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The importance of the sensitivity of an algorithm to the output size of a problem is well-known especially if the upper bound on the output size is known to be not too large.
These problems are more challenging than static problems in real-world applications because the latter are usually dynamic, with the environment constantly subjected to change or the size of a problem increasing sporadically.
Sometimes the sheer size of a problem may engender a sense of hopelessness.
Task parallelism does not usually scale with the size of a problem.
In this case, the size of a problem instance is Θ nB).
For example, including only the largest estimates of the size of a problem, such as the proportion of children who do not complete their vaccination schedule, will result in a poor understanding of a problem such as incomplete vaccination.
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However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time.
In practice the size of a design problem can be very large, the limit state functions are usually implicit in terms of the random variables.
Modularity/decomposability -The complexity and size of a design problem inherently forces designers to compartmentalize it into "modules".
Using an exact algorithm to find a global optimal solution is very hard considering the size of a typical problem where there can be up to a hundred users and hundreds of sub-channels.
Others argue that it lacks a conceptual basis for priority setting of health interventions, as the size of a disease problem has no relation to the potential for effective reduction [ 33].
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