### TABLE I THEORETICALCOMPLEXITIESOF THE SEARCH PROCEDURES.

### Table 1. The beam search procedure

### Table 1: Binary and parallel search procedures.

1996

"... In PAGE 8: ... if (p 1) return bin search high(y; x; n); = ensure that y belongs to x before recursive call = if (y lt; x[0]) return ?1; else if (y x[n ? 1]) return n ? 1; return search high rec(y; x; n ? 1; p); = search in array excluding last element = g 9. Table1 compares sequential binary search to the parallel search procedures. The gures give running times (in SB-PRAM clock cycles) for searching (in each run for a non- trivial element) in moderate sized ordered arrays of integers, and compares parallel search to (sequential) binary search.... ..."

Cited by 4

### Table. 3. Description of the interesting iterations of the search procedure.

### Table 2: Comparison of search procedures for calibrating soil water parameters

1997

"... In PAGE 9: ... 3 Experiments and Results 3.1 Calibrating the soil water parameters Table2 summarizes the results of our attempts to calibrate the soil water parameters. In the cali- bration of each subgroup, the top row corresponds to our standard con guration, which employed the nested golden search algorithm in the two dimensional cases and simulated annealing in the four dimensional cases, and an objective function JW .... ..."

### Table V. VBSS and VBSS-HC as compared to discrepancy search procedures.

2005

Cited by 4

### Table 30.3 Results of Local Search procedures for solving the PPP

1998

Cited by 2

### Table 2: Results for a pure random search procedure

"... In PAGE 12: ...niform distribution. Because of the presence of the scaling factors r1,...,r5 in scoring formula [4] the natural choice is to draw the factors from the interval (0,1). Table2 shows the average volume utilisations obtained if such an approach is applied to the 700 test problems used as benchmarks by Ratcliff and Bischoff (1998) and Davies (2000). The three rows of the table refer to t=100, 500 and 1000 sets of random weights, respectively, and the columns represent the average volume utilisation obtained for the 100 problems in each class.... ..."

### Table 3: Comparison of search procedures and objective functions for calibrating the transpiration parameters

1997

"... In PAGE 11: ...his amount. Random settings of the parameters generally result in errors in the millions. 3.2 Calibrating the transpiration parameters Table3 summarizes our attempts to calibrate the transpiration parameters. Each row reports the best of 5 runs except in the case of simulated annealing, where the result of only one run is shown.... ..."