Adaptive Design in Discrete Stochastic Optimization
Abstract
We present adaptive assignment rules for the design of the necessary simulations when solving discrete stochastic optimization problems. The rules are constructed in such a way, that the expected size of confidence sets for the optimizer is as small as possible.