Iterative ranking-and-selection for large-scale optimization
Sigurdur Oli Olafsson · 1999
We develop a novel algorithm for simulation based optimization where the number of alternatives is finite but very large. Our approach draws on recent work in adaptive random search and from ranking-and-selection. In particular, it combines the nested partitions method for global optimization and Y. Rinott's (1978) two-stage ranking-and-selection procedure. We prove asymptotic convergence of the new algorithm under fairly mild conditions.