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- W1656046927 abstract "This dissertation solves a fundamental problem of isolating the real roots of nonlinear systems of equations by Monte-Carlo that were published by Bush Jones. This algorithm requires only function values and can be applied readily to complicated systems of transcendental functions. The implementation of this sequential algorithm provides scientists with the means to utilize function analysis in mathematics or other fields of science. The algorithm, however, is so computationally intensive that the system is limited to a very small set of variables, and this will make it unfeasible for large systems of equations. Also a computational technique was needed for investigating a metrology of preventing the algorithm structure from converging to the same root along different paths of computation. The research provides techniques for improving the efficiency and correctness of the algorithm. The sequential algorithm for this technique was corrected and a parallel algorithm is presented. This parallel method has been formally analyzed and is compared with other known methods of root isolation. The effectiveness, efficiency, enhanced overall performance of the parallel processing of the program in comparison to sequential processing is discussed. The message passing model was used for this parallel processing, and it is presented and implemented on Intel/860 MIMD architecture. The parallel processing proposed in this research has been implemented in an ongoing high energy physics experiment: this algorithm has been used to track neutrinoes in a super K detector. This experiment is located in Japan, and data can be processed on-line or off-line locally or remotely." @default.
- W1656046927 created "2016-06-24" @default.
- W1656046927 creator A5068975974 @default.
- W1656046927 date "2022-06-14" @default.
- W1656046927 modified "2023-10-07" @default.
- W1656046927 title "Parallelization and Implementation of Approximate Root Isolation for Nonlinear System by Monte Carlo." @default.
- W1656046927 doi "https://doi.org/10.31390/gradschool_disstheses.6682" @default.
- W1656046927 hasPublicationYear "2022" @default.
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