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Table 1: Best weights and the value of optimization criterion corresponding to the global
"... In PAGE 24: ... For convergence, about 40 iterations were needed for each trial. In Table1 the best set of weights, obtained by running the optimization with seven retrievals over the entire database is shown. It corresponds to the highest obtained value (J =3:4207) of the objective function (15).... ..."
Table 1: Best weights and the value of optimization criterion corresponding to the global maximum.
"... In PAGE 23: ... For convergence, about 40 iterations were needed for each trial. In Table1 the best set of weights, obtained by running the optimization with seven retrievals over the entire database is shown. It corresponds to the highest obtained value (J = 3:4207) of the objective function (15).... ..."
Table 1. Best weights and the value of optimization criterion corresponding to the global maximum
"... In PAGE 8: ...f w1 and w2 generated the 16 initial simplexes (Fig. 10). For convergence, about 40 iterations were needed for each trial. In Table1 , the best set of weights, obtained by run- ning the optimization with seven retrievals over the entire database is shown. It corresponds to the highest obtained value (J =3:4207) of the objective function (Eq.... ..."
TABLE 2 Correlations Between Performance and Deviations From the Optimal Reward Criterion and the Optimal Accuracy Criterion
Table 1: Optimal Values for Fisher and Optimal Separation criterion
2005
"... In PAGE 4: ... The training data was used for checking the Fisher (4) as well as the Optimal Separation (12) criterion where optimization of (4) is done under the CDA (5) as well as under the FSDA (6) constraint. Table1 shows the good performance of the FSDA method for the Fisher criterion and also the bad performance of the Optimal Separation Projection method in terms of the Fisher criterion but the good performance especially for r = 2 in terms of the OS criterion. But this is expected as the OSP method aims at minimizing this criterion.... ..."
Table 5: Ordinal Optimization applied to original criterion
Table 6: Ordinal Optimization applied to surrogate criterion
Table 6: Optimization of the original problem formulation with TAopt for a varying number of jobs using the makespan as the optimization criterion.
2005
"... In PAGE 14: ... reachability analysis (of the TA model), and to compute lower bounds of the cost-to-go using the LP model. Table6 shows results obtained with TAopt for the original version of the case study with a reduced number of jobs. We considered the minimization of the makespan for a varying number of jobs, where the rst ve problems do not include hard deadlines for the jobs, while the last two do.... ..."
Table 1: Ordinal Optimization applied to criterion PL(B10)
"... In PAGE 11: ...From these results, we conclude that the simulation required to meet the relaxed goal is at most one-fifteenth that of the original.8 If we are further willing to relax our goals to the isolation of a set containing some good enough (for this example, top-10) designs with high probability, then an even shorter simulation is required ( Table1 ). This idea is central to Ordinal Optimization.... ..."
Table 2: Ordinal Optimization applied to criterion PL(B3)
Results 1 - 10
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