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This explains the large execution time.
Compared to dynamic schedules, which involve relatively large execution time overhead and have poor predictability, quasi-static schedules can lead to significantly more efficient and reliable implementations.
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When the problem was first formulated in 2001, Integer Linear Programming, an exact method, was found to be impractical because of large execution times.
Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times.
The HT technique has a higher error and a larger execution time than the proposed approach.
LDR, EHS, AGCWD, RSWHE, and HMF also take comparatively larger execution time.
Overall, we observe that networks with a larger number of nodes incur a larger execution time; for networks with comparable number of nodes, the execution time for the centrality metrics increases with increase in the edge-node ratio (ratio of the number of nodes to the number of edges), especially to compute the time-consuming centrality metrics, such as the BWC and EVC.
Although initially the difference is large, the execution time for the Opt algorithm is not that long.
Using a kernel with a larger variance, execution time can be reduced, but the accuracy might decrease slightly.
As the number of affected plots can be large, the algorithm execution time is potentially high.
Most experiments were performed 10 times each and they noticed large variations in execution time for the different runs.
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