ESTIMASI PENGUNJUNG MENGGUNAKAN SIMULASI MONTE CARLO PADA WARUNG INTERNET XYZ

Monte Carlo is a simulation method that using random numbers obtained from Linear Congruential Generator (multiplicative generator) as an approximation in estimating the number of visitors using previous time visitor data. The number of visitors who come to use internet services on internet cafes is often difficult to predict. Apart from some indicators that affect or may be experienced by the cafe owner with the activities of the internet service, it will predicted the number of visitors using visitor data from 60 days ago with linear method congruential generator as scrambler and monte carlo method as estimator. The results obtained is the estimated number of visitor in uniform distribution [0,1] for the next 60 days that can be used as information for cafe owner

Comparison of Randomized Solutions for Constrained Vehicle Routing Problem

In this short paper, we study the capacity-constrained vehicle routing problem (CVRP) and its solution by randomized Monte Carlo methods. For solving CVRP we use some pseudorandom number generators commonly used in practice. We use linear, multiple-recursive, inversive, and explicit inversive congruential generators and obtain random numbers from each to provide a route for CVRP. Then we compare the performance of pseudorandom number generators with respect to the total time the random route takes. We also constructed an open-source library github.com/iedmrc/binary-cws-mcs on solving CVRP by Monte-Carlo based heuristic methods.Comment: 6 pages, 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE), 12-13 June 2020, Istanbul, Turke

Systematic errors due to linear congruential random-number generators with the Swendsen-Wang algorithm: A warning

We show that linear congruential pseudo-random-number generators can cause systematic errors in Monte Carlo simulations using the Swendsen-Wang algorithm, if the lattice size is a multiple of a very large power of 2 and one random number is used per bond. These systematic errors arise from correlations within a single bond-update half-sweep. The errors can be eliminated (or at least radically reduced) by updating the bonds in a random order or in an aperiodic manner. It also helps to use a generator of large modulus (e.g. 60 or more bits).Comment: Revtex4, 4 page

Modifikasi Metode Linear Congruential Generator Untuk Optimalisasi Hasil Acak

Pelaksanaan ujian secara konvensional dianggap kurang efektif dan efisien karena membutuhkan biaya yang besar dan waktu yang lama dalam pelaksanaannya sehingga perlu dilakukan perbaikan dengan mengubah sistem ujian menjadi komputerisasi. Dalam setiap pelaksanaan ujian perlu memperhatikan tindak kecurangan yang dilakukan siswa berupa mencontek dan kerja sama bertukar jawaban. Penelitian ini bertujuan untuk memberikan soal acak yang berbeda kepada setiap siswa dengan menggunakan metode Linear Congruential Generator (LCG). Akan tetapi penggunaan metode LCG masih memiliki kelemahan dimana hasil pengacakan mudah ditebak sehingga perlu adanya optimalisasi pengacakan yaitu menggunakan dua LCG dan bantuan matrik yang menjadi metode Coupled Linear Congruential Generator (CLCG). Metode modifikasi CLCG menghasilkan pengacakan yang lebih baik dan pola yang lebih rumit dibandingkan dengan metode LCG

On the period of the linear congruential and power generators

We consider the periods of the linear congruential and the power generators modulo $n$ and, for fixed choices of initial parameters, give lower bounds that hold for ``most'' $n$ when $n$ ranges over three different sets: the set of primes, the set of products of two primes (of similar size), and the set of all integers. For most $n$ in these sets, the period is at least $n^{1/2+\epsilon(n)}$ for any monotone function $\epsilon(n)$ tending to zero as $n$ tends to infinity. Assuming the Generalized Riemann Hypothesis, for most $n$ in these sets the period is greater than $n^{1-\epsilon}$ for any $\epsilon >0$. Moreover, the period is unconditionally greater than $n^{1/2+\delta}$, for some fixed $\delta>0$, for a positive proportion of $n$ in the above mentioned sets. These bounds are related to lower bounds on the multiplicative order of an integer $e$ modulo $p-1$, modulo $\lambda(pl)$, and modulo $\lambda(m)$ where $p,l$ range over the primes, $m$ ranges over the integers, and where $\lambda(n)$ is the order of the largest cyclic subgroup of $(\Z/n\Z)^\times$.Comment: 20 pages. One of the quoted results (Theorem 23 in the previous version) is stated for any unbounded monotone function psi(x), but it appears that the proof only supports the case when psi(x) is increasing rather slowly. As a workaround, we provide a modified version of Theorem 23, and change the argument in the proof of Theorem 27 (Theorem 25 in the previous version

Portable random number generators

Computers are deterministic devices, and a computer-generated random number is a contradiction in terms. As a result, computer-generated pseudorandom numbers are fraught with peril for the unwary. We summarize much that is known about the most well-known pseudorandom number generators: congruential generators. We also provide machine-independent programs to implement the generators in any language that has 32-bit signed integers-for example C, C++, and FORTRAN. Based on an extensive search, we provide parameter values better than those previously available.Programming (Mathematics) ; Computers

A Portable High-Quality Random Number Generator for Lattice Field Theory Simulations

The theory underlying a proposed random number generator for numerical simulations in elementary particle physics and statistical mechanics is discussed. The generator is based on an algorithm introduced by Marsaglia and Zaman, with an important added feature leading to demonstrably good statistical properties. It can be implemented exactly on any computer complying with the IEEE--754 standard for single precision floating point arithmetic.Comment: pages 0-19, ps-file 174404 bytes, preprint DESY 93-13

Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units

Basic uniform pseudo-random number generators are implemented on ATI Graphics Processing Units (GPU). The performance results of the realized generators (multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR, RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX generator is found to be the most appropriate for using on GPU in Monte Carlo simulations. The brief review of the pseudo-random number generators used in modern software packages for Monte Carlo simulations in high-energy physics is present.Comment: 31 pages, 9 figures, 3 table

A Comparative Study of Some Pseudorandom Number Generators

We present results of an extensive test program of a group of pseudorandom number generators which are commonly used in the applications of physics, in particular in Monte Carlo simulations. The generators include public domain programs, manufacturer installed routines and a random number sequence produced from physical noise. We start by traditional statistical tests, followed by detailed bit level and visual tests. The computational speed of various algorithms is also scrutinized. Our results allow direct comparisons between the properties of different generators, as well as an assessment of the efficiency of the various test methods. This information provides the best available criterion to choose the best possible generator for a given problem. However, in light of recent problems reported with some of these generators, we also discuss the importance of developing more refined physical tests to find possible correlations not revealed by the present test methods.Comment: University of Helsinki preprint HU-TFT-93-22 (minor changes in Tables 2 and 7, and in the text, correspondingly