### Table 1. Comparison of label relaxation techniques

2006

"... In PAGE 12: ... We assume that 50 unique color filters can be manufactured, that the sensor network is deployed from 2,400ft (neces- sary for the time-constrained relaxation) and that the time required for reporting connectivity grows linearly, with an initial reporting period of 160sec, as used in a real world tracking application [1]. The localization duration results, as presented in Table1 , are depicted in Figure 25. As shown, for all practical purposes the time required by the space constrained relaxation techniques is 0sec.... ..."

Cited by 4

### Table 1: Comparison of label relaxation techniques

2006

"... In PAGE 12: ... We assume that 50 unique color filters can be manufactured, that the sensor network is deployed from 2, 400ft (neces- sary for the time-constrained relaxation) and that the time required for reporting connectivity grows linearly, with an initial reporting period of 160sec, as used in a real world tracking application [1]. The localization duration results, as presented in Table1 , are depicted in Figure 26. As shown, for all practical purposes the time required by the space constrained relaxation techniques is 0sec.... ..."

Cited by 4

### Table 1. Computation times for solving the linear relaxation.

"... In PAGE 3: ... Computation times for solving the linear programming relaxation by different algorithms available with the CPLEX 8.0 package are given in Table1 . The problem is very degenerated and requires the use of perturbations, leading to large computation times.... In PAGE 3: ... The interior point algorithm was the best solution strategy. We also can see in Table1 that the new formulation considerably reduced the computation time of the interior points algorithm, making possible an efficient implementation of the cutting plane algorithm. Table 1.... ..."

### Table 1. Computation times for solving the linear relaxation.

"... In PAGE 3: ... Computation times for solving the linear programming relaxation by different algorithms available with the CPLEX 8.0 package are given in Table1 . The problem is very degenerated and requires the use of perturbations, leading to large computation times.... In PAGE 3: ... The interior point algorithm was the best solution strategy. We also can see in Table1 that the new formulation considerably reduced the computation time of the interior points algorithm, making possible an efficient implementation of the cutting plane algorithm. Results obtained with algorithms B amp;B-ANFP and B amp;C-ANFP are given in Ta- ble 2.... ..."

### Table 1: CPU Times and algorithm efficiency in pruning dominated paths

"... In PAGE 4: ... All the CPU times are based on a SUN SPARC classic workstation. Table1 summarizes the inputs. Each of the three HLS benchmarks namely Differential equation, Ellip- tical filter and AR filter, have been processed with different initial control steps(ics).... In PAGE 5: ... 3). Table1 also gives details of the CPU time as well as the efficiency of the algo- rithm in pruning the dominated paths. The pruning is quite effective as the ratio of total paths (column 5) to non-dominated paths (column 8) is as high as 390.... ..."

### Table (1) : Efficiency of four region-based indexing techniques

### Table 2Lagrangian and Linear Programming Relaxations Lagrangian LB Strong LP

2000

"... In PAGE 12: ... For these instances, the Lagrangian approach requires a maximal computation time of less than 9 minutes (537 seconds), which is in sharp contrast to the 72 hours needed by Brucker and Knust (2000). Table2 compares the Lagrangian approach to the corresponding linear programming relaxation (2), (3), (4), (8), and (9), again based on the ProGen instances Table 1 Comparison of Quality and Computation Times for Lower Bounds Lagrangian LB+(14) Brucker and Knust # jobs # inst. Dev.... In PAGE 13: ... It turns out that the primal simplex method solves this linear programming relaxation much faster than the barrier method does. With the barrier method, the instances with 120 jobs could not be solved in reason- able time, hence the data is missing in Table2 . More importantly, these computation times are, in fact, dras- tically higher than the computation times required to (approximately) solve the Lagrangian dual.... ..."

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### Table 2. Experimental results: comparing solvers based on different relaxations

"... In PAGE 11: ... This strategy pays off for difficult problems. Table2 displays the performances of solvers combining Box-consistency and three different relaxation techniques. There is no significant difference between the solver based on the Qmid heuristics and the solver based on the rAI heuristics.... ..."

### Table 2. Experimental results: comparing solvers based on di erent relaxations

"... In PAGE 11: ... This strategy pays o for di cult problems. Table2 displays the performances of solvers combining Box-consistency and three di erent relaxation techniques. There is no signi cant di erence between the solver based on the Qmid heuristics and the solver based on the rAI heuristics.... ..."

### Table 7. Linear programming relaxation

in Statistical Disclosure Limitation with Released Marginals and Conditionals for Contingency Tables