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Housing may seem like a commodity that has a somewhat objective value; for example, a buyer could rely on a yardstick like the asking price per square foot of an apartment, and compare it with the sale price per square foot of similar properties in the same neighborhood.
The optimal objective value for the problem (Snyder and Daskin 2006).
Table 4 compares the objective value for k={0.10n,0.20n} of the vertices in each network, respectively.
For each e, let ( {rm PR}_{e}^ ) be the optimal objective value for (( p_{e}^{prime } )); we assume ( {rm PR}_{e}^ ) > 0 for all e.
Figure 8 shows the evolution of the objective value for an algorithm test of the NMPC over a simulated period of 100 days.
We cannot take the worst case under all scenarios for evaluating solutions because in multiobjective optimization problems, we obtain that the objective value for each scenario is a vector.
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Table 4 lists objective values for some Pareto-front solutions.
TSPLIB provides optimal objective values for each of the problems.
The comparison of objective values for the particles of PSO is made in the same manner.
Moreover, we improve the best-known objective values for some large Australia Post and PlanetLab instances.
In Section 6 we will see how even this supposition may be relaxed in scientific contexts where completely objective values for likelihoods are not realistically available.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com