Obviously, the overall complexity of the reception process depends on the number of iterations conducted.
A 64-core parallel computing platform was used for bootstrap resampling (to reduce the computational costs, each of the 64 CPUs ran 6 iterations; 384 iterations were conducted).
More specifically, we consider a broad class of optimization algorithms that sample a set of feasible system designs in each iteration, conduct simulations at the sampled designs in an effort to estimate the performance of these designs, and then use the simulation results to decide on what designs should be sampled in the next iteration and on the current estimate of the optimal system design.
Therefore, each iteration is conducted in O(n) time, and the total running time of all n iterations of the algorithm is O(n).
The procedure was therefore repeated and an assessment of factor loadings from this second iteration was conducted as before and items that did not load were removed.
For each iteration, we conducted an error analysis; we calculated precision and an estimated recall using 100 random candidate relations with a probability greater than 90% and 100 random candidate relations with a probability less than 90%.
A total of 100 independent runs with 10,000-50,000 burn-in iterandons and 100,000 post burn-in iterations were conducted at each value of K using default settings.
Simulation for 58 iterations were conducted for different willingness-to-pay threshold ratio and for each iteration, a value of perfect information for each willingness-to-pay threshold was calculated with the use of the effective population [ 12].
Due to logistical limitations, the first, second and third iterations were conducted at the first testing site with four English-speaking adolescents per round and the fourth iteration of testing occurred at the second testing site with 6 French-speaking adolescents.
Therefore, only 1000 iterations were conducted in the simulations.
