### Table 3: Throughput comparison Greedy Near-Optimal

2006

"... In PAGE 10: ... All results are the average over 20 random topologies generated by the setdest tool [21]. Table3 shows the maximum, minimum and average end- to-end throughput over 20 random topologies for both greedy and near-optimal ad-hoc relay. Greedy ad-hoc relay protocol achieves throughput gains of 572 897% with an average throughput gain of 785%.... ..."

Cited by 3

### Table 13: Improving Near-Optimal Tours with DPC

in Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study

### Table 13: Improving Near-Optimal Tours with DPC

in Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study

### Table 1: Near-optimal feature transform functions.

"... In PAGE 6: ... This leads us with the following scoring function: score(d) = n X i=1 wiTi(Fi(d)) For each feature, we chose a transform function that we em- pirically determined to be well-suited. Table1 shows the chosen transform functions. We tuned the scalar weights by selecting 5000 queries at random from the test set, us- ing an iterative refinement process to determine the weight that maximized the given performance measure, fixed the weight, and used the remaining 23,043 queries to assess the performance of the scoring function.... ..."

### Table 1: Near-optimal feature transform functions.

"... In PAGE 6: ... We chose transform functions that we empirically determined to be well-suited. Table1 shows the chosen transform functions. This type of linear combination is appropriate if we as- sume features to be independent with respect to relevance and an exponential model for link features, as discussed in [8].... ..."

### Table 2: Near-optimal configurations estimated through our fast methodology compared to optimal configurations found with the exhaustive search. (Simulations have been performed on different workstations working in parallel)

"... In PAGE 3: ... Ta- ble 2 compares the final configurations estimated with our method- ology with the optimal configurations found with the exhaustive search. Table2 shows also the percentage errors between the op- timal BXBW and the near-optimal BXBW values. For all benchmarks but one, the optimal configuration has been found without explor- ing the whole design space.... In PAGE 3: ...54% error with re- spect to the optimal BXBW value (CM CR D3D4D8 and CM CQ D3D4D8 differ from CR D3D4D8 and CQ D3D4D8 ). Table2 depicts also the exhaustive search time as well as the overall speedup obtained by our methodology. Note how simula- tion times can be reduced approximately by a factor of six.... ..."

### Table 5. Number of Solutions within x percent near optimal for 1000 Tasks Sets.

"... In PAGE 6: ... For each experiment, a workload of 10 tasks has been generated with an overload (120% utilization for each task set). Results shown in Table5 indicate the number of solutions within a certain per- cent close to optimal. For the two optimality criteria a near optimal solution (more than 91%) is obtained using AP(2).... In PAGE 7: ... Figure 4 shows that the complexity of the algorithm is relatively low even with a high degree of quality ( = 0:02). From the results shown in Table5 it can be concluded that for values of k 2, 92.5% and 100% of the solutions are 95%-close to optimal when the criterion is to maximize criti- cality and utilization, respectively.... ..."

### Table 2: A greedy heuristic to nd cost of near-optimal key sequence

### Table 45 clearly demonstrates that the column generation heuristic is able to solve small problems to near-optimality; the reported average optimality gap is with respect to the known optimal solutions to these problems found using the smart enumeration method. Further, Table 46 demonstrates that the heuristic solution approach is much faster than

in Seaports

2006

"... In PAGE 88: ....8.3 Performance of Heuristics The performance of the column generation heuristics is now evaluated by comparing the solutions obtained from the heuristic with the optimal solutions obtained from the smart enumeration method. Table45 summarizes the performance of LSP pricing heuristic on UDTP problems. Table 46 compares the computational time required by the two methods for the same problem sizes; note that computation times are decomposed into label gener- ation time and branch-and-bound time.... In PAGE 88: ... All values represent averages over 10 instances. Table45 : Performance of Root Column Generation Heuristic Using LSP for UDTP: Solu- tion Quality Problem No. of Inst- Avg Avg Size ances solved optimality Requests optimally gap per vehicle 11 X 11 5 0.... ..."