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Table 1: Solving generic MILPs with SYMPHONY: Default settings and no a priori upper bound (summary)

in Parallel Branch and Cut
by T. K. Ralphs 2006
"... In PAGE 29: ... Strong branching was used to make branching decisions and the search strategy was a hybrid diving strategy in which one of the children of a given node was retained as long as its bound was within a given percentage of the best available. Table1 in the Appendix shows the results of the first set of experiments, in which SYMPHONY was run with default settings and no a priori upper bound. Detailed results... In PAGE 30: ... This eliminates redundant work, but the results still exhibit a very slight increase in the number of search nodes as the number of NP/CG modules is increased. This hurts the parallel efficiency, but the provision of an a priori upper bound still improves solution times across the board in comparison to those in Table1 . Note that the total amount of overhead, especially the ramp down and the idle time spent waiting for new node descriptions to be sent from the TM module are very significantly reduced for these runs over the runs with... In PAGE 43: ...2071 0.2977 Table1 0: Solving VRP instances with SYMPHONY: Default settings with heuristic upper bounds and global cut pool (summary) 4... In PAGE 44: ...5373 51.0083 Table1 1: Solving SPP instances with SYMPHONY: Default settings and no a priori upper bound (summary) 4... In PAGE 45: ...5373 51.0083 Table1 2: Solving SPP instances with SYMPHONY: Default settings and no a priori upper bound (32 NPs) 4... ..."

Table 9: Solving VRP instances with SYMPHONY: Default settings and no a priori upper bound (summary)

in Parallel Branch and Cut
by T. K. Ralphs 2006
"... In PAGE 31: ...eneric MILP instances in Section 5.1, but the levels of overhead are much smaller. As in the case of the generic MILP instances, because the baseline levels of overhead are small to begin with, the efficiencies are similar in each case. Table9 shows results of a slightly smaller set of instances with no a priori upper bounds... ..."

Table 2: Solving generic MILPs with SYMPHONY: Default settings and no a priori upper bound (32 NPs)

in Parallel Branch and Cut
by T. K. Ralphs 2006
"... In PAGE 30: ...summary results only for all other runs. Table2 shows the detailed results for the run with 32 NP/CG modules. For most of these instances, the time needed to process a search tree node is small in comparison with the overall running time, which tends to lead to good scalability.... ..."

Table 11: Solving SPP instances with SYMPHONY: Default settings and no a priori upper bound (summary)

in Parallel Branch and Cut
by T. K. Ralphs 2006
"... In PAGE 32: ... The small cardinality of this test set should serve to emphasize that these instances are the exception to the rule. As in the previous section, Table11 shows summary results obtained when solving these five instances with different numbers of NP/CG modules. Detailed results are shown only for the case with 1 NP/CG module.... ..."

Table 12: Solving SPP instances with SYMPHONY: Default settings and no a priori upper bound (32 NPs)

in Parallel Branch and Cut
by T. K. Ralphs 2006
"... In PAGE 32: ... Detailed results are shown only for the case with 1 NP/CG module. Table12 shows results for the runs with 32 NP/CG modules. As could be expected, the results show that the node processing times are an order of magnitude larger than for the instances in Sections 5.... ..."

Table 1: A priori bounds on the condition number to ensure faithful rounding in IEEE- 754 double precision for polynomials of degree 10 to 500 n 10 100 200 300 400 500

in unknown title
by unknown authors 2008
"... In PAGE 9: ... From Lemma 6 we deduce that r is a faithful rounding of p(x). Numerical values of condition numbers for a faithful polynomial evaluation in IEEE- 754 double precision are presented in Table1 for degrees varying from 10 to 500.... ..."

Table 4: Computational Cost vs. Accuracy. bounds. There is ongoing work to investigate the possibility of a priori determining d and Ci to obtain speci ed error bounds.

in Computing Performance Bounds for Fork-Join Queueing Models
by John C. S. Lui, Richard R. Muntz, Don Towsley

Table 4: Computational Cost vs. Accuracy. bounds. There is ongoing work to investigate the possibility of a priori determining d and Ci to obtain speci ed error bounds.

in Computing Performance Bounds for Fork-Join Queueing Models
by John C. S. Lui, Richard R. Muntz, Don Towsley

Table 1: Recognition accuracy (%) on factory noise for combination spectral subtraction/missing data with bounds, with two noise estimation techniques (section 3) and for the a- priori information.

in Missing Data Theory, Spectral Subtraction And Signal-To-Noise Estimation For Robust Asr: An Integrated Study
by A. Vizinho, P. Green, M. Cooke, L. Josifovski 1999
"... In PAGE 3: ... Hirsch apos;s weighted average technique produces a small advantage at low SNRs. 3) Combination results Table1 shows the recognition results when spectral subtraction and missing data (i.e.... ..."
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Table 2: The a-priori probabilities for the node S of the network in Figure 1

in Bayesian models of the PM10 Atmospheric Urban Pollution
by M. Cossentino, F.M. Raimondi, M. C. Vitale
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