doi: 10.17706/ijapm.2019.9.4.173-181
Deterministic-Embedded Monte Carlo Approach to Find out an Objective Item in a Large Number of Data Sets
- 1 Department of Mechatronic Engineering, Foshan University, Foshan, China.
- 2 Guangdong Engineering Center of Information Security for Intelligent Manufacturing System, Foshan, China
Abstract
The paper investigates an approach to find out an objective integer in a large integer interval. It first puts forward an approach to subdivide a large integer interval into small ones that are available for the Monte Carlo randomized search algorithm, then selects a small interval by the Monte Carlo algorithm and applies a deterministic search algorithm on the selected one. In order to make the search in an expected computing time, the paper proposes certain regulations to set an initial length for the small interval and to update it in accordance with the expectation of the time complexity. Mathematical foundations for setting-up the initial value and updating it to an acceptable value are presented and proved in detail and a parallel computing strategy is introduced to realized it. Except for the availability in integer factorization, the approach is also applicable in big data searches.
Keywords
- Subdivision
- randomized algorithm
- parallel computing
- big data
- integer factorization
How to Cite
Xingbo Wang and Jianxiang Guo, "Deterministic-Embedded Monte Carlo Approach to Find out an Objective Item in a Large Number of Data Sets," International Journal of Applied Physics and Mathematics, vol. 9, no. 4, pp. 173-181, 2019. https://doi.org/ 10.17706/ijapm.2019.9.4.173-181
Copyright & License
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).