole patterns were supplied to our specification by Zoneplates Ltd, London, UK.…”

Section: Methodsmentioning

confidence: 99%

Soft X-ray spectromicroscopy using ptychography with randomly phased illumination

et al. 2013

View full text Add to dashboard Cite

Ptychography is a form of scanning diffractive imaging that can successfully retrieve the modulus and phase of both the sample transmission function and the illuminating probe. An experimental difficulty commonly encountered in diffractive imaging is the large dynamic range of the diffraction data. Here we report a novel ptychographic experiment using a randomly phased X-ray probe to considerably reduce the dynamic range of the recorded diffraction patterns. Images can be reconstructed reliably and robustly from this setup, even when scatter from the specimen is weak. A series of ptychographic reconstructions at X-ray energies around the L absorption edge of iron demonstrates the advantages of this method for soft X-ray spectromicroscopy, which can readily provide chemical sensitivity without the need for optical refocusing. In particular, the phase signal is in perfect registration with the modulus signal and provides complementary information that can be more sensitive to changes in the local chemical environment.

show abstract

Section: Methodsmentioning

confidence: 99%

Soft X-ray spectromicroscopy using ptychography with randomly phased illumination

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The Mersenne Twister (Matsumoto & Nishimura, 1998), which is known as MT19937, does not repeat itself until it has dispensed 2 19937 − 1 values. Even among scientists who are accustomed to dealing with big numbers, that is a huge number.…”

Section: Where Do Random Numbers Come From?mentioning

confidence: 99%

“…On the surface, at least, that seems to imply that if one sets the same random seed to initialize the process, then one ought to be able to draw the exact same stream of random numbers. Documentation for most programs is superficial, simply stating that the generator is MT19937 and referring the authors to the well known publication (Matsumoto & Nishimura, 1998 …”

Section: Replicationmentioning

confidence: 99%

Monte Carlo Analysis in Academic Research

Johnson

2013

Oxford Handbooks Online

View full text Add to dashboard Cite

Monte Carlo analysis is a research strategy that incorporates randomness into the design, implementation or evaluation of theoretical models. It began in the 1940s, when the development of computer hardware and mathematical models made it possible to generate streams of random numbers. These random number streams are combined with mathematical models in order to create models and evaluate theories of random processes. This chapter attempts to tame this diverse, unmanageable collection of concepts and methods by dividing simulation projects into three types. The first, commonly called "Monte Carlo simulation," is used to evaluate statistical estimators. When an estimation procedure is proposed, it is standard procedure to test it against a variety of simulated research problems. A second type of project, referred to as "Markov chain Monte Carlo" (or MCMC), helps researchers to draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third 1 type of project arises in the study of complex systems, which are characterized by a large number of loosely interconnected, autonomous elements. Commonly known as agent-based models, these simulations have found enthusiastic advocates in environmental and social sciences.Keywords: Monte Carlo, Markov chain Monte Carlo (MCMC), pseudo random number generation (PRNG), Bayesian statistics, agent-based modeling Monte Carlo (MC) analysis is a general term that refers to research that employs random numbers, usually in the form of a computer model (or simulation). Although this research began in the natural sciences, computer science, and mathematics, it is now widely applied in social science as well. This essay attempts to explain the fundamental ideas that spurred the creation of these new procedures as well as their eventual adaptation for use in social science research.This chapter is not a "how to" guide for simulation, but rather it is a "what for" or "why you might want to" guide. Some of the difficulties that arise in MC research projects are considered as well. It begins with some background information on the development of computers and algorithms for random numbers. After that, the chapter takes up applications in the evaluation of proposed statistical estimators, the practice of Bayesian statistics via computer simulation, and investigation of complex systems through agent-based models. Some conclusions about the challenges that face the field are presented, along with a conclusion. A significant part of the presentation is about the exciting developments that have occurred since 1990. Rapid improvements in hardware and software have opened up opportunities for scholars to work with models that were previously prohibited by conceptual and technical barriers. At the current time, we are able to conceptualize and implement models that were simply impossible just 10 years ago. The extremely rapid progress has been driven by a fruitful interaction of substantive researchers in the natural and social science...

show abstract

“…The Mersenne Twister PRNG [9] was used during the initialisation phase and afterwards. For every problem, the GA was run ten times, for a maximum of 500 generations.…”

Section: F(x) = X4-12x3+15x2+56x-60mentioning

confidence: 99%

Millipede, an Extended Representation for Genetic Algorithms

Meli¹

2013

IJCTE

View full text Add to dashboard Cite

Abstract-In this paper, a proposal of a new extended binary representation ('Millipede') is made for genetic algorithms (GA). The GA representation may have an important effect on its performance. This paper looks at how single-valued encoding could be enhanced. Initial tests indicate this new solution encoding can be effective. Tests were made using a Genetic Algorithm (GA) package called GAGENES, written in object-oriented C++.

show abstract

Mersenne twister

Cited by 4,690 publications

References 29 publications

Soft X-ray spectromicroscopy using ptychography with randomly phased illumination

Soft X-ray spectromicroscopy using ptychography with randomly phased illumination

Monte Carlo Analysis in Academic Research

Millipede, an Extended Representation for Genetic Algorithms

Contact Info

Product

Resources

About