Improvements Of Interval Algorithm For Random Numbers And Its Application In Resource Allocation

Random number generation is the core technology of information security and computer simulation.How to generate high-quality random numbers is the core problem in the field of random number research.The random number generation based on interval algorithm is characterized by simplicity and high efficiency.The efficiency of random number generation is the highest among all known methods,and it is one of the focuses of research in recent years.In this thesis,a binary exact partition interval algorithm is proposed based on the existing interval algorithm.Major improvements include the output of the implicit method and output interval of 3 subinterval renormalization method,completely eliminate the rounding error algorithm output interval segmentation problem,but also greatly reduce the rounding error of input interval segmentation problem,thus solved the finite precision arithmetic problems led to the decrease of the quality of the output random interval algorithm,realized under the condition of limited precision integer arithmetic to any known probability distribution of the input data is converted into a specified in the form of the exact or near exact arbitrary probability distribution of random numbers.This thesis also designs an algorithm for generating random numbers using publicly visible information sources.This algorithm selects the stock price data as the information source,uses the ARIMA model and the interval algorithm to process the information source data,and finally outputs the binary random sequence.NIST SP800-22 random number detection set test results show that the algorithm works well.