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New, efficient, computational algorithms are proposed, allowing a remarkable reduction of computing time and a good reliability in evaluating special quantities.
The results demonstrate that the reconstruction strategy has comparable performance with a significant reduction of computing time.
The reduction of computing time is particularly dramatic and estimates based on operation counts are partially verified by actual computational experience.
Further, the clear reduction of computing time and memory requirements of inexact solvers compared to sparse direct ones makes possible to scale far beyond state-of-the-art BDDC implementations.
The reduction of computing time scaled linearly with the number of allocated cores.
The binary images also enable a reduction of computing time because the system uses 2-bit rather than 8-bit images.
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Meanwhile, a comparison between the pattern search method and genetic algorithm reflects that the former gives better correlation performance and more significant reduction of computing times than the latter.
Comparative computational exercises are presented in order to point out the consequent significant reduction of computing times in test simulations and, hence, in the minimization of the discrepancy function by the Trust Region Algorithm, namely by a traditional first-order mathematical programming method.
This is a model reduction approach called modal reduction which, if efficiently implemented, can save a huge amount of computing time and storage, despite the fact that it is partially heuristic.
Secondly, Boinc would allow fuller use of computing time.
Moreover, a large computing center often requires buying a predefined offer (i.e., a minimum fixed amount of computing time).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com