A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Thoughts on pseudorandom number generators
Journal of Computational and Applied Mathematics - Random numbers and simulation
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Systematic searches for good multiple recursive random number generators
Computers and Operations Research
Chinese remainder theorem: applications in computing, coding, cryptography
Chinese remainder theorem: applications in computing, coding, cryptography
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Uniform Random Number Generators
Journal of the ACM (JACM)
Generalized Feedback Shift Register Pseudorandom Number Algorithm
Journal of the ACM (JACM)
The k-distribution of generalized feedback shift register pseudorandom numbers
Communications of the ACM
| Hi-index | 7.29 |
The combined random number (RN) generator has been considered by many scholars as a good RN generator. One promising type of combined RN generator, recommended by L'Ecuyer (Oper. Res. 44 (1996) 816; 47 (1999) 159), is the combined multiple recursive generator (MRG). This paper analyzes the combined MRG via the Chinese remainder theorem. A new combined generator based on the generalized Chinese remainder theorem and on the Ore algorithm (Amer. Math. Monthly 59 (1952) 365) is presented. The proposed combined generator improves the combined MRG in terms of both the suitability for various types of RN generators and the restriction on the moduli of the individual generators. Therefore, the proposed combined generator is an ideal RN generator and is most recommended.