Sentence examples for solution lengths from inspiring English sources

Increasing the number of iterations to 10,000 30,000 makes it possible for the CS algorithm to obtain better solution lengths as compared to only 500 iterations for both the large problem instances P-n40-k5 and P-n101-k4 P-n101-k4 P-n101-k4

In addition, the distribution of solution lengths in one of the domains does not follow that expected for a process driven by intermittent silence.

To address solution lengths, we considered the lengths of "solutions:" sequences of interventions required to complete the solution (or non-solution) of an individual virtual patient in the mechanical ventilation data.

The number of interventions required to complete each virtual patient was counted, yielding a distribution of solution lengths that were then rank ordered by appearance frequency, and the effective frequency of a "space" was determined within the dataset.

Moreover, during the simulated practice of mechanical ventilation the "solution lengths" also followed a power law, and the observed trajectories of both intervention lengths and the solutions into which they are concatenated diverged sharply from those predicted by intermittent silence and random text models.

As for P-n101-k5 P-n101-k5 P-n101-k5iterations, the besameolution length obtained was 1044.22480, with a relative error of 0.5088 when compared to the best known solution.

At 30,000 iterations, the best solution length obtained for P-n40-k5 was 581.15897, with a relative error of 0.2689 when compared to the best known solution.

A normalized feature such as percentage of hints would allow us to better compare the level of scaffolding in terms of hints across problems of varying complexity or solution length.

Here we introduce an approach to learning more powerful nogoods, that are sound regardless of solution length, i.e., that identify dead-end states for which no solution exists.

If this latter explanation holds, the multi-scale behaviors ("word length" and "solution length") observed may share causes similar to the evolutionary pattern described by Nowak.

Almost all existing approaches learn nogoods relative to a fixed solution-length bound, in which case planning/reachability reduces to a constraint satisfaction problem.