RT Monograph
SR 00
A1 Futschik, A.
A1 Pflug, G.C.
T1 Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization
YR 1996
FD 1996-03
SP 18
AB Approximate solutions for discrete stochastic optimization problems  are often obtained via simulation. It is reasonable to complement  these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws  should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be  asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution  and to minimize the expected size of the confidence sets.
PB WP-96-023
PP IIASA, Laxenburg, Austria
AV Published
LK https://pure.iiasa.ac.at/id/eprint/5002/