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Table 2. Heuristic performance for large problems

in Assembly Scheduling in a Make-to-order Environment with Shop Floor Constraints
by Rainer Kolisch, Armin Rinne
"... In PAGE 6: ... But at the same time the probabilistic methods re- quire much more computation time. Table2 reports the results of the problems... ..."

TABLE 2. Pairwise comparisons on very large problems

in Interior-point methods for nonconvex nonlinear programming: Orderings and higher-order methods
by Hande Y. Benson, David F. Shanno, Robert, J. Vanderbei 2000
Cited by 48

Table 12: GENCAN with very large problems.

in Large-scale active-set box-constrained optimization method with spectral projected gradients
by Ernesto G. Birgin, et al. 2001
"... In PAGE 29: ... Both methods found a global solution in all the cases. In Table12 we also report the number of free variables at the solution so far found by GENCAN. GENCAN Problem(n) using Fortran subroutines using SIF LANCELOT IT FE GE CG Time IT FE GE CG Time IT FE GE CG Time 1(400) 2 3 3 2 0.... ..."

Table 12: GENCAN with very large problems.

in Large-scale active-set box-constrained optimization method with spectral projected gradients
by Ernesto G. Birgin, et al. 2001
"... In PAGE 29: ... Both methods found a global solution in all the cases. In Table12 we also report the number of free variables at the solution so far found by GENCAN. GENCAN Problem(n) using Fortran subroutines using SIF LANCELOT IT FE GE CG Time IT FE GE CG Time IT FE GE CG Time 1(400) 2 3 3 2 0.... ..."

Table 5.4: Results for Large Problems

in A Set Covering Approach To Infeasibility Analysis Of Linear Programming Problems And Related Issues
by Mark Richard Parker 1995
Cited by 8

Table 7: RQP: Large problems m = 15

in An algorithm for the nonlinear programming problem of the railway Yield Management
by Di Pillo, G. Di, Pillo S. Lucidi, L. Palagi

Table 8: LANC: Large problems m = 15.

in An algorithm for the nonlinear programming problem of the railway Yield Management
by Di Pillo, G. Di, Pillo S. Lucidi, L. Palagi

Table 9: PLA: Large problems m = 15.

in An algorithm for the nonlinear programming problem of the railway Yield Management
by Di Pillo, G. Di, Pillo S. Lucidi, L. Palagi

Table 1. Comparison of FDA and BKDA on large problems.

in unknown title
by unknown authors

Table 1. Comparison of FDA and BKDA on large problems.

in Estimation of Distribution Algorithms and Minimum Relative Entropy
by Heinz Mühlenbein, Robin Höns 2005
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