Optimal multipliers for pseudo-random number generation by the linear congruential method

1983 ◽  
Vol 23 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Itshak Borosh ◽  
Harald Niederreiter
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Number Generation ◽  
Pseudo Random Number ◽  
Congruential Method ◽  
Linear Congruential

scholarly journals Pseudo-random number generator based on linear congruence and delayed Fibonacci method

2021 ◽  
Author(s):  
Radosław Cybulski
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Test Machine ◽  
Number Generator ◽  
Chi Square ◽  
Pseudo Random Number ◽  
Congruential Method ◽  
Linear Congruential ◽  
Pseudo Random Number Generator

Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.

Pseudo-random number generation using a 3-state cellular automaton

2017 ◽  
Vol 28 (06) ◽  
pp. 1750078 ◽  
Author(s):  
Kamalika Bhattacharjee ◽  
Dipanjyoti Paul ◽  
Sukanta Das
Keyword(s):  
Cellular Automaton ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Number Generator ◽  
Number Generation ◽  
Pseudo Random Number ◽  
Empirical Tests ◽  
Pseudo Random Number Generator

This paper investigates the potentiality of pseudo-random number generation of a 3-neighborhood 3-state cellular automaton (CA) under periodic boundary condition. Theoretical and empirical tests are performed on the numbers, generated by the CA, to observe the quality of it as pseudo-random number generator (PRNG). We analyze the strength and weakness of the proposed PRNG and conclude that the selected CA is a good random number generator.

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