Optimization via adaptive sampling and regenerative simulation

We investigate a new approach for simulation-based optimization that draws on two recent stochastic optimization methods: an adaptive sampling approach called the nested partitions method and ordinal optimization. An ordinal comparison perspective is used to show that the nested partitions method converges globally under weak conditions. Furthermore, we use those results to determine a lower bound for the required sampling effort in each iteration, and show that global convergence requires relatively little simulation effort in each iteration.