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Global optimization problems sometimes attain their extrema on infinite subsets of the search space, forcing mathematically rigorous programs to require large amounts of data to describe these sets. This makes these programs natural candidates for both vectorization methods and parallel computing. Here, we give a brief overview of parallel computing and vectorization methods, exploit their availability by constructing a fully distributed implementation of a mathematically rigorous Vector Parallel Branch and Bound Algorithm using MATLAB’s SPMD architecture and interval arithmetic, and analyze the performance of the algorithm across different methods of inter-processor communication.
| Advisor: | Kearfott, R. Baker |
| Commitee: | Browne, Cameron, Sutton, Karyn |
| School: | University of Louisiana at Lafayette |
| Department: | Sciences |
| School Location: | United States -- Louisiana |
| Source: | DAI-B 78/12(E), Dissertation Abstracts International |
| Source Type: | DISSERTATION |
| Subjects: | Mathematics, Operations research, Computer science |
| Keywords: | Branch and Bound, Global optimization, Interval arithmetic, Parallel processing, Vector operations |
| Publication Number: | 10242153 |
| ISBN: | 978-0-355-11282-5 |
